LlamaIndex vs Haystack
Both are alternatives to LangChain. Here's how they stack up — verified facts, no spin.
Also searched as Haystack vs LlamaIndex — same comparison, one verdict.
LlamaIndex and Haystack are closely matched on ownership (90 vs 89) — this one comes down to pricing and to which trade-offs below you can live with.
LlamaIndex
The focused RAG framework — data in, grounded answers out.
LlamaIndex (MIT, ~49k stars) is the framework to pick when your problem is specifically retrieval — getting your documents ingested, indexed, and answered over. Its primitives are built around the data side (loaders, indexes, retrievers, query engines) rather than trying to abstract everything, which keeps it noticeably leaner to reason about than LangChain for RAG builds. The company monetizes hosted parsing/extraction (LlamaCloud); the framework itself stays open and self-sufficient.
Haystack
The production-pipeline veteran — explicit, modular, Apache-2.0.
Haystack (Apache-2.0, by Germany's deepset) is the framework for teams who want explicit, inspectable pipelines rather than magic: you wire components — retrievers, routers, generators, memory — into a graph you can see and test. It predates the LLM boom (it started in extractive search), and that production heritage shows in its stability and its popularity for serious RAG and semantic-search deployments in enterprises.
Side by side
10 points of comparison, every one read from a verified field. Green marks the side that wins a row outright. A dash means we do not hold that fact — never that it is zero.
| LlamaIndex | Haystack | |
|---|---|---|
| Sovereignty ScoreOur transparent 0–100 composite for data ownership and exit cost. | 90 | 89 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first data | Yes | Yes |
| License | MIT | Apache-2.0 |
| Pricing | Free (MIT); optional hosted LlamaCloud for managed parsing/extraction | Free (Apache-2.0); deepset offers a commercial platform on top |
| RAM to run it wellThe figure that actually matters, not the vendor's minimum. | 4 GB for the app; your vector store is the real cost | 4 GB |
| Realistic running costWhat the box costs each month if you run it yourself. | $0 for the framework. The bill is embeddings and the vector store underneath. | $0 for the framework; embeddings and the store are the bill |
| Setup timeHonest first-install estimate, not the marketing quickstart. | An hour to a working pipeline, weeks to a good one | A day to a working pipeline |
| Ongoing maintenanceThe part nobody budgets for. | Version churn is real — the API has moved fast. Pin versions on anything long-lived. | Moderate. The 1.x to 2.x transition was a rewrite — check which version tutorials target. |
LlamaIndex edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Weighing both against staying on LangChain? Is LangChain free? What it actually costs →
LlamaIndex
Strengths
- +Purpose-built for RAG — the data primitives are the product
- +Leaner mental model than LangChain for retrieval apps
- +Huge loader/integration ecosystem for document types
Trade-offs
- −Less suited to general agent orchestration than dedicated tools
- −Fast-moving API surface — pin versions
Haystack
Strengths
- +Explicit pipeline graphs — no hidden control flow
- +Production-grade stability; strong enterprise track record
- +EU-rooted project with a clean open-source license
Trade-offs
- −More upfront wiring than the batteries-included frameworks
- −Smaller ecosystem than LangChain's
Which one fits you
The trade-offs above, turned into a decision. Find the line that describes your team.
Choose LlamaIndex
if a lower exit cost matters more to you than any single feature, and purpose-built for RAG — the data primitives are the product.
Choose Haystack
if explicit pipeline graphs — no hidden control flow.
Neither, yet
if both carry a real cost you should weigh first — less suited to general agent orchestration than dedicated tools, and more upfront wiring than the batteries-included frameworks. If either of those is a dealbreaker for your team, the shortlist is wrong rather than the choice.
What it takes to run these yourself
Real requirements and honest running costs, not the vendor quickstart.
Self-hosting LlamaIndex
4 GB for the app; your vector store is the real cost RAM · An hour to a working pipeline, weeks to a good one
$0 for the framework. The bill is embeddings and the vector store underneath.
Self-hosting Haystack
4 GB RAM · A day to a working pipeline
$0 for the framework; embeddings and the store are the bill
LlamaIndex vs Haystack — common questions
Is LlamaIndex a better fit than Haystack for llm & agent frameworks?
It depends on what you are optimising for, and the honest split is this: LlamaIndex scores 90 to Haystack's 89 on data ownership and exit cost, so it is the safer choice if you care about being able to leave. Haystack earns its place on a different axis — explicit pipeline graphs — no hidden control flow. Neither is a wrong answer for every team; the table above is the actual comparison.
What happens if we want to switch later?
LlamaIndex keeps its data local or in open formats, so leaving is an export rather than a negotiation. Haystack is still self-hostable, so the files stay on your server either way — but it is not local-first by design, so check what its export produces before you rely on it.
Can I self-host LlamaIndex or Haystack?
Both can be self-hosted. The difference is what it costs you in time rather than whether it is possible — see the setup and maintenance rows above.
Are LlamaIndex and Haystack both alternatives to LangChain?
Yes — both appear in our LangChain comparison, which is why they are worth putting side by side. People usually arrive here already having decided to move off LangChain and now choosing between the two replacements, which is a narrower and much easier question.
More LangChain head-to-heads
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Facts verified 2026-07-16. Licenses and pricing change — spotted something out of date? That's a correction we want.