If you're building a RAG system in 2026, you've probably narrowed your framework choice down to two open-source contenders: Haystack and LlamaIndex. Both are production-grade. Both have active communities. Both will get you to a working prototype in a weekend. But they're built around different opinions, and picking the wrong one means rewriting your retrieval layer six months in.
This comparison is for builders who've already decided not to roll their own from scratch. We'll skip the "what is RAG" preamble and get to the parts that matter when you're choosing between them.
Why This Comparison Matters
Both frameworks landed in the RAG space early and have evolved in different directions. Haystack, from deepset, started life as a search and QA framework and pushed into agents and pipelines. LlamaIndex started as GPT Index — a tool to feed LLMs your data — and grew into a full data framework with first-class parsing.
That origin matters. Haystack thinks in pipelines and components. LlamaIndex thinks in documents, indices, and query engines. Both abstractions work. They just feel different in your hands, and they reward different kinds of applications.
Feature Comparison Table
| Feature | Haystack | LlamaIndex |
|---|---|---|
| Core Abstraction | Pipeline + Components | Index + Query Engine |
| Document Parsing | Solid, integrations-based | Best-in-class via LlamaParse |
| PDF / Table Extraction | Via 3rd-party connectors | Native LlamaParse, multimodal |
| Agentic Workflows | Strong, first-class agents | Supported, less mature |
| Vector Store Integrations | 40+ providers | 50+ providers |
| LLM Provider Support | Broad, modular | Broad, modular |
| Production Deployment | Hayhooks, REST API tools | LlamaDeploy, microservices |
| Visual / Low-Code Tooling | Limited | Limited |
| Learning Curve | Steep (pipeline DAG model) | Steep (many abstractions) |
| Community Size | Large, enterprise-leaning | Larger, developer-leaning |
| Language | Python | Python + TypeScript |
| Best For | Pipelines, search, enterprise QA | Document-heavy RAG, parsing |
Pricing Comparison
Both frameworks are open source under permissive licenses. You can run either for $0 in infrastructure costs beyond what you pay for your LLM API and vector store. The cost differences show up in the paid services each project ships alongside the framework.
Haystack
- Open Source: Free. Full framework, all integrations, no usage caps.
- Enterprise (deepset): Custom pricing. Adds SLA-backed support, deepset Cloud (managed hosting), and professional services. Aimed at companies with regulated workloads or large teams.
LlamaIndex
- Open Source: Free. Full framework, community support.
- LlamaParse: $10 per 1,000 pages. This is the differentiator — a managed parsing service for complex PDFs, tables, and multimodal documents.
- LlamaExtract: Usage-based pricing for structured data extraction with custom schemas.
If your documents are clean HTML or Markdown, you'll never pay LlamaIndex a cent. If you're ingesting messy enterprise PDFs with nested tables, LlamaParse is genuinely worth the line item — and it's the single biggest reason teams pick LlamaIndex over Haystack.
Use Case Scenarios
Pick Haystack When...
- You think in pipelines. If your application has clear stages — fetch, embed, retrieve, rerank, generate — Haystack's component model maps cleanly onto that mental model.
- You're building search-heavy applications. Haystack's heritage is in search and QA. Hybrid retrieval, reranking, and BM25 integrations feel first-class.
- You need agentic workflows in production. Haystack agents are mature, and the pipeline DAG gives you observability you'd otherwise build yourself.
- You're a regulated enterprise. deepset's commercial offering and the framework's auditability story land well with security review teams.
- You want to deploy as REST APIs without writing FastAPI. Hayhooks wraps pipelines as HTTP endpoints with minimal ceremony.
Pick LlamaIndex When...
- Your data is the hard part. If you're ingesting PDFs, scanned documents, spreadsheets, or anything with non-trivial structure, LlamaParse is unmatched in the open-source ecosystem.
- You want a TypeScript option. LlamaIndex.TS is a real port, not a wrapper. If your stack is Node-first, this matters.
- You're building document-centric applications. Knowledge bases, contract review tools, research assistants — anything where the document model is central — fits LlamaIndex's grain.
- You want structured extraction baked in. LlamaExtract handles schema-driven extraction without bolting on a second framework.
- You're earlier in the build. LlamaIndex's defaults get you to a working prototype faster than Haystack's component composition.
Verdict
These two frameworks tie on raw capability — both score 8.2 in our review, both will ship production RAG systems, and both have communities that will still be active in two years. The choice comes down to what your application is actually doing.
For document-heavy RAG, LlamaIndex is the winner. LlamaParse alone justifies the choice when you're staring down a corpus of messy PDFs. The TypeScript SDK is a real advantage for full-stack JavaScript teams.
For pipeline-driven applications and enterprise search, Haystack is the winner. The component model holds up as systems grow complex, the agent story is more mature, and deepset's enterprise tier is the cleaner path for regulated industries.
If you're genuinely undecided, build the same trivial RAG against both this week. You'll know which one fits your head within a few hours. The wrong choice isn't catastrophic — both expose enough escape hatches that you can swap components — but the right choice will save you a month of fighting the framework's grain.