2 Haystack Agencies for Document Processing

LlamaIndex offers more sophisticated retrieval strategies for document processing — HierarchicalNodeParser, SentenceWindowNodeParser, RecursiveRetriever — that preserve document structure and improve answer accuracy on complex documents. Haystack's advantage is production engineering: type-safe pipelines, validated component connections, YAML serialization, and the deepset Cloud managed option make Haystack easier to operate reliably at enterprise scale. For a research team prototyping a document Q&A system, LlamaIndex's richer retrieval toolkit provides faster iteration. For an enterprise team deploying a document processing pipeline that will handle millions of documents in production and needs to satisfy IT governance requirements, Haystack's pipeline architecture and tooling is better suited. The choice often comes down to: is the primary challenge retrieval accuracy (LlamaIndex) or production reliability and governance (Haystack)?

Haystack is among the most production-ready open-source document processing frameworks available. deepset has deployed Haystack in production at Fortune 500 companies in finance, legal, healthcare, and manufacturing — domains with complex document types, strict accuracy requirements, and governance mandates. The framework's type safety, pipeline validation, YAML serialization, and deepset Cloud monitoring make it suitable for enterprise production without the additional scaffolding that less opinionated frameworks require. Compared to LlamaIndex, Haystack requires more upfront configuration but provides stronger guarantees about pipeline behavior at runtime. Compared to LangChain, Haystack's pipeline model is more constrained but more auditable. For enterprises that have already failed with a more flexible framework due to production reliability issues, Haystack's explicit validation and serialization capabilities typically resolve the root causes.

Haystack is free and open-source. Enterprise document processing cost at scale: PDF parsing and chunking is CPU-bound with no API costs; embedding with OpenAI ada-002 runs $0.0001 per page; LLM-based metadata extraction with GPT-4o-mini costs $0.0002 per page; document store hosting on Elasticsearch managed service runs $150–$500/month for 10M+ document corpora. For an enterprise processing 100 000 pages per day: daily API costs are approximately $30 (embedding + extraction), monthly infrastructure cost is $150–$500. Total: $1 050–$1 400/month. This compares favorably to commercial document intelligence APIs — AWS Textract charges $0.015 per page for analyze-document (equivalent to $1 500/day at 100 000 pages), and Microsoft Azure AI Document Intelligence charges similar rates. deepset Cloud adds $500–$2 000/month on top of infrastructure but provides managed operations, which justifies cost for teams without dedicated ML engineering.

Haystack's document processing accuracy varies by document type and pipeline configuration. For well-structured text-heavy documents (policies, contracts, manuals), a Haystack hybrid retrieval pipeline achieves 80–90% exact match accuracy on factual extraction tasks in controlled evaluations. For PDFs with complex layouts (multi-column, mixed tables and text), accuracy depends heavily on the PDF parser configured — PDFMinerToDocument handles text extraction well but struggles with table structure; adding a table-specific parser improves structured data extraction accuracy by 20–40%. For scanned documents, Haystack integrates with AWS Textract or Azure Document Intelligence as OCR preprocessing steps; OCR quality determines the accuracy ceiling. HTML documents from internal wikis and knowledge bases are handled with high accuracy by HTMLToDocument. deepset publishes benchmark results for their enterprise customer use cases, which are available through their documentation and show consistent accuracy improvements of 15–30% over keyword search baselines across document types.