Build, framework, or boilerplate?

Three real ways to ship a RAG product for EU clients. Here's the effort and risk profile of each, without the "competitor X is bad" framing.

This page expands the comparison on the landing page into the three structural choices a team faces. Specific feature claims about named projects must be re-checked against their current docs before you rely on them; the RAG landscape moves fast.

Build it yourself

In-house development · engineering time
Best when RAG is a long-term strategic capability and you have (or plan to hire) a dedicated team that owns the stack end-to-end.
  • Effort: Significant and hard to predict.
  • Full control over architecture and dependencies.
  • You own the GDPR posture entirely, including the obligation to design, test, and maintain deletion cascades, audit logging, and data-residency rules yourself.
  • You carry the long tail: edge cases in document parsing, embedding-model migrations, retention and backup policy, security review.
  • No license fee, but the build cost is the dominant cost.

Generic framework (LangChain, LlamaIndex, Haystack)

Open-source libraries · free
Best when you need to assemble a custom pipeline (research, novel retrieval strategies, multi-agent orchestration) and the framework's primitives are the bulk of what you'd otherwise write.
  • LangChain: retrieval, embedding, and chain primitives; the production wrapper (auth, UI, document ingestion, audit log, GDPR tooling) is still yours to build.
  • LlamaIndex: data ingestion and indexing abstractions; same production-wrapper caveat applies.
  • Haystack: the most opinionated of the three on pipelines; focuses on the retrieval/QA primitives.
  • GDPR/data-residency tooling is not provided by the framework; it's a deployment and integration concern you solve yourself.
  • You inherit the framework's breaking-change cadence as ongoing maintenance cost.

Ready-to-use app (AnythingLLM, and similar)

Open-source application · free / paid hosted
Best when you want a working RAG application you can install and use directly, with minimal customization needed.
  • Full application out of the box: UI, auth, document management, multi-user.
  • Customizing the application's behavior or UI is harder than starting from a boilerplate, because you're modifying a finished product rather than extending a designed-to-be-extended one.
  • Vector search with optional reranking (LanceDB); no BM25 keyword/hybrid retrieval.

SaaS boilerplate (ChatRAG, StartKit.AI)

Paid boilerplate · $199–399
Best when your goal is to launch a multi-tenant chatbot SaaS with Stripe billing on cloud infrastructure, and you're comfortable with a JavaScript / Node.js stack.
  • ChatRAG: $199 (Starter) / $269 (Complete). Stack: Next.js + OpenAI + Supabase.
  • StartKit.AI: $199–399 (one-time perpetual license).
  • Cloud-first by design: typically assumes managed vector and relational databases.
  • Built-in SaaS primitives (Stripe, multi-tenancy, billing) that EuroRAG does not ship.
  • GDPR data-residency tooling is generally not the focus; verify against each project's current docs if it matters to you.

EuroRAG

Paid boilerplate · from €89
Best when you're an EU-based team (or a team building for EU clients) shipping a self-hosted, customizable RAG product and you want GDPR tooling built into the architecture rather than bolted on at deployment.
  • Python-only stack (FastAPI + Jinja2 + HTMX), no JavaScript build pipeline.
  • Self-hosted: vectors, relational database, document storage, and inference all run on your infrastructure.
  • Hybrid BM25 + vector search (reciprocal rank fusion), which none of the compared tools offer by default.
  • GDPR tooling as architectural primitives: per-provider data residency tracking, deletion cascade across all storage layers, audit logging, consent management, and an 11-check compliance dashboard (Professional tier and above).
  • Core/custom directory separation so you can customize without forking.
  • Perpetual license, 12 months of updates, optional renewal. See pricing and license.

These options are not strictly ordered. The right choice depends on whether your dominant cost is engineering time (favoring a framework or boilerplate), your need for SaaS primitives (favoring a SaaS boilerplate), your need to customize deeply (favoring a Python boilerplate like EuroRAG, or building), or your requirement for an install-and-go application (favoring AnythingLLM). Pricing figures shown are verified as of July 2026; specific feature claims about named projects should be re-checked against their current docs before you rely on them, as the RAG landscape moves fast.