Haystack vs LlamaIndex: Which One for Side Projects?
Haystack has 24,592 stars on GitHub while LlamaIndex boasts almost double with 47,902 stars. But stars don’t ship features, and star count alone won’t tell you which tool you should pick for your side projects. If you’re trawling through libraries for building AI search and document indexing but confused by the sea of options, specifically between haystack vs llamaindex, you’re in the right place.
I’ve spent a fair amount of time hacking with both, and here’s my no-fluff take: one of these tools is more polished for quick prototyping and fast dev cycles, while the other has raw power but comes with quirks that’ll slow you down unless you’re ready to wrestle with complexity.
| Metric | Haystack (deepset-ai/haystack) | LlamaIndex (run-llama/llama_index) |
|---|---|---|
| GitHub Stars | 24,592 | 47,902 |
| Forks | 2,671 | 7,072 |
| Open Issues | 102 | 269 |
| License | Apache-2.0 | MIT |
| Last Updated | March 23, 2026 | March 20, 2026 |
| Pricing | Open Source, Free | Open Source, Free |
What is Haystack Actually Doing?
Haystack, developed by deepset, is a Python framework for building search systems that tap into large language models (LLMs) and traditional NLP models for document retrieval and question-answering. It’s laser-focused on search pipelines fed by any source—PDFs, elasticsearch, or even raw text—and comes with an abstraction layer that integrates embedding models, retrievers, and readers. It mostly targets semantic search, bringing in vector stores like FAISS, Milvus, or Elasticsearch for similarity search, plus options for QA on chunks of documents.
Here’s a short snippet to spin up a basic haystack pipeline that answers questions on a small set of documents:
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import FARMReader, BM25Retriever
from haystack.pipelines import ExtractiveQAPipeline
# Initialize document store
document_store = InMemoryDocumentStore()
# Write some sample docs
docs = [{"content": "Python is a programming language.", "meta": {"source": "intro"}}]
document_store.write_documents(docs)
# Retriever & Reader combo
retriever = BM25Retriever(document_store=document_store)
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
# Build pipeline
pipe = ExtractiveQAPipeline(reader, retriever)
# Ask a question
res = pipe.run(query="What is Python?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 3}})
print(res["answers"][0].answer)
What’s Good
- Batteries included: Haystack ships with connectors for popular vector stores, different retrievers (BM25, dense retrievers), and pretrained readers ready for QA out-of-the-box.
- Open source and free: Apache-2.0 license means you can bend it any way you want professionally or for hobby projects.
- Solid documentation & active community: The docs are decent, and the project has an active Discord and GitHub presence. Issues get attention quickly.
- Production-ready design: If you want to build something close to production, Haystack’s pipelines scale nicely, and microbes like handling document updates, embeddings, and retriever-reader orchestration are thoughtfully implemented.
- Supports various deploy options: You can run locally, deploy with Kubernetes, or cloud options, which is neat for eventual MVPs.
What Sucks
- Heavyweight setup: It’s a beast in terms of dependencies and is often slower to get running on your laptop unless you trim the fat.
- Overkill for small scale: For tiny projects or quick experiments, setting up Haystack feels like using a sledgehammer to crack a nut.
- Sometimes confusing API: Parts of the API require you to understand retrievers, readers, embedding models, and their interplay—steeper learning curve compared to LlamaIndex.
What is LlamaIndex Actually Doing?
LlamaIndex (formerly GPT Index) by run-llama is also a Python framework but it’s more like a glue code that sits between your data and language models. Its goal: help you build a structured index over documents to query LLMs effectively without wrestling with vector databases explicitly. It focuses more on creating custom data structures that can be queried with natural language through LLMs.
Here’s the core of what using LlamaIndex looks like—loading documents and querying an index:
from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader
# Load docs from a directory
documents = SimpleDirectoryReader('data/').load_data()
# Create an index
index = GPTSimpleVectorIndex(documents)
# Query your index
response = index.query("What's Python?")
print(response)
What’s Good
- Simplicity: The API is intuitive—load your docs, build an index, and query it. No wrestling with retrievers or multiple model types.
- Great for prototyping: It’s fantastic for side projects, demos, and quick personal tools. You can get a search or question-answering system going with just a few lines.
- Flexible indexing: Supports multiple different index types (vector, tree, list), which helps you customize without much effort.
- MIT License: More permissive than Apache in some use cases, which can be a plus for startups experimenting with code reuse.
What Sucks
- Brittle scaling: It’s not really built for heavy production workloads or huge datasets.
- Issue overload: 269 open issues on GitHub and some user complaints about bugs and slow response times from maintainers.
- Less mature ecosystem: Compared to Haystack, third-party tools, tutorials, and integrations are spotty.
- Limited deployment guidance: Docs on scaling and deploying are minimal, so if you want to do anything beyond a proof of concept, you’re basically on your own.
Head-to-Head: Where Does Each Tool Win?
| Criteria | Haystack | LlamaIndex | Winner |
|---|---|---|---|
| Ease of Use | Steeper learning curve, verbose setup | Simple, minimal API | LlamaIndex |
| Feature Completeness | Full-fledged retrievers, readers, pipeline management | Basic indexing and querying | Haystack |
| Community & Maintenance | Active, responsive, fewer open issues (102) | Larger community but more open issues (269) | Haystack |
| Production Readiness | Designed with production in mind | Prototype focused, fragile on scale | Haystack |
| Flexibility with Data Sources | Built-in support for many document stores | Limited to file loading and lambda function injection | Haystack |
Look, if you want an easy on-ramp and don’t care about production scalability, LlamaIndex feels like a warm hug. But if you aim to build something sustainable that can grow beyond your side project, Haystack is the better bet.
The Money Question
Both projects are open-source and free to code around. That’s the good news. But pricing for side projects often gets banged up by hidden costs tied to required infrastructure and external APIs, especially the LLMs running behind these tools.
Haystack often integrates Elasticsearch or Milvus for vector search, which isn’t free if you host it yourself or use a managed service. Plus, if you tap into commercial models like OpenAI’s GPT-4 or Cohere, API usage can add up fast. But since Haystack gives you a lot of freedom on backends and retrievers, you could optimize aggressively. Need a basic BM25 retriever? Doable without major spending.
LlamaIndex is mostly a wrapper on top of LLMs and simple indexing. Meaning, your biggest expense will be paying for API calls to OpenAI, Anthropic, or similar providers. It abstracts away vector stores but in return, you lose control over data storage costs and performance tuning. The flipside: less ops work, so time spent tinkering goes down.
Either way, the bottleneck is your LLM pricing, which can cost from a few cents per thousand tokens to much more depending on the model. If you want to keep costs low, Haystack’s ability to run local retrievers and open-source embedding models gives it the edge for thrifty hackers.
My Take: What to Pick Based on Who You Are
No two side projects are the same, so here’s my no-BS advice for three developer personas.
The Hacky Solo Dev
Fast iteration is king. You want something that gets out of your way so you can show friends or demo an idea quickly. LlamaIndex is your friend. Minimal setup, no heavy infrastructure, and you will get a prototype off the ground in an afternoon.
The Product Dev Thinking Long-Term
You want this side project to potentially turn into a real product or MVP. Haystack wins hands down. The ability to scale, swap retrievers, and the active ecosystem means you won’t need to throw everything away and start from scratch when your project grows.
The Data Nerd with Custom Data Sources
If you’re working with large or weird document collections, PDFs, databases, or want complex pipelines, Haystack is the way to go. It gives you all the knobs and handles multi-step workflows elegantly. It’s more work initially, but your data thanks you later.
FAQ
Q: Can I use Haystack without an external vector database?
Yes. Haystack includes an in-memory document store and supports other local stores like FAISS for vector search, so you can run small projects completely locally without spinning up Elasticsearch or Milvus.
Q: Does LlamaIndex support multimodal data?
Not out of the box. It’s mostly focused on text data and doesn’t have built-in pipelines for images or audio. You’d have to extend it yourself or preprocess data accordingly.
Q: Which tool supports incremental updates to the document dataset?
Haystack handles incremental document additions and deletions gracefully, making it suitable for dynamic datasets. LlamaIndex generally rebuilds the index from scratch, which can be a pain with growing data.
Q: How steep is the learning curve for each?
Haystack’s learning curve is steeper; you need to understand search pipelines, retrievers, and readers. LlamaIndex’s API is friendlier for people new to NLP or LLM-powered search.
Q: Which has better community support?
Haystack has fewer open issues and more active maintainers responding quickly. LlamaIndex’s community is larger but noisier, with unresolved bugs sometimes lingering.
Data Sources
Data as of March 23, 2026. Sources: https://github.com/deepset-ai/haystack, https://github.com/run-llama/llama_index
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