Vector and retrieval infrastructure
Vector databases, embeddings, and retrieval components.
7 tools. Adoption level is the effort to run it well, not a quality score. Limitations are sourced; a blank means none documented yet.
| Tool | Best for | Level | Pricing | Key documented limitation |
|---|---|---|---|---|
| Elasticsearch (AI Features) | Product teams optimizing in-app search experiences. | L3 Integrated | Free (Self-managed) + $95/mo Cloud Standard tier | None documented yet |
| LlamaIndex | Allowing LLMs to query custom/private datasets via structured indexing. | L2 Assisted | Not published | None documented yet |
| Milvus | Data engineering teams building high-performance vector search features on top of existing SQL data. | L3 Integrated | Open Source (Free) + Managed Zilliz Cloud ($0.50/hr+). | None documented yet |
| Pinecone | Marketing ops and data scientists needing to connect disparate data sources to an LLM.,description | L3 Integrated | Free Tier + Usage-based / Serverless | None documented yet |
| Unstructured.io | Data teams building RAG pipelines that need high-quality data extraction from PDFs and docs. | L3 Integrated | Open Source (Free) + Usage-based API. | None documented yet |
| Weaviate | Organizations moving from traditional search to AI-powered vector and hybrid search. | L3 Integrated | Open Source (Free) + Managed Cloud starting at $25/mo. | None documented yet |
| XetHub | Developers needing high-speed storage for multi-cloud AI training. | L2 Assisted | Free for small teams + Enterprise storage based. | None documented yet |