Haystack vs LlamaIndex
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Haystack | LlamaIndex |
|---|---|---|
| Best for | Teams in regulated environments needing a declarative, YAML-based pipeline framework for production RAG with built-in evaluation and auditability. | Engineers building RAG pipelines over complex documents, especially those needing advanced PDF parsing and structured extraction. |
| Pricing | Open-source framework free under Apache 2.0; managed deepset Cloud with custom pricing for enterprise features like SSO and visual builder. | Open-source framework free under MIT; LlamaCloud usage-based with LlamaParse and LlamaExtract. |
| Setup complexity | Moderate: enforces component-based pipelines with typed connections, best understood with pipeline composition knowledge; YAML serialization eases deployment. | Low to moderate: quick to prototype with many loaders and pre-built components; more complex for multi-step workflows. |
| Strongest differentiator | Explicit, serializable pipeline model with built-in evaluation and observability, suited for production deployments in regulated settings. | 300+ data loaders and advanced document parsing (LlamaParse) for handling complex PDFs and structured data extraction. |
Haystack vs LlamaIndex: Haystack wins for teams deploying production RAG in regulated environments thanks to its declarative YAML pipeline model, built-in evaluation, and strong emphasis on observability. LlamaIndex wins for engineers needing rapid prototyping over complex documents, especially with its 300+ loaders and advanced PDF parsing via LlamaParse. Choose Haystack if you need auditability and pipeline versioning; choose LlamaIndex if your primary challenge is data ingestion and extraction from heterogeneous documents.
Open-source framework for building production-ready RAG, agents, and AI applications with explicit pipeline composition.
Visit WebsiteData framework for LLMs — ingestion, indexing, retrieval, and agents over your private data.
Visit WebsiteFeature-by-feature
Core capabilities: Haystack vs LlamaIndex
Haystack v2 enforces a component-based pipeline model with typed inputs and outputs, validated at build time. Pipelines can be serialized to YAML for version-controlled, cloud-agnostic deployment. It includes an evaluation framework with metrics like SAS, answer correctness, and RAGAS directly wired into pipelines. LlamaIndex offers multiple indexing strategies (vector, keyword, knowledge graph, tree, summary) and query engines with hybrid retrieval and routing. For 2026, Haystack's pipeline approach is stronger for teams needing reproducibility and observability, while LlamaIndex's flexibility in indexing is better for varied retrieval scenarios. Haystack wins for production-grade structured pipelines; LlamaIndex wins for flexible retrieval strategies.
AI/model approach: Haystack vs LlamaIndex
Haystack provides a standardized generator interface for conversational AI and supports multi-modal pipelines (image processing, audio transcription). Its agent implementation uses tool calling with branching/looping pipelines. LlamaIndex integrates with many LLMs and offers workflows for multi-step orchestration. In 2026, Haystack's explicit pipeline composition gives developers more control over AI behavior, while LlamaIndex's workflow system is simpler for straightforward chains. Haystack wins for sophisticated agent workflows; LlamaIndex ties for simple linear chains.
Integrations & ecosystem
Haystack integrates with 110+ services including major LLM providers, vector stores, and monitoring tools (Arize Phoenix, Arize AI). Its companion deepset Cloud provides a visual pipeline builder. LlamaIndex boasts 300+ data loaders via LlamaHub and integrates with many vector stores and databases. LlamaCloud offers LlamaParse for advanced PDF parsing and LlamaExtract for schema-based extraction. LlamaIndex's data loaders are unmatched for breadth, but Haystack's ecosystem is more curated for production environments. LlamaIndex wins for data ingestion breadth; Haystack wins for production monitoring and deployment integrations.
Performance & scale
Haystack is designed for production with YAML serialization enabling deployment without Python glue code. It supports streaming and async, and is Kubernetes-ready. LlamaIndex focuses on retrieval performance with multiple indexing strategies and hybrid retrieval. Public benchmarks are not available for either framework in the input data. As of 2026, LlamaIndex's advanced parsing (LlamaParse) can improve accuracy on complex documents, potentially boosting retrieval performance. Haystack wins for scaled deployment; LlamaIndex wins for retrieval accuracy on complex inputs.
Developer experience & workflow
Haystack requires understanding of component-based pipelines but offers a visual builder in deepset Cloud. Its evaluation framework is built-in, making it easy to assess quality. LlamaIndex is more accessible for quick prototypes with its many loaders and quick start guides. The TypeScript SDK in LlamaIndex appeals to full-stack developers. LlamaIndex wins for rapid prototyping; Haystack wins for teams that prioritize evaluation and production readiness.
Pricing compared
Haystack pricing (2026)
Haystack is open-source under Apache 2.0, free for any use. Features include the full framework, all integrations, and YAML pipeline serialization. For managed services, deepset Cloud offers custom pricing with visual pipeline builder, managed hosting, SSO, and enterprise support. The input data does not specify per-seat or usage-based pricing for deepset Cloud; interested teams must contact sales.
LlamaIndex pricing (2026)
LlamaIndex is open-source under MIT, free for any use. Features include the full Python and TypeScript framework, 300+ loaders, all retrieval strategies, and workflows. LlamaCloud offers usage-based pricing for LlamaParse (advanced PDF parsing), LlamaExtract (schema-based extraction), managed indexes, and enterprise SSO. Specific rates are not published in the input data, but usage-based implies cost scales with processing volume.
Value-per-dollar: Haystack vs LlamaIndex
Both frameworks are free for open-source use. LlamaIndex's LlamaCloud services (especially LlamaParse) can be cost-effective for teams processing many complex documents. Haystack's deepset Cloud pricing is custom, potentially higher for small teams but justified by enterprise features. For budget-conscious teams, either open-source version is free; managed costs depend on volume and required features. LlamaIndex open-source offers more data loaders out of the box; Haystack's managed tier likely suits enterprises needing compliance and SSO.
Who should pick which
- Team of 5 in a regulated industry deploying RAGPick: Haystack
Haystack's declarative YAML pipelines and built-in evaluation (RAGAS, SAS) are vital for compliance and auditability in regulated environments.
- Engineer processing thousands of PDFs with complex layoutsPick: LlamaIndex
LlamaParse offers advanced PDF parsing for tables, charts, and handwriting, combined with 300+ loaders for diverse data sources.
- Startup building a RAG chatbot over varied documentsPick: LlamaIndex
Quick prototyping with many loaders and pre-built query engines reduces time to MVP; free open-source framework fits limited budget.
- Platform team needing pipeline versioning and deployment automationPick: Haystack
YAML serialization enables Git-ops and CI/CD for pipeline changes, and deepset Cloud provides visual builder for non-coders.
Frequently Asked Questions
What is the main difference between Haystack and LlamaIndex?
Haystack enforces a component-based pipeline model with YAML serialization and built-in evaluation, while LlamaIndex offers flexible indexing strategies and 300+ data loaders. Haystack is better for production deployments requiring reproducibility; LlamaIndex is better for rapid prototyping and complex document parsing.
Which framework is better for building an AI agent?
Haystack provides explicit agent tool calling with branching and looping pipelines, making it suitable for sophisticated agent workflows. LlamaIndex's workflow system is simpler but less capable for complex agent logic.
Do Haystack and LlamaIndex have free tiers?
Both are free open-source frameworks. Haystack is Apache 2.0 licensed, LlamaIndex is MIT. Managed clouds (deepset Cloud and LlamaCloud) have paid tiers.
Which tool integrates better with Elasticsearch?
Haystack has built-in integrations with Elasticsearch and OpenSearch and is designed for classic search migration. LlamaIndex also integrates with Elasticsearch. Both are viable, but Haystack's pipeline model fits better for existing IR use cases.
Can I use these frameworks for non-RAG applications?
Haystack supports content generation with Jinja2 templates and conversational AI. LlamaIndex is retrieval-focused; for pure chat without retrieval, other tools may be better.
What is the learning curve for each framework?
LlamaIndex is more accessible for beginners due to its many loaders and simpler retrieval setup. Haystack requires understanding of typed pipeline composition but offers a visual builder in its cloud tier.
Last reviewed: May 12, 2026