Building an Open-Source DNS Filtering SaaS: A Technical Blueprint

I. Introduction

Purpose

This report provides a comprehensive technical blueprint for developing an open-source Software-as-a-Service (SaaS) platform with functionality analogous to NextDNS. The primary objective is to identify, evaluate, and propose viable technology stacks composed predominantly of open-source software components, deployed on suitable cloud infrastructure. The focus is on replicating the core DNS filtering, security, privacy, and user control features offered by services like NextDNS, while adhering to open-source principles.

Context

The digital landscape is increasingly characterized by concerns over online privacy, security threats, and intrusive advertising. Services like NextDNS have emerged to address these concerns by offering sophisticated DNS-level filtering, providing users with greater control over their internet experience across all devices and networks.¹ This has generated significant interest in privacy-enhancing technologies. An open-source alternative to such services holds considerable appeal, offering benefits such as transparency in operation, the potential for community-driven development and auditing, and greater user control over the platform itself. Building such a service, however, requires careful consideration of complex technical challenges, including distributed systems design, real-time data processing, and robust security implementations.

Scope

This report delves into the technical requirements for building a NextDNS-like open-source SaaS. The analysis encompasses:

1. A detailed examination of NextDNS's core features, architecture, and underlying technologies, particularly its global Anycast network infrastructure.

2. Identification of the essential technical components required for such a service.

3. Evaluation and comparison of relevant open-source software, including DNS server engines, filtering tools and techniques, web application frameworks, scalable databases, and user authentication systems.

4. Assessment of cloud hosting providers and infrastructure strategies, with a specific focus on implementing low-latency Anycast networking.

5. Synthesis of these findings into concrete, actionable technology stack proposals, outlining their respective strengths and weaknesses.

Target Audience

The intended audience for this report consists of technically proficient individuals and teams, such as Software Architects, Senior Developers, and DevOps Engineers, who possess the capability and intent to design and implement a complex, distributed, open-source SaaS platform. The report assumes a high level of technical understanding and provides in-depth analysis and objective comparisons to support architectural decision-making.

II. Understanding the Target: NextDNS Core Features and Architecture

Overview

NextDNS positions itself as a modern DNS service designed to enhance security, privacy, and control over internet connections.² Its core value proposition lies in providing these protections at the DNS level, making them effective across all user devices (computers, smartphones, IoT devices) and network environments (home, cellular, public Wi-Fi) without requiring client-side software installation for basic functionality.¹ The service emphasizes ease of setup, often taking only a few seconds, and native support across major platforms.¹

Key Feature Areas

NextDNS offers a multifaceted feature set, broadly categorized as follows:

• Security: The platform aims to protect users from a wide array of online threats, including malware, phishing attacks, cryptojacking, DNS rebinding attacks, IDN homograph attacks, typosquatting domains, and domains generated by algorithms (DGAs).¹ It leverages multiple real-time threat intelligence feeds, citing Google Safe Browsing and feeds covering Newly Registered Domains (NRDs) and parked domains.¹ A key differentiator claimed by NextDNS is its ability to analyze DNS queries and responses "on-the-fly (in a matter of nanoseconds)" to detect and block malicious behavior, potentially identifying threats associated with newly registered domains faster than traditional security solutions.¹ This functionality positions it against enterprise security solutions like Cisco Umbrella, Fortinet, and Heimdal EDR, which also offer DNS-based threat prevention.³

• Privacy: A central feature is the blocking of advertisements and trackers within websites and applications.¹ NextDNS utilizes popular, real-time updated blocklists containing millions of domains.¹ It also highlights "Native Tracking Protection" designed to block OS-level trackers, and the capability to detect third-party trackers disguising themselves as first-party domains to bypass browser protections like ITP.¹ The use of encrypted DNS protocols (DoH/DoT) further enhances privacy by shielding DNS queries from eavesdropping.¹

• Parental Control: The service provides tools for managing children's online access. This includes blocking websites based on categories (pornography, violence, piracy), enforcing SafeSearch on search engines (including image/video results), enforcing YouTube's Restricted Mode, blocking specific websites, apps, or games (e.g., Facebook, TikTok, Fortnite), and implementing "Recreation Time" schedules to limit access to certain services during specific hours.¹ These features compete with dedicated parental control solutions and offerings from providers like Cisco Umbrella.⁵

• Analytics & Logs: Users are provided with detailed analytics and real-time logs to monitor network activity and assess the effectiveness of configured policies.¹ Log retention periods are configurable (from one hour up to two years), and logging can be disabled entirely for a "no-logs" experience.¹ Crucially for compliance and user preference, NextDNS offers data residency options, allowing users to choose log storage locations in the United States, European Union, United Kingdom, or Switzerland.¹ "Tracker Insights" provide visibility into which entities are tracking user activity.¹

• Configuration & Customization: NextDNS allows users to create multiple distinct configurations within a single account, each with its own settings.¹ Users can define custom allowlists and denylists for specific domains, customize the block page displayed to users, and implement DNS rewrites to override responses for specific domains.¹ The service automatically performs DNSSEC validation to ensure the authenticity of DNS answers and supports the experimental Handshake peer-to-peer root naming system.¹ While integrations with platforms like Google Analytics, AdMob, Chartboost, and Google Ads are listed ⁶, their exact role within a privacy-focused DNS service is unclear from the snippet; they might relate to NextDNS's own business analytics or specific optional features rather than core filtering functionality.

Architecture & Infrastructure

The effectiveness and performance of NextDNS are heavily reliant on its underlying infrastructure:

• Global Anycast Network: NextDNS operates a large, globally distributed network of DNS servers, with 132 locations mentioned.¹ This network utilizes Anycast routing, where the same IP address is announced from multiple locations.¹ When a user sends a DNS query, Anycast directs it to the geographically or topologically nearest server instance.² NextDNS claims its servers are embedded within carrier networks in major metropolitan areas, minimizing network hops and delivering "unbeatably low latency at the edge".¹ This infrastructure is fundamental to providing a fast and responsive user experience worldwide.

• Encrypted DNS: The service prominently features support for modern encrypted DNS protocols, specifically DNS-over-HTTPS (DoH) and DNS-over-TLS (DoT).¹ These protocols encrypt the DNS query traffic between the user's device and the NextDNS server, preventing interception and modification by third parties like ISPs.²

• Scalability: The infrastructure is designed to handle massive query volumes, with NextDNS reporting processing over 100 billion queries per month and blocking 15 billion of those.¹ This scale necessitates a highly efficient and resilient architecture.

Architectural Considerations for Replication

Replicating the full feature set and performance characteristics of NextDNS using primarily open-source components presents considerable technical challenges. The combination of diverse filtering capabilities (security, privacy, parental controls), real-time analytics, and user customization, all delivered via a high-performance, low-latency global Anycast network, requires sophisticated engineering. Achieving the claimed "on-the-fly" analysis of DNS queries for threat detection ¹ at scale likely involves significant distributed processing capabilities and potentially proprietary algorithms or data sources beyond standard blocklists. Building and managing a comparable Anycast network ¹ demands substantial infrastructure investment and deep expertise in BGP routing and network operations, as detailed later in this report.

Furthermore, the explicit offering of data residency options ¹ underscores the importance of compliance (e.g., GDPR) as a core architectural driver. This necessitates careful design choices regarding log storage, potentially requiring separate infrastructure deployments per region or complex data tagging and access control within a unified system, impacting database selection and overall deployment topology.

Finally, the mention of "Native Tracking Protection" operating at the OS level ¹ suggests capabilities that might extend beyond standard DNS filtering. While DNS can block domains used by OS-level trackers, the description implies a potentially more direct intervention mechanism. This could rely on optional client-side applications provided by NextDNS, adding a layer of complexity that might be difficult to replicate in a purely server-side, DNS-based open-source SaaS offering.

III. Essential Components for a NextDNS-like Service

To construct an open-source service mirroring the core functionalities of NextDNS, several key technical components must be developed or integrated. These form the high-level functional blocks of the system:

1. DNS Server Engine: This is the heart of the service, responsible for receiving incoming DNS requests over various protocols (standard UDP/TCP DNS, DNS-over-HTTPS, DNS-over-TLS, potentially DNS-over-QUIC). It must parse these requests, interact with the filtering subsystem, and either resolve queries recursively, forward them to upstream resolvers, or serve authoritative answers based on the filtering outcome (e.g., providing a sinkhole address). Performance, stability, and extensibility are critical requirements.

2. Filtering Subsystem: This component integrates tightly with the DNS Server Engine. Its primary role is to inspect incoming DNS requests against a set of rules defined by the user and the platform. This includes checking against selected blocklists, applying custom user-defined rules (including allowlists and denylists, potentially using regex), and implementing category-based filtering (security, privacy, parental controls). Based on the matching rules, it instructs the DNS engine on how to respond (e.g., block, allow, rewrite, sinkhole). This subsystem must support dynamic updates to load new blocklist versions and user configuration changes without disrupting service.

3. User Management & Authentication: A robust system is needed to handle user accounts. This includes registration, secure login (potentially with multi-factor authentication), password management (resets, recovery), user profile settings, and the generation/management of API keys or unique configuration identifiers linking clients/devices to specific user profiles. For a SaaS model, this might also need to incorporate multi-tenancy concepts or role-based access control (RBAC) for different user tiers or administrative functions.

4. Web Application & API: This constitutes the user interface and control plane. A web-based dashboard is required for users to manage their accounts, configure filtering policies (select lists, create custom rules), view analytics and query logs, and access support resources. A corresponding backend API is essential for the web application to function and potentially allows third-party client applications or scripts to interact with the service programmatically (e.g., dynamic DNS clients, configuration tools).

5. Data Storage: Multiple types of data need persistent storage, likely requiring different database characteristics.

   • User Configuration Data: Stores user account details, security settings, selected filtering policies, custom rules, allowlists/denylists, and associated metadata. This typically requires a database with strong consistency and transactional integrity (OLTP characteristics).

   • Blocklist Metadata: Information about available blocklists, their sources, categories, and update frequencies.

   • DNS Query Logs: Captures details of DNS requests processed by the service for analytics and troubleshooting. This dataset can grow extremely large very quickly (potentially billions of records per month ¹), demanding a database optimized for high-volume ingestion and efficient time-series analysis (OLAP characteristics).

6. Distributed Infrastructure: To achieve low latency and high availability comparable to NextDNS, a globally distributed infrastructure is mandatory. This involves:

   • Points of Presence (PoPs): Deploying DNS server instances in multiple data centers across different geographic regions.

   • Anycast Routing: Implementing Anycast networking to route user queries to the nearest PoP.

   • Load Balancing: Distributing traffic within each PoP across multiple server instances.

   • Synchronization Mechanism: Ensuring consistent application of filtering rules and user configurations across all PoPs.

   • Monitoring & Health Checks: Continuously monitoring the health and performance of each PoP and the overall service.

   • Deployment Automation: Tools and processes for efficiently deploying updates and managing the distributed infrastructure.

IV. Open-Source DNS Server Engine Evaluation

The DNS server engine is the cornerstone of the service, handling every user query and interacting with the filtering logic. Selecting an appropriate open-source DNS server is therefore a critical architectural decision. The ideal candidate must be performant, reliable, secure, and, crucially for this application, extensible enough to integrate custom filtering logic and SaaS-specific features. The main contenders in the open-source space are CoreDNS, BIND 9, and Unbound.

Contenders

• CoreDNS:

  • Description: CoreDNS is a modern DNS server written in the Go programming language.⁷ It graduated from the Cloud Native Computing Foundation (CNCF) in 2019 ⁹ and is the default DNS server for Kubernetes.⁹ Its defining characteristic is a highly flexible, plugin-based architecture where nearly all functionality is implemented as middleware plugins.⁷ Configuration is managed through a human-readable Corefile.¹³ It supports multiple protocols including standard DNS (UDP/TCP), DNS-over-TLS (DoT), DNS-over-HTTPS (DoH), and DNS-over-gRPC.¹³

  • Pros: The plugin architecture provides exceptional flexibility, allowing developers to chain functionalities and easily add custom logic by writing new plugins.⁷ Configuration via the Corefile is generally considered simpler and more user-friendly than BIND's configuration files.⁸ Being written in Go offers advantages in terms of built-in concurrency handling, modern tooling, and potentially easier development for certain tasks compared to C.⁸ Its design philosophy aligns well with cloud-native deployment patterns.⁸

  • Cons: As a newer project compared to BIND, it may have a less extensive track record in extremely diverse or large-scale deployments outside the Kubernetes ecosystem.⁸ Its functionality is entirely dependent on the available plugins; if a required feature doesn't have a corresponding plugin, it needs to be developed.⁷

  • Relevance: CoreDNS is a very strong candidate for a NextDNS-like service. Its plugin system is ideally suited for integrating the complex, dynamic filtering rules, user-specific policies, and potentially the real-time analysis required for a SaaS offering.

• BIND (BIND 9):

  • Description: Berkeley Internet Name Domain (BIND), specifically version 9, is the most widely deployed DNS server software globally and is often considered the de facto standard.⁸ Developed in the C programming language ⁸, BIND 9 was a ground-up rewrite featuring robust DNSSEC support, IPv6 compatibility, and numerous other enhancements.⁸ It employs a more monolithic architecture compared to CoreDNS ⁸ and can function as both an authoritative and a recursive DNS server.⁹

  • Pros: BIND boasts unparalleled maturity, stability, and reliability, proven over decades of internet-scale operation.⁸ It offers a comprehensive feature set covering almost all aspects of DNS.⁸ It has extensive documentation and a vast community knowledge base. BIND supports Response Policy Zones (RPZ), a standardized mechanism for implementing DNS firewalls/filtering.¹⁷

  • Cons: Its primary drawback is the complexity of configuration and management, which can be steep, especially compared to CoreDNS.⁸ Its monolithic design makes extending it with custom, tightly integrated logic (like per-user SaaS rules beyond RPZ) more challenging than using CoreDNS's plugin model.⁸ It might also be more resource-intensive in some scenarios ⁸ and could be considered overkill for simpler DNS tasks.¹⁵

  • Relevance: BIND remains a viable option due to its robustness and native support for RPZ filtering. However, implementing the dynamic, multi-tenant filtering logic required for a SaaS platform might be significantly more complex than with CoreDNS.

• Unbound:

  • Description: Unbound is primarily designed as a high-performance, validating, recursive, and caching DNS resolver.⁷ Developed by NLnet Labs, it emphasizes security (strong DNSSEC validation) and performance.¹⁵ While mainly a resolver, it can serve authoritative data for stub zones.¹⁵ It supports encrypted protocols like DoT and DoH. Like BIND, Unbound can utilize RPZ for implementing filtering policies.¹⁵ Some sources mention a modular architecture, similar in concept to CoreDNS but perhaps less granular.⁹

  • Pros: Excellent performance for recursive resolution and caching.¹⁵ Strong focus on security standards, particularly DNSSEC.¹⁵ Potentially simpler to configure and manage than BIND for resolver-focused tasks. Supports RPZ for filtering.¹⁵

  • Cons: Not designed as a full-featured authoritative server like BIND or CoreDNS. Its extensibility for custom filtering logic beyond RPZ or basic module integration is less developed than CoreDNS's plugin system.

  • Relevance: Unbound is less likely to be the primary DNS engine handling the core SaaS logic and user-specific filtering. However, it could serve as a highly efficient upstream recursive resolver behind a CoreDNS or BIND filtering layer, or potentially be used as the main engine if RPZ filtering capabilities are deemed sufficient for the service's goals.

Architectural Implications

The selection between CoreDNS and BIND represents a fundamental architectural decision, reflecting a trade-off between modern adaptability and proven stability. CoreDNS, with its Go foundation, plugin architecture, and CNCF pedigree, is inherently geared towards flexibility, customization, and seamless integration into cloud-native environments.⁷ This makes it particularly attractive for building a new SaaS platform requiring bespoke filtering logic and integration with other modern backend services. BIND, conversely, offers decades of proven reliability and a comprehensive, standardized feature set, backed by a vast knowledge base.⁸ Its complexity ⁸ and monolithic nature ⁸, however, present higher barriers to the kind of deep, dynamic customization often needed in a multi-tenant SaaS environment. For integrating complex, user-specific filtering rules beyond the scope of RPZ, CoreDNS's plugin model ⁷ appears significantly more conducive to development and iteration.

While Unbound is primarily a resolver, its strengths in performance and security, combined with RPZ support ¹⁵, mean it shouldn't be entirely discounted. Projects like Pi-hole and AdGuard Home often function as filtering forwarders that rely on upstream recursive resolvers.¹⁹ Unbound is a popular choice for this upstream role.¹⁵ Therefore, a valid architecture might involve using CoreDNS or BIND for the filtering layer and Unbound for handling the actual recursive lookups. Alternatively, if the filtering requirements can be fully met by RPZ, Unbound itself could potentially serve as the primary engine, leveraging its efficiency.

Comparison Summary

The following table summarizes the key characteristics of the evaluated DNS servers:

Feature	CoreDNS	BIND9	Unbound
Primary Role	Flexible DNS Server (Auth/Rec/Fwd)	Authoritative/Recursive DNS Server	Recursive/Caching DNS Resolver
Architecture	Plugin-based 7	Monolithic 8	Modular (Resolver focus)
Configuration Method	Corefile (Simplified) 13	Multiple files (Complex) 8	unbound.conf (Moderate)
Primary Language	Go 7	C 8	C
Extensibility (Filtering)	High (Custom Plugins) 7	Moderate (RPZ, Modules) 17	Moderate (RPZ, Modules) 15
DNSSEC Support	Yes (via plugins)	Yes (Built-in, Mature) 8	Yes (Built-in, Strong Validation) 15
DoH/DoT/DoQ Support	Yes (DoH/DoT/gRPC) 13	Yes (DoH/DoT - newer versions)	Yes (DoH/DoT/DoQ)
Cloud-Native Suitability	High 8	Moderate 8	Moderate/High (as resolver)
Maturity/Stability	Good (Rapidly maturing) 8	Very High (Industry Standard) 8	High (Widely used resolver)
Community Support	Active (CNCF, Go community)	Very Large (Long history)	Active (NLnet Labs, DNS community)

V. Implementing DNS Filtering Logic

Once a DNS server engine is chosen, the next critical task is implementing the filtering logic that forms the core value proposition of a NextDNS-like service. This involves intercepting DNS queries, evaluating them against various rulesets, and deciding whether to block, allow, or modify the response.

Filtering Mechanisms

Several techniques can be employed to achieve DNS filtering:

• DNS Sinkholing: This is a common and straightforward method used by popular tools like Pi-hole ¹⁹ and AdGuard Home.²¹ When a query matches a domain on a blocklist, the DNS server intercepts it and returns a predefined, non-routable IP address (e.g., 0.0.0.0, ::) or sometimes the IP address of the filtering server itself. This prevents the client device from establishing a connection with the actual malicious or unwanted server.

• NXDOMAIN/REFUSED Responses: Instead of returning a fake IP, the server can respond with specific DNS error codes. NXDOMAIN ("Non-Existent Domain") tells the client the requested domain does not exist. REFUSED indicates the server refuses to process the query. Different blocking tools and plugins may use different responses. For instance, the external coredns-block plugin returns NXDOMAIN ²², while the built-in CoreDNS acl plugin offers options to return REFUSED (using the block action) or an empty NOERROR response (using the filter action).²³ The choice of response code can sometimes influence client behavior or application error handling.

• RPZ (Response Policy Zones): RPZ provides a standardized mechanism for encoding DNS firewall policies within special DNS zones. DNS servers that support RPZ (like BIND ¹⁷, Unbound ¹⁵, Knot DNS ¹⁷, and PowerDNS ¹⁷) can load these zones and apply the defined policies (e.g., block, rewrite, allow) to matching queries. Major blocklist providers like hagezi ¹⁷ and 1Hosts ¹⁸ offer their lists in RPZ format, simplifying integration with compatible servers. RPZ offers more granular control than simple sinkholing, allowing policies based on query name, IP address, nameserver IP, or nameserver name.

• Custom Logic (CoreDNS Plugins): The most flexible approach, particularly when using CoreDNS, is to develop custom plugins.⁷ This allows for implementing bespoke filtering logic tailored to the specific needs of the SaaS platform.

  • Existing plugins like acl provide basic filtering based on source IP and query type ²³, but are likely insufficient for a full-featured service.

  • External plugins like coredns-block ²² serve as a valuable precedent, demonstrating capabilities such as downloading multiple blocklists, managing lists via an API (crucial for SaaS integration), implementing per-client overrides (essential for multi-tenancy), handling expiring entries, and returning specific block responses (NXDOMAIN).

  • Developing a unique plugin offers the potential to integrate diverse data sources (blocklists, threat intelligence feeds, user configurations), implement complex rule interactions, perform dynamic analysis (potentially approaching NextDNS's "on-the-fly" analysis claims ¹), and optimize performance for SaaS scale.

Blocklist Management

Effective filtering relies heavily on comprehensive and up-to-date blocklists. A robust management system is required:

• Sources: Leverage high-quality, community-maintained or commercial blocklists. Prominent open-source options include:

  • hagezi/dns-blocklists: Offers curated lists in multiple formats (Adblock, Hosts, RPZ, Domains, etc.) and varying levels of aggressiveness (Light, Normal, Pro, Pro++, Ultimate). Covers categories like ads, tracking, malware, phishing, Threat Intelligence Feeds (TIF), NSFW, gambling, and more.¹⁷ Explicitly compatible with Pi-hole and AdGuard Home.¹⁷

  • 1Hosts: Provides Lite, Pro, and Xtra versions targeting ads, spyware, malware, etc., in formats compatible with AdAway, Pi-hole, Unbound, RPZ (Bind9, Knot, PowerDNS), and others.¹⁸ Offers integration points with services like RethinkDNS and NextDNS.¹⁸

  • Defaults from Pi-hole/AdGuard: These tools come with default list selections.²¹

  • Technitium DNS Server: Includes a feature to add blocklist URLs with daily updates and suggests popular lists.²⁶

  • Specialized/Commercial Feeds: Consider integrating feeds like Spamhaus Data Query Service (DQS) for broader threat coverage (spam, phishing, botnets) ²⁷, similar to how NextDNS incorporates multiple threat intelligence sources.¹ Tools like MXToolbox provide blacklist checking capabilities.²⁸

• Formats: The system must parse and normalize various common blocklist formats, including HOSTS file syntax (IP address followed by domain), domain-only lists, Adblock Plus syntax (which includes cosmetic rules but primarily domain patterns for DNS blocking), and potentially RPZ zone file format.¹⁷

• Updating: Implement an automated process to periodically download and refresh blocklists from their source URLs. This is crucial for maintaining protection against new threats.²⁶ The coredns-block plugin provides an example of scheduled list updates.²²

• Management Interface: The user-facing web application must allow users to browse available blocklists, select which ones to enable for their profile, potentially add URLs for custom lists, and view metadata about the lists (e.g., description, number of entries, last updated time).¹

Custom Rules & Allowlisting/Denylisting

Beyond pre-defined blocklists, users require granular control:

• Custom Blocking Rules: Allow users to define their own rules to block specific domains or patterns. Pi-hole, for example, supports exact domain blocking, wildcard blocking, and regular expression (regex) matching.¹⁹

• Allowlisting (Whitelisting): Provide a mechanism for users to specify domains that should never be blocked, even if they appear on an enabled blocklist.¹ This is essential for fixing false positives and ensuring access to necessary services. Maintaining allowlists for critical internal or partner domains is also a best practice.²⁷

• Denylisting (Blacklisting): Allow users to explicitly block specific domains, regardless of whether they appear on other lists.¹⁹

• Per-Client/Profile Rules: In a multi-user or multi-profile SaaS context, these custom rules and list selections must be applied on a per-user or per-configuration-profile basis. The coredns-block plugin's support for per-client overrides is relevant here ²², as is AdGuard Home's client-specific settings functionality.³¹

Inspiration from Pi-hole/AdGuard Home

Existing open-source projects provide valuable architectural insights:

• Pi-hole: Demonstrates a successful integration of a DNS engine (FTL, a modified dnsmasq written in C ¹⁹) with a web interface (historically PHP, potentially involving JavaScript ³³) and management scripts (Shell, Python ¹⁹). It uses a script (gravity.sh) to download, parse, and consolidate blocklists into a format usable by FTL.³⁵ It exposes an API for statistics and control.¹⁹ Its well-established Docker containerization ²⁹ simplifies deployment. While not a SaaS architecture, its core components (DNS engine, web UI, blocklist updater, API) provide a functional model.³⁶

• AdGuard Home: Presents a more modern, self-contained application structure, primarily written in Go.²¹ It supports a wide range of platforms and CPU architectures ³⁸, including official Docker images.³⁸ It functions as a DNS server supporting encrypted protocols (DoH/DoT/DoQ) both upstream and downstream ²¹, includes an optional DHCP server ²¹, and uses Adblock-style filtering syntax.³¹ Configuration is managed via a web UI or a YAML file.⁴⁰ Its architecture, featuring client-specific settings ³¹, provides a closer model for a potential SaaS backend, although significant modifications would be needed for true multi-tenancy and scalability.²¹

Filtering Implementation Considerations

Relying solely on publicly available open-source blocklists ¹⁷, while effective for basic ad and tracker blocking, is unlikely to fully replicate the advanced, real-time threat detection capabilities claimed by NextDNS (e.g., analysis of DGAs, NRDs, zero-day threats).¹ These advanced features often depend on proprietary algorithms, behavioral analysis, or integration with commercial, rapidly updated threat intelligence feeds.²⁷ Building a truly competitive open-source service in this regard would likely necessitate significant investment in developing custom filtering logic, potentially within a CoreDNS plugin ¹⁴, and possibly integrating external, specialized data sources.

The choice of filtering mechanism itself—RPZ versus a custom CoreDNS plugin versus simpler sinkholing—carries significant trade-offs. RPZ offers standardization and compatibility with multiple mature DNS servers (BIND, Unbound) ¹⁵ but might lack the flexibility needed for highly dynamic, user-specific rules common in SaaS applications. A custom CoreDNS plugin provides maximum flexibility for implementing complex logic and integrations but demands Go development expertise and rigorous maintenance.¹⁴ Simpler sinkholing approaches, like that used by Pi-hole's FTL ³⁴, are easier to implement initially but might face performance or flexibility limitations when dealing with millions of rules and complex interactions at SaaS scale.

Furthermore, efficiently handling potentially millions of blocklist entries combined with per-user custom rules and allowlists presents a data management challenge. The filtering subsystem requires optimized data structures (e.g., hash tables, prefix trees, Bloom filters) held in memory within each DNS server instance for low-latency lookups during query processing. The coredns-block plugin's reference to dnsdb.go ²² hints at this need for efficient in-memory representation. Storing, updating, and synchronizing these massive rule sets across a distributed network of DNS servers requires a scalable backend database and a robust propagation mechanism.

VI. Selecting an Open-Source Web Framework

A web framework is essential for building the user-facing dashboard and the backend API that drives the SaaS platform. The dashboard allows users to manage their configurations, view analytics, and interact with the service, while the API handles data persistence, communicates with the DNS infrastructure (e.g., pushing configuration updates), and manages user authentication.

Requirements

The chosen framework should meet several key requirements:

• Scalability: Capable of handling a growing number of users and API requests.

• Development Efficiency: Provide tools and abstractions that speed up development (e.g., ORM, authentication helpers, templating).

• Database Integration: Offer robust support for interacting with the chosen database(s) (PostgreSQL, TimescaleDB, ClickHouse).

• API Capabilities: Facilitate the creation of clean, secure, and well-documented RESTful or GraphQL APIs.

• Security: Include built-in protections against common web vulnerabilities (XSS, CSRF, etc.) or make integration of security middleware straightforward.

• Ecosystem & Community: Have an active community, good documentation, and a healthy ecosystem of libraries and tools.

Language Considerations and Options

The choice of programming language for the web framework often influences framework selection. Go, Node.js (JavaScript/TypeScript), and Python are strong contenders.

• Node.js (JavaScript/TypeScript):

  • Strengths: Excellent for building web applications and APIs due to its asynchronous, event-driven nature, well-suited for I/O-bound operations. Boasts the largest package ecosystem (npm), offering libraries for virtually any task. Popular choice for modern frontend development (React, Vue, Angular often paired with Node.js backends).

  • Framework Options:

    • AdonisJS: A full-featured, TypeScript-first framework providing an MVC structure similar to frameworks like Laravel or Ruby on Rails.⁴² It comes with many built-in modules, including the Lucid ORM (SQL database integration), authentication, authorization (Bouncer), testing tools, a template engine (Edge), and a powerful CLI, potentially accelerating development by providing a cohesive ecosystem.⁴²

    • Strapi: Primarily a headless CMS, but its strength lies in rapidly building customizable APIs.⁴³ It features a plugin marketplace, a design system for building admin interfaces, and integrates well with various frontend frameworks (Next.js, React, Vue).⁴³ Could be suitable if an API-first approach with a pre-built admin panel is desired. Open source (MIT licensed).⁴³

    • AdminJS: Focused specifically on auto-generating administration panels for managing data.⁴⁴ Offers CRUD operations, filtering, RBAC, and customization using a React-based design system.⁴⁴ Likely more suitable for building an internal admin tool rather than the primary user-facing dashboard of the SaaS.

    • Wasp: A full-stack framework aiming to simplify development by using a configuration language on top of React, Node.js, and Prisma (ORM).⁴⁵ Automates boilerplate code but introduces a specific framework dependency.

    • (Other popular Node.js options like Express, NestJS, Fastify exist but were not detailed in the provided materials).

• Python:

  • Strengths: Strong capabilities in data analysis and visualization, which could be beneficial for building the analytics dashboard component. Large ecosystem for scientific computing, machine learning (potentially relevant for future advanced filtering features). Mature and widely used language.

  • Framework Options:

    • Reflex: An interesting option that allows building full-stack web applications entirely in Python.⁴⁶ It provides over 60 built-in components, a theming system, and compiles the frontend to React. This could simplify the tech stack if the development team has strong Python expertise and prefers to avoid JavaScript/TypeScript.⁴⁶

    • Marimo: An interactive notebook environment for Python, focused on reactive UI for data exploration.⁴⁵ Not a traditional web framework suitable for building the main SaaS application, but could be useful for internal data analysis or specific dashboard components.

    • (Widely used Python frameworks like Django, Flask, and FastAPI are strong contenders, known for their robustness, documentation, and large communities, although not detailed in the provided snippets).

• Go:

  • Strengths: If the DNS engine chosen is CoreDNS ⁷ or AdGuard Home ²¹ (both written in Go), using Go for the backend API and web application could offer significant advantages. It simplifies the overall technology stack, potentially improves performance through direct integration (e.g., shared libraries or efficient RPC instead of REST over HTTP between DNS engine and API), and leverages Go's strengths in concurrency and efficiency.

  • Framework Options:

    • (Popular Go web frameworks like Gin, Echo, Fiber, or the standard library's net/http package could be used, but were not specifically evaluated in the provided materials).

Framework Selection Factors

The decision hinges on several factors. If CoreDNS or a modified AdGuard Home (both Go-based) is selected as the DNS engine, using a Go web framework presents a compelling case for stack unification and potential performance gains, especially for tight integration between the control plane (API) and the data plane (DNS servers). This could simplify inter-component communication. However, the Go web framework ecosystem, while robust, might offer fewer batteries-included, full-stack options compared to Node.js or Python, potentially requiring more manual integration of components like ORMs or authentication libraries.

Node.js frameworks like AdonisJS ⁴² or Strapi ⁴³ offer highly structured environments with many built-in features (ORM, Auth, Admin UI scaffolding) that can significantly accelerate the development of the API and management interface. This comes at the cost of adhering to the framework's specific conventions and potentially introducing a language boundary if the DNS engine is Go-based. Python frameworks like Django or FastAPI (or Reflex ⁴⁶ for a pure-Python approach) offer similar benefits, particularly if the team has strong Python skills or anticipates leveraging Python's data science libraries for analytics features.

Frameworks providing more structure (AdonisJS, Strapi, Django) can speed up initial development by handling boilerplate but impose their own architectural patterns. More minimal frameworks (like Express in Node.js, Flask/FastAPI in Python, or Gin/Echo in Go) offer greater flexibility but require assembling more components manually. The choice ultimately depends on team expertise, desired development speed versus flexibility, and the chosen language for the core DNS engine.

VII. Choosing a Scalable Database Solution

The database layer is critical for storing user information, configurations, and the potentially vast amount of DNS query log data generated by a SaaS platform operating at scale. The distinct requirements for these two data types—transactional consistency for user configurations versus high-volume ingestion and analytical querying for logs—necessitate careful evaluation of database options.

Requirements

• User Configuration Data: This includes user accounts, authentication details, selected blocklists, custom filtering rules (allow/deny lists, regex), API keys, and billing information. This data requires:

  • Transactional Integrity (ACID compliance): Ensuring operations like account creation or rule updates are atomic and consistent.

  • Relational Modeling: User data often has clear relationships (users have configurations, configurations have rules).

  • Efficient Reads/Writes: Relatively fast lookups and updates are needed for user login, profile loading, and configuration changes.

  • Consistency: Changes made by a user should be reflected accurately and reliably. This aligns with typical Online Transaction Processing (OLTP) workloads.

• DNS Query Logs: This dataset captures details for every DNS query processed (timestamp, client IP/ID, queried domain, action taken, etc.). Given NextDNS handles billions of queries monthly ¹, this dataset can become enormous. Requirements include:

  • High-Speed Ingestion: Ability to write millions or billions of log entries per day/week without impacting performance.

  • Efficient Analytical Queries: Supporting fast queries for user dashboards displaying statistics, top domains, blocked queries, time-series trends, etc. This involves aggregations, filtering by time ranges, and potentially complex joins.

  • Scalability: Ability to scale storage and query capacity horizontally as data volume grows.

  • Data Compression/Tiering: Mechanisms to reduce storage costs for historical log data. This aligns with Online Analytical Processing (OLAP) and time-series database workloads.

Contenders

• PostgreSQL:

  • Description: A highly regarded, mature, open-source relational database management system (RDBMS) known for its reliability, feature richness, and standards compliance.⁴⁷ It is fully ACID compliant ⁴⁹, making it excellent for transactional data. It supports advanced SQL features, indexing, and partitioning ⁴⁷, and has a vast ecosystem of extensions.⁴⁹

  • Pros: Ideal for storing structured, relational user configuration data due to its ACID guarantees and data integrity features.⁴⁸ Offers flexible data modeling.⁴⁹ Benefits from strong community support and is widely available as a managed service on all major cloud platforms (AWS RDS, Azure Database for PostgreSQL, Google Cloud SQL).⁵⁰

  • Cons: While capable of handling large datasets with proper tuning (partitioning, indexing), vanilla PostgreSQL can face challenges with the extreme ingestion rates and complex analytical query patterns typical of massive time-series log data compared to specialized databases.⁴⁸ Scaling write performance for logs might require significant effort.

  • Relevance: A primary choice for storing user configuration data. Can be used for logs, but may require extensions or careful optimization for performance and scalability at the target scale.

• TimescaleDB (PostgreSQL Extension):

  • Description: An open-source extension that transforms PostgreSQL into a powerful time-series database.⁴⁷ It inherits all of PostgreSQL's features and reliability while adding specific optimizations for time-series data.⁴⁷ Key features include automatic time-based partitioning (hypertables), columnar compression, continuous aggregates (materialized views for faster analytics), and specialized time-series functions.⁴⁷

  • Pros: Offers a compelling way to handle both relational user configuration data and high-volume time-series logs within a single database system, potentially simplifying the architecture and operational overhead.⁴⁷ Provides significant performance improvements over vanilla PostgreSQL for time-series ingestion and querying.⁴⁷ Can achieve better insert performance than ClickHouse for smaller batch sizes (100-300 rows/batch).⁴⁸ Retains the familiar PostgreSQL interface and tooling.

  • Cons: While highly optimized, it might not match the raw query speed of a pure columnar OLAP database like ClickHouse for certain extremely large-scale, complex analytical aggregations.⁵¹ Adds a layer of complexity on top of standard PostgreSQL.

  • Relevance: A very strong contender, potentially offering the best balance by capably handling both the OLTP workload for user configurations and the high-volume time-series workload for logs within a unified PostgreSQL ecosystem.

• ClickHouse:

  • Description: An open-source columnar database management system specifically designed for high-performance Online Analytical Processing (OLAP) and real-time analytics on large datasets.⁴⁸ Its architecture features columnar storage ⁴⁹, vectorized query execution ⁴⁹, and the MergeTree storage engine optimized for extremely high data ingestion rates, particularly with large batches.⁴⁸ It is designed for scalability and high availability.⁵²

  • Pros: Delivers exceptional performance for data ingestion (potentially exceeding 600k rows/second on a single node with appropriate batching ⁴⁸) and complex analytical queries involving large aggregations.⁴⁹ Offers efficient data compression tailored for analytical workloads.⁴⁹ Generally cost-effective for storing and querying large analytical datasets.⁴⁹

  • Cons: ClickHouse is not a general-purpose database and is poorly suited for OLTP workloads.⁴⁸ It lacks full-fledged ACID transactions.⁴⁸ Modifying or deleting individual rows is inefficient and handled through slow, batch-based ALTER TABLE operations that rewrite data parts.⁴⁸ Its sparse primary index makes point lookups (retrieving single rows by key) inefficient compared to traditional B-tree indexes found in OLTP databases.⁴⁸ Its SQL dialect has some variations from standard SQL.⁵¹ Can consume more disk space than TimescaleDB when ingesting small batches.⁴⁸

  • Relevance: An excellent choice specifically for handling the massive volume of DNS query logs, particularly for powering the analytics dashboard. However, it is unsuitable for storing the transactional user configuration data, necessitating a separate database (like PostgreSQL) for that purpose.

Database Strategy Considerations

Given the distinct nature of user configuration data (requiring transactional integrity) and DNS query logs (requiring high-volume ingestion and analytical performance), a hybrid database strategy often emerges as the most robust solution. This typically involves using a reliable RDBMS like PostgreSQL for the user configuration data, leveraging its ACID compliance and efficient handling of relational data and point updates.⁴⁸ For the DNS query logs, a specialized database like ClickHouse or TimescaleDB would be employed. ClickHouse offers potentially superior raw analytical query performance and ingestion speed for large batches ⁴⁸, making it ideal if maximizing analytics performance is paramount. TimescaleDB, built on PostgreSQL, provides excellent time-series capabilities while allowing the possibility of unifying both data types within a single, familiar PostgreSQL ecosystem.⁴⁷

Attempting to use a single database type for both workloads involves compromises. Vanilla PostgreSQL might struggle to scale efficiently for the log ingestion and complex analytics required.⁴⁸ ClickHouse is fundamentally unsuited for the transactional requirements of user configuration management due to its lack of efficient updates/deletes and transactional guarantees.⁴⁸

TimescaleDB presents the most compelling case for a unified approach.⁴⁷ It leverages PostgreSQL's strengths for the configuration data while adding specialized features for the logs. This simplifies the technology stack, potentially reducing operational complexity (managing backups, updates, monitoring for one system instead of two) and development effort (using a single database interface). However, a thorough evaluation is necessary to ensure TimescaleDB can meet the most demanding analytical query performance requirements at the target scale compared to a dedicated OLAP engine like ClickHouse. The trade-off lies between operational simplicity (TimescaleDB) and potentially higher peak analytical performance with increased architectural complexity (PostgreSQL + ClickHouse).

Comparison Summary

Feature	PostgreSQL (Vanilla)	TimescaleDB (on PostgreSQL)	ClickHouse
Primary Use Case	OLTP, General Purpose	OLTP + Time-Series 47	OLAP, Real-time Analytics 48
Data Model	Relational	Relational + Time-Series Extensions	Columnar 49
ACID Compliance	Yes 49	Yes (Inherited from PostgreSQL)	No (Limited Transactions) 48
Update/Delete Performance	High (for single rows)	High (for single rows)	Low (Batch operations only) 48
Point Lookup Efficiency	High (B-tree indexes)	High (B-tree indexes)	Low (Sparse primary index) 48
High-Volume Ingestion Speed	Moderate (Tuning required)	High (Optimized for time-series)	Very High (Optimized, esp. large batches) 48
Complex Query Perf (Aggreg.)	Moderate/Low (on large data)	High (Continuous aggregates) 47	Very High (Vectorized engine) 49
Scalability	High (with partitioning etc.)	Very High (Built-in partitioning)	Very High (Distributed architecture) 52
Data Compression	Basic/Extensions	High (Columnar time-series) 47	High (Columnar) 49
Ecosystem/Tooling	Very Large	Large (Leverages PostgreSQL)	Growing
Suitability for User Config	Excellent	Excellent	Poor
Suitability for DNS Logs	Fair (Needs optimization)	Excellent	Excellent

VIII. Open-Source User Authentication Systems

A secure and scalable user authentication system is fundamental for any SaaS platform. It needs to manage user identities, handle login processes (potentially including Single Sign-On (SSO) and Multi-Factor Authentication (MFA)), manage sessions, and control access to the platform's features and APIs. Several robust open-source solutions are available.

Key Selection Criteria

When evaluating open-source authentication tools, consider the following criteria:

• Security: Robust encryption, support for standards like OAuth 2.0, OpenID Connect (OIDC), SAML 2.0, MFA options (TOTP, WebAuthn/FIDO2, SMS), secure password policies, regular security updates, and audit logging capabilities.⁵³

• Customizability: Ability to tailor authentication flows, user interface elements, and integrate with custom business logic.⁵³ Open-source should provide deep customization potential.

• Scalability: Capacity to handle a large and growing number of users and authentication requests without performance degradation. Support for horizontal scaling, high availability, and load balancing is crucial.⁵³

• Ease of Use & Deployment: Clear documentation, straightforward setup and configuration, availability of Docker images or Kubernetes operators, and intuitive management interfaces.⁵³

• Community & Support: An active developer community, responsive support channels (forums, chat), and comprehensive documentation are vital for troubleshooting and long-term maintenance.⁵³ Paid support options can be beneficial for enterprise deployments.⁵⁶

• Compatibility: Support for various programming languages, frameworks, and platforms relevant to the rest of the tech stack.⁵³

• Permissions & RBAC: Features for managing user roles and permissions, enabling fine-grained access control to different parts of the application.⁵³

Contenders

• Keycloak:

  • Description: A widely adopted, mature, and comprehensive open-source Identity and Access Management (IAM) platform developed and backed by Red Hat.⁵³

  • Features: Offers a vast feature set out-of-the-box, including SSO and Single Logout (SLO), user federation (LDAP, Active Directory), social login support, various MFA methods (TOTP, WebAuthn), fine-grained authorization services, an administrative console, and support for OIDC, OAuth 2.0, and SAML protocols.⁵³ It's extensible via Service Provider Interfaces (SPIs) and themes.⁵⁶ Deployment is flexible via Docker, Kubernetes, or standalone, using standard databases like PostgreSQL or MySQL.⁵⁶ Supports multi-tenancy through "realms".⁵⁸

  • Pros: Extremely feature-rich, covering most standard IAM needs.⁵³ Benefits from a large, active community, extensive documentation, and the backing of Red Hat.⁵³ Proven stability and scalability for large deployments.⁵⁴ Completely free open-source license.⁵⁴

  • Cons: Can be resource-intensive compared to lighter solutions.⁵³ Setup and configuration can be complex due to the sheer number of features.⁵³ Customization beyond theming often requires Java development and understanding the SPI system, which can be challenging.⁵⁴ Its all-encompassing nature can sometimes lead to inflexibility if specific components or flows need significant deviation from Keycloak's model.⁵⁸

  • Relevance: A strong, mature choice if its comprehensive feature set aligns well with the project's requirements and the team is comfortable with its potential complexity and resource footprint. Excellent if standard protocols and flows are sufficient.

• Ory Kratos / Hydra:

  • Description: Ory provides a suite of modern, API-first, cloud-native identity microservices.⁵⁵ Ory Kratos focuses specifically on identity and user management (login, registration, MFA, account recovery, profile management).⁵⁴ Ory Hydra acts as a certified OAuth 2.0 and OpenID Connect server, handling token issuance and validation.⁵⁴ They are designed to be used together or independently, often alongside other Ory components like Keto (permissions) and Oathkeeper (proxy).⁵⁴

  • Features (Kratos): Self-service user flows, flexible authentication methods (passwordless, social login, MFA), customizable identity schemas via JSON Schema, fully API-driven.⁵⁵ Extensible via webhooks ("Ory Actions") for integrating custom logic.⁵⁴

  • Features (Hydra): Full, certified implementation of OAuth2 & OIDC standards, delegated consent management, designed to be lightweight and scalable.⁵⁵

  • Pros: Highly flexible and customizable due to the API-first design and modular ("lego block") approach.⁵⁴ Well-suited for modern, cloud-native architectures and microservices.⁵⁵ Stateless services facilitate horizontal scaling and high availability.⁵⁵ Good documentation and active community support (e.g., public Slack).⁵⁴ Easier to integrate highly custom authentication flows compared to Keycloak's SPI model.⁵⁸

  • Cons: Requires integrating multiple components (Kratos + Hydra at minimum) for a full authentication/authorization solution, increasing initial setup complexity compared to Keycloak's integrated platform.⁵⁴ The API-first approach means more development effort is needed to build the user interface and user-facing flows.⁵⁵ While the core components are open-source, Ory also offers managed cloud services with associated costs.⁵⁴

  • Relevance: An excellent choice for projects prioritizing flexibility, customizability, and a modern, API-driven, cloud-native architecture. Ideal if the team prefers composing functionality from specialized services rather than using an all-in-one platform.

• Authelia:

  • Description: An open-source authentication and authorization server primarily designed to provide SSO and 2FA capabilities, often deployed in conjunction with reverse proxies like Nginx or Traefik to protect backend applications.⁵⁵

  • Features: Supports authentication via LDAP, Active Directory, or file-based user definitions.⁵⁵ Offers 2FA methods like TOTP and Duo Push.⁵⁵ Provides policy-based access control rules.⁵⁵ Configuration is typically done via YAML, and deployment via Docker is common.⁵⁵

  • Pros: Relatively simple to set up and configure for its core use case.⁵⁵ Lightweight and resource-efficient.⁵⁵ Effective at adding a layer of 2FA and SSO protection to existing applications that may lack these features natively.⁵⁷

  • Cons: Significantly less feature-rich than Keycloak or Ory Kratos/Hydra, particularly regarding comprehensive user management, advanced federation protocols (limited SAML/OIDC provider capabilities), or extensive customization of identity flows.⁵⁷ Primarily acts as an authentication gateway or proxy rather than a full identity provider. Scalability might be more limited ("Moderate" rating in ⁵⁵) compared to Keycloak or Ory for very large user bases.

  • Relevance: Likely too limited to serve as the central user management and authentication system for a full-featured SaaS platform like the one proposed. It might be useful in specific, simpler scenarios or as a complementary component, but lacks the depth of Keycloak or Ory.

• Other Mentions: Several other open-source options exist, including Gluu (enterprise-focused toolkit ⁵⁶), Authentik (user-friendly, full OAuth/SAML support ⁵⁵), ZITADEL (multi-tenancy, event-driven ⁵⁵), SuperTokens (developer-focused alternative ⁵³), Dex (Kubernetes-centric OIDC provider ⁵⁵), LemonLDAP::NG, Shibboleth IdP, and privacyIDEA.⁵⁵ Each has its own strengths and target use cases.

Authentication System Philosophy

The choice between a solution like Keycloak and the Ory suite reflects a fundamental difference in approach. Keycloak offers an integrated, "batteries-included" platform that aims to provide most common IAM functionalities out of the box.⁵⁵ This can lead to faster initial setup if the built-in features meet the requirements. Ory, conversely, provides a set of composable, specialized microservices (Kratos for identity, Hydra for OAuth/OIDC, Keto for permissions) that are designed to be combined via APIs.⁵⁴ This offers greater flexibility and aligns well with microservice architectures but requires more integration effort. Keycloak customization typically involves Java SPIs or themes ⁵⁶, whereas Ory customization relies heavily on interacting with its APIs and potentially using webhooks (Ory Actions).⁵⁴

It is crucial to recognize that self-hosting any authentication system, whether Keycloak or Ory, carries significant responsibility.⁵³ Authentication is paramount to security, and misconfigurations or failure to keep the system updated can have severe consequences. Operational tasks include managing the underlying infrastructure, applying patches and updates, monitoring performance and security logs, ensuring scalability, and handling backups.⁵³ While open-source provides control and avoids vendor lock-in, the operational burden must be factored into the decision, especially for a production SaaS platform handling user credentials. Utilizing community support channels or purchasing paid support becomes essential.⁵³

Comparison Summary

Feature	Keycloak	Ory (Kratos + Hydra)	Authelia
Primary Focus	Full IAM Platform	Composable Identity/OAuth Services	SSO/2FA Authentication Proxy
Architecture	Monolithic (Modular Internally)	Microservices 58	Gateway/Proxy
Core Features	SSO, MFA, User Mgmt, Federation, Social Login, Admin UI 55	User Mgmt, MFA, Social Login (Kratos); OAuth/OIDC Server (Hydra); API-first 54	SSO (via proxy), 2FA, Basic Auth Control 55
Protocol Support	OIDC, OAuth2, SAML 56	OIDC, OAuth2 (Hydra) 55	Primarily Proxy (limited IdP)
Customization Approach	Themes, SPIs (Java) 58	APIs, Webhooks (Actions) 54	Configuration (YAML)
Scalability	High 54	High (Stateless, Cloud-Native) 55	Moderate 55
Deployment Options	Docker, K8s, Standalone 56	Docker, K8s 55	Docker, Standalone 55
Ease of Use/Setup	Moderate/Complex 53	Moderate (API-focused) 55	Easy 55
Community/Support	Very Large (Red Hat) 53	Active 54	Active
Ideal Use Case	Enterprises needing full-featured IAM; Standard protocol integration 53	Modern apps needing custom flows; Microservices; API-driven auth 55	Adding SSO/2FA to existing apps; Simpler needs 57

IX. Designing the Global Deployment Infrastructure

To emulate the low-latency, high-availability user experience of NextDNS ¹, a globally distributed infrastructure is essential. This requires deploying the DNS service across multiple geographic locations (Points of Presence - PoPs) and intelligently routing users to the nearest or best-performing PoP. The core technology enabling this is Anycast networking.

Key Technology: Anycast Networking

• Concept: Anycast is a network addressing and routing strategy where a single IP address is assigned to multiple servers deployed in different physical locations.⁵⁹ When a client sends a packet (e.g., a DNS query) to this Anycast IP address, the underlying network routing protocols (primarily BGP - Border Gateway Protocol) direct the packet to the "closest" instance of that server.⁵⁹ "Closest" is typically determined by network topology (fewest hops) or other routing metrics, not necessarily strict geographic distance.⁶¹ Nearly all DNS root servers and many large TLDs and CDN providers utilize Anycast.⁶¹

• Benefits:

  • Low Latency: By routing users to a nearby server, Anycast significantly reduces round-trip time compared to connecting to a single, distant server.⁵⁹

  • High Availability & Resilience: If one Anycast node (PoP) becomes unavailable (due to failure or maintenance), the network automatically reroutes traffic to the next closest available node, providing transparent failover.⁵⁹

  • Load Distribution: Anycast naturally distributes incoming traffic across multiple locations based on user geography and network paths.⁵⁹

  • DDoS Mitigation: Distributing the service across many locations makes it harder to overwhelm with a denial-of-service attack, as the attack traffic tends to be absorbed by the nodes closest to the attack sources.⁵⁹

  • Configuration Simplicity (for End Users): Users configure a single IP address for the service, regardless of their location.⁶²

• Challenges & Best Practices:

  • Deployment Complexity: Implementing a true Anycast network requires significant network engineering expertise, particularly with BGP. It often involves owning or leasing a portable IP address block (e.g., a /24 for IPv4) and establishing BGP peering relationships with upstream Internet Service Providers (ISPs) or transit providers to announce the Anycast prefix from multiple locations.⁶⁰

  • Consistency & Synchronization: Ensuring that all Anycast nodes serve consistent data (e.g., DNS records, filtering rules) is critical. Discrepancies can lead to inconsistent user experiences.⁶⁰ A robust synchronization mechanism is required.

  • Health Monitoring & Failover: While BGP provides basic reachability-based failover, more sophisticated health monitoring is needed at each PoP to detect application-level failures and withdraw BGP announcements promptly if a node is unhealthy.⁶⁰

  • Troubleshooting: Diagnosing issues can be complex because it's often difficult to determine exactly which Anycast node handled a specific user's request.⁶⁰ Specialized monitoring tools and techniques (like EDNS Client Subnet or specific DNS queries) might be needed.

  • Routing Conflicts & Tuning: BGP routes based on network topology (hop count), while application performance depends on latency. These don't always align.⁶¹ ISP routing policies ("hot-potato routing") can also send traffic along suboptimal paths.⁶¹ Best practices often involve:

    • A/B Clouds: Splitting the Anycast deployment into two or more "clouds," each using a different IP address and potentially different routing policies. This allows DNS resolvers (which often track server latency) to fail over effectively between clouds if one cloud performs poorly for a given client, reinforcing Anycast's failover.⁶¹

    • Consistent Transit Providers: Using the same set of major transit providers at all locations within an Anycast cloud helps prevent suboptimal routing due to ISP peering policies.⁶¹

  • TCP State Issues: While less critical for primarily UDP-based DNS, long-lived TCP connections to an Anycast address can break if network topology changes mid-session and packets get routed to a different node without the established TCP state.⁶⁰ This is relevant if using TCP for DNS or for API/web connections to Anycasted endpoints.

Cloud Provider Evaluation for Infrastructure

Choosing the right cloud provider(s) is crucial for deploying the necessary compute, database, and networking infrastructure, especially the Anycast component.

• Major Cloud Providers (AWS, GCP, Azure):

  • Compute: All offer mature virtual machine instances (EC2, Compute Engine, Azure VMs) and managed Kubernetes services (EKS, GKE, AKS), suitable for running the DNS server software (e.g., CoreDNS containers) and the web application backend.⁵⁰ Serverless functions (Lambda, Cloud Functions, Azure Functions) could host parts of the API.⁵⁰

  • Databases: Provide managed relational databases (RDS for PostgreSQL, Cloud SQL for PostgreSQL, Azure Database for PostgreSQL) ⁵⁰ and potentially managed options or support for self-hosting TimescaleDB or ClickHouse. Globally distributed databases (like Azure Cosmos DB ⁵⁰ or Google Spanner) exist but might be overly complex or expensive for this use case compared to regional deployments with read replicas or a dedicated log database.

  • Networking: Offer Virtual Private Clouds (VPCs/VNets), various load balancing options, and Content Delivery Networks (CDNs).⁵⁰

  • Anycast Support:

    • AWS: Offers Anycast IPs primarily through AWS Global Accelerator, which provides static Anycast IPs routing traffic to optimal regional endpoints (like Application Load Balancers or EC2 instances). CloudFront now also offers dedicated Anycast static IPs, potentially useful for zero-rating scenarios or allow-listing.⁶⁶ Achieving fine-grained BGP control typically requires AWS Direct Connect and complex configurations.⁶⁵

    • GCP: Google Cloud Load Balancing (specifically the Premium Tier network service tier) utilizes Google's global network and Anycast IPs to route users to the nearest backend instances. GCP also supports Bring Your Own IP (BYOIP), allowing customers to announce their own IP ranges via BGP for more control.

    • Azure: Azure Front Door provides global traffic management using Anycast.⁵⁰ The global tier of Azure Cross-region Load Balancer also uses Anycast. Azure supports BYOIP, enabling BGP announcements of customer-owned prefixes.

  • Pros: Extensive global infrastructure (regions, availability zones, edge locations) ⁶⁴, wide range of managed services simplifying operations, mature platforms with strong support and documentation.⁵⁰

  • Cons: Can lead to higher costs, particularly for bandwidth egress.⁵⁰ Anycast implementations are often tied to specific load balancing or CDN services, potentially limiting direct BGP control compared to specialized providers. Potential for vendor lock-in.⁶⁷

• Alternative/Specialized Providers:

  • Vultr: Offers standard cloud compute, storage, and managed databases. Crucially for Anycast, Vultr provides BGP sessions, allowing users to announce their own IP prefixes directly, offering significant network control at competitive pricing points.

  • Fly.io: A platform-as-a-service focused on deploying applications geographically close to users via its built-in Anycast network.⁶⁸ It abstracts much of the underlying infrastructure complexity, potentially simplifying Anycast deployment. Offers dedicated IPv4 addresses and usage-based pricing.⁶⁸ Might be simpler but offers less infrastructure-level control than IaaS providers.

  • Equinix Metal: A bare metal cloud provider offering high levels of control over hardware and networking. Provides reservable Global Anycast IP addresses (from Equinix-owned space) that can be announced via BGP from any Equinix Metal metro.⁶⁹ Billing is per IP per hour plus bandwidth.⁶⁹ Ideal for performance-sensitive applications requiring deep network customization.

  • Cloudflare: While primarily known for its CDN and security services built on a massive Anycast network, Cloudflare also offers services like Workers (serverless compute at the edge), DNS hosting, and Load Balancing with Anycast capabilities. Could potentially host the DNS filtering edge nodes or parts of the API, leveraging their network, but might be less suitable for hosting the core stateful backend (databases, complex application logic).

  • Others: Providers like DigitalOcean, Linode, Hetzner ⁷⁰ offer competitive compute but may have less direct or flexible Anycast/BGP support compared to Vultr or Equinix Metal, often requiring BYOIP. Alibaba Cloud offers Anycast EIPs with specific pricing structures.⁷¹

• Cost Considerations: Implementing Anycast involves several cost factors:

  • IP Addresses: Providers might charge for Anycast IPs directly (e.g., Equinix Metal per IP/hour ⁶⁹, Alibaba config fee ⁷¹). Bringing Your Own IP (BYOIP) requires membership in a Regional Internet Registry (RIR) like ARIN (approx. $500+/year ⁷²) plus the cost of acquiring IPv4 addresses (market rate around $25+/IP or higher for larger blocks ⁷²).

  • Bandwidth: Data transfer, especially egress traffic leaving the provider's network to users, is often a significant cost component in globally distributed systems.⁵⁰ Internal data transfer between PoPs for synchronization also incurs costs.⁷¹ Pricing models vary significantly between providers.

  • Compute & Database: Standard costs for virtual machines, container orchestration, managed databases, storage, etc., apply and vary based on provider, region, and resource size.⁶⁸

Deployment Strategy Outline

A potential deployment strategy would involve:

1. PoP Deployment: Select multiple geographic regions based on target user locations and provider availability. Deploy the chosen DNS server engine (e.g., CoreDNS in containers) and potentially API components within each PoP using VMs or Kubernetes clusters.

2. Anycast Implementation: Configure Anycast routing (either via provider services like Global Accelerator/Cloud LB/Front Door, or by managing BGP sessions with BYOIP on providers like Vultr/Equinix Metal) to announce the service's public IP(s) from all PoPs. Consider A/B cloud strategy for resilience.⁶¹

3. Data Synchronization: Implement a robust mechanism to ensure filtering rules, blocklist updates, and user configurations are propagated consistently and quickly to all DNS server instances across all PoPs. This might involve a central database with regional read replicas, a distributed database system, or a message queue/pub-sub system pushing updates.

4. Backend Deployment: Deploy the main web application/API backend and the primary user configuration database. This could be centralized in one region initially for simplicity or deployed regionally for lower latency configuration changes (at higher complexity).

5. Log Aggregation: Configure DNS servers to stream query logs to a central or regional logging database (e.g., ClickHouse or TimescaleDB) optimized for ingestion and analytics.

6. Health Checks & Monitoring: Implement comprehensive health checks for DNS services, APIs, and databases at each PoP. Integrate with monitoring systems (e.g., Prometheus/Grafana) to track performance and availability globally.²² Ensure failing PoPs automatically stop announcing the Anycast route.

7. Layer Separation: Architecturally separate DNS layers (e.g., filtering edge, internal recursive if needed) for improved security and resilience.⁷³

Infrastructure Considerations

Achieving optimal Anycast performance and control, mirroring the best practices outlined ⁶¹, often necessitates direct management of BGP sessions and potentially utilizing BYOIP. This favors Infrastructure-as-a-Service (IaaS) providers that explicitly offer BGP capabilities (like Vultr, Equinix Metal) or the advanced networking features (including BYOIP support) of major clouds (AWS, GCP, Azure). Relying solely on abstracted Anycast services provided by load balancers or CDNs might limit the ability to implement fine-grained routing policies or the recommended A/B cloud separation for maximum resilience.⁶⁰

The financial implications, particularly bandwidth costs, cannot be overstated. A globally distributed service handling billions of DNS queries ¹ will generate substantial egress traffic. Careful analysis of provider bandwidth pricing models is essential.⁵⁰ Providers with large edge networks and potentially more favorable bandwidth pricing (like Fly.io or Cloudflare, though their suitability for hosting the full stack varies) might offer cost advantages over traditional IaaS egress rates.

Finally, the challenge of maintaining data consistency across a global network of DNS nodes is significant.⁶⁰ Users expect configuration changes (e.g., allowlisting a domain) to take effect globally within a short timeframe. Blocklists require timely updates across all PoPs. This demands a carefully designed synchronization strategy, considering the trade-offs between consistency, availability, and partition tolerance (CAP theorem), and the network latency between PoPs.

Anycast Provider Comparison Summary

Provider	Anycast Offering(s)	BGP Control / BYOIP Support	Global PoP Footprint	Ease of Implementation	Est. Cost Model (IPs, BW, Compute)	Suitability for DNS SaaS
AWS	Global Accelerator, CloudFront Anycast IPs 66	Limited (Direct Connect) / Yes	Very Large 64	Moderate (via Service)	High (Service + BW Egress)	High (Managed Services)
GCP	Cloud Load Balancing (Premium), BYOIP	Yes	Large 64	Moderate (via Service/BGP)	High (Service + BW Egress)	High (Good Network/LB)
Azure	Front Door, Cross-Region LB (Global), BYOIP 50	Yes	Very Large 64	Moderate (via Service/BGP)	High (Service + BW Egress)	High (Enterprise Integration)
Vultr	BGP Sessions for Own IPs	Yes	Moderate	High (Requires BGP config)	Moderate (Competitive Compute/BW)	Very High (Network Control)
Fly.io	Built-in Anycast Platform 68	No (Abstracted)	Moderate	Easy (Platform handles)	Moderate (Usage-based) 68	High (Simplicity)
Equinix Metal	Global Anycast IPs + BGP 69	Yes	Moderate	High (Requires BGP config)	High (Bare Metal + IP/BW fees) 69	Very High (Performance/Control)
Cloudflare	DNS, Load Balancing, Workers (on Anycast Network)	Limited (Enterprise) / Yes	Very Large	Easy (for specific services)	Variable (Service-dependent)	Moderate/High (Edge focus)

X. Proposed Technology Stacks

Synthesizing the evaluations of DNS servers, filtering mechanisms, databases, authentication systems, and infrastructure options, we can propose several viable technology stacks based primarily on open-source components. Each stack represents different trade-offs between flexibility, maturity, operational complexity, and development effort.

Stack 1: The Flexible Go Ecosystem

This stack prioritizes flexibility and leverages the Go ecosystem for core components, aligning well with modern cloud-native practices.

• DNS Engine: CoreDNS.⁷ Chosen for its exceptional plugin architecture, allowing for deep customization of filtering logic and integration with the SaaS backend.

• Filtering: Custom CoreDNS Plugin (written in Go). This plugin would handle blocklist fetching/parsing (using sources like hagezi ¹⁷ or 1Hosts ¹⁸), apply user-specific rules (allow/deny/custom), integrate with the user configuration database, and potentially implement advanced filtering techniques. Inspiration can be drawn from existing plugins like coredns-block.²²

• Web Framework/API: Go (using frameworks like Gin, Echo, or Fiber). This choice ensures language consistency with the DNS engine, potentially simplifying development and enabling high-performance communication between the API/control plane and the DNS data plane.

• Database: PostgreSQL + TimescaleDB Extension.⁴⁷ This provides a unified database system capable of handling both transactional user configuration data (leveraging PostgreSQL's strengths) and high-volume time-series DNS logs (using TimescaleDB's optimizations).

• Authentication: Ory Kratos + Ory Hydra.⁵⁴ Selected for their modern, API-first, cloud-native design, offering high flexibility for building custom authentication flows suitable for a SaaS platform. Aligns well with a Go-based backend.

• Infrastructure: Deployed on Kubernetes clusters hosted on providers offering good BGP control (e.g., Vultr, Equinix Metal) or major clouds with robust BYOIP/Global Load Balancing support (GCP, Azure). This allows for fine-grained Anycast implementation.

• Rationale: This stack maximizes flexibility through CoreDNS plugins and the Ory suite. Using Go throughout the backend simplifies the toolchain and allows for tight integration. TimescaleDB potentially simplifies the database layer.

• Trade-offs: Requires significant Go development expertise, particularly for the custom CoreDNS plugin. CoreDNS, while mature, might be perceived as less battle-tested in massive non-Kubernetes deployments than BIND. The Ory suite requires integrating and managing multiple distinct services for full authentication/authorization capabilities.

Stack 2: The Mature & Robust Approach

This stack favors well-established, highly reliable components, potentially reducing risk but potentially sacrificing some flexibility.

• DNS Engine: BIND9.⁸ Chosen for its unmatched stability, maturity, and native, standardized support for RPZ filtering. Alternatively, Unbound ¹⁵ could be used if its RPZ capabilities are deemed sufficient and its resolver performance is prioritized.

• Filtering: RPZ (Response Policy Zones). Filtering logic is implemented primarily using RPZ zones generated from blocklist sources (e.g., hagezi/1Hosts RPZ formats ¹⁷). Managing user-specific overrides would require custom tooling to dynamically generate or modify RPZ zones per user/profile, which adds complexity.

• Web Framework/API: Node.js (e.g., AdonisJS ⁴² for a full-featured experience) or Python (e.g., Django or FastAPI). These ecosystems offer mature tools for building robust web applications and APIs, potentially faster than building from scratch in Go.

• Database: PostgreSQL (for user configuration) + ClickHouse (for DNS logs).⁴⁸ This hybrid approach uses PostgreSQL for its transactional strengths and ClickHouse for its superior OLAP performance on massive log datasets.

• Authentication: Keycloak.⁵³ Selected for its comprehensive, out-of-the-box feature set covering most standard IAM requirements, reducing the need for custom authentication development.

• Infrastructure: Deployed on managed Kubernetes (e.g., AWS EKS, GCP GKE, Azure AKS) using managed databases (RDS, Cloud SQL, Azure SQL for PostgreSQL) and potentially self-hosted ClickHouse clusters or a managed ClickHouse service. Anycast implemented using provider-managed services (e.g., AWS Global Accelerator, GCP Cloud Load Balancing, Azure Front Door).

• Rationale: Leverages highly mature and widely trusted components (BIND, PostgreSQL, Keycloak). Separates log storage into a dedicated OLAP database (ClickHouse) for optimal analytics performance. Utilizes feature-rich web frameworks for potentially faster API/dashboard development.

• Trade-offs: Filtering flexibility is limited by the capabilities of RPZ; implementing dynamic, per-user rules beyond basic overrides is complex. Managing two distinct database systems (PostgreSQL and ClickHouse) increases operational overhead. Keycloak, while feature-rich, can be resource-heavy and complex to customize deeply.⁵³ Relying on provider-managed Anycast services might offer less granular control over routing compared to direct BGP management.

Stack 3: The AdGuard Home Inspired Model

This stack proposes leveraging an existing open-source DNS filter as a starting point, potentially accelerating initial development but requiring significant adaptation.

• DNS Engine: AdGuard Home (modified).²¹ Start with the AdGuard Home codebase (written in Go) and adapt it for multi-tenancy, scalability, and the specific API requirements of a SaaS platform.

• Filtering: Utilize AdGuard Home's built-in filtering engine, which supports Adblock syntax and custom rules.³¹ Requires substantial modification to handle per-user configurations and potentially millions of rules efficiently at scale. Integrate standard blocklists.¹⁷

• Web Framework/API: Go. Extend AdGuard Home's existing web server and API capabilities or build a separate Go service that interacts with the modified AdGuard Home core.²¹

• Database: PostgreSQL + TimescaleDB Extension.⁴⁷ Similar to Stack 1, offering a unified database for configuration and logs.

• Authentication: Ory Kratos + Ory Hydra.⁵⁴ Provides a flexible, modern authentication solution suitable for integration with the Go backend.

• Infrastructure: Consider deploying on Fly.io ⁶⁸ to simplify Anycast network deployment by leveraging their platform, or use Kubernetes on any major cloud provider.

• Rationale: Starts from an existing, functional open-source DNS filter written in Go, potentially reducing the time needed to achieve basic filtering functionality. Using Fly.io could significantly lower the barrier to entry for implementing Anycast.

• Trade-offs: Requires deep understanding and significant modification of the AdGuard Home codebase to meet SaaS requirements (multi-tenancy, scalability, robust API, per-user state management). May inherit architectural limitations of AdGuard Home not designed for this scale. Filtering flexibility might be less than a custom CoreDNS plugin. Using Fly.io introduces a specific platform dependency.

XI. Conclusion and Recommendations

Summary of Findings

Building an open-source SaaS platform analogous to NextDNS is a technically demanding but feasible undertaking. The core challenges lie in replicating the sophisticated, real-time filtering capabilities, achieving globally distributed low-latency performance via Anycast networking, managing massive data volumes (especially query logs), and ensuring robust security and scalability, all while primarily using open-source components.

The analysis indicates that:

• DNS Engine: CoreDNS offers superior flexibility for custom filtering logic via its plugin architecture, making it highly suitable for a SaaS model, while BIND provides unparalleled maturity and standardized RPZ filtering. Unbound serves best as a high-performance resolver component.

• Filtering: Relying solely on public blocklists is insufficient to match advanced threat detection; custom logic and potentially commercial feeds are likely necessary. RPZ offers standardization but less flexibility than custom CoreDNS plugins. Efficiently managing and applying millions of rules per user is a key performance challenge.

• Databases: A hybrid approach using PostgreSQL for transactional user configuration and a specialized database (ClickHouse for peak OLAP or TimescaleDB for unified time-series/relational) for logs appears optimal. TimescaleDB offers a compelling simplification by potentially handling both workloads within the PostgreSQL ecosystem.

• Authentication: Keycloak provides a comprehensive out-of-the-box solution, while the Ory suite offers greater flexibility and a modern, API-first approach suitable for cloud-native designs. Self-hosting either requires significant operational commitment.

• Infrastructure: Implementing effective Anycast networking is critical for performance but complex, often requiring direct BGP management and careful provider selection. Bandwidth costs and data synchronization across global PoPs are major operational considerations.

Recommendations

Based on the analysis, the following recommendations are provided:

1. Prioritize Flexibility and Customization (Recommended: Stack 1): For teams aiming to build a highly differentiated service with unique filtering capabilities and prioritizing a modern, flexible architecture, Stack 1 (CoreDNS + Go API + Ory + TimescaleDB) is recommended. This approach embraces the extensibility of CoreDNS and the modularity of Ory. However, it requires significant investment in developing the custom CoreDNS filtering plugin and strong Go expertise across the backend. The potential unification of the database layer with TimescaleDB is a significant advantage in operational simplicity.

2. Prioritize Stability and Maturity (Recommended: Stack 2): For teams prioritizing stability, leveraging well-established components, and potentially having stronger expertise in Node.js/Python than Go, Stack 2 (BIND/RPZ + Node/Python API + Keycloak + PostgreSQL/ClickHouse) is a viable alternative. This stack uses industry-standard components but introduces operational complexity with a hybrid database system and potentially limits filtering flexibility due to reliance on RPZ. Keycloak offers rich features but requires careful management and potentially complex customization.

3. Accelerated Start (Conditional Recommendation: Stack 3): Using AdGuard Home as a base (Stack 3) should only be considered if the team possesses the expertise to heavily modify its core for SaaS requirements (multi-tenancy, scalability, API) and if the primary goal is rapid initial development of basic filtering. This path carries risks regarding long-term scalability and flexibility compared to building on CoreDNS or BIND.

4. Invest in Network Expertise: Regardless of the chosen software stack, successfully implementing and managing the global Anycast infrastructure is paramount. Access to deep network engineering expertise, particularly in BGP routing and distributed systems monitoring, is non-negotiable. Failure in network design or operation will undermine the core value proposition of low latency and high availability.

5. Adopt Phased Rollout: Begin deployment with a limited number of geographic PoPs to validate the architecture and operational procedures before scaling globally. This allows for incremental learning and refinement of the Anycast implementation, synchronization mechanisms, and monitoring strategies.

6. Emphasize Automation and Monitoring: Given the complexity of a distributed system, robust automation for deployment (CI/CD pipelines, infrastructure-as-code) and comprehensive monitoring (system health, application performance, network latency, filtering effectiveness) are essential from day one.

Final Thoughts

Creating an open-source alternative to NextDNS presents a significant engineering challenge, particularly in matching the performance and feature breadth of a mature commercial service. However, by carefully selecting appropriate open-source components—leveraging the flexibility of CoreDNS or the maturity of BIND, combined with suitable database and authentication solutions, and underpinned by a well-designed Anycast network—it is possible to build a powerful and valuable platform. Success will depend critically on making informed architectural trade-offs that balance flexibility, performance, scalability, cost, and operational complexity, with a particular emphasis on mastering the intricacies of distributed DNS infrastructure.

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