
Web search API and crawler built for AI agents
Ragie: Managed RAG API that handles end-to-end document ingestion and parsing, whereas Pinecone focuses purely on vector storage. Pinecone records 286,732 monthly visits with -31% month-over-month growth and Freemium, Paid, Contact for Pricing pricing. Ragie records 7,557 monthly visits with -26.8% growth and Freemium, Free Trial, Free pricing.

The managed context engine for agents, assistants and apps
| Dimension / Metric | ![]() | |
|---|---|---|
| Official Domain | pinecone.io | ragie.ai |
| Pricing Model | Freemium, Paid, Contact for Pricing | Freemium, Free Trial, Free |
| Monthly Visits | 286.73K (286,732) | 7.56K (7,557) |
| MoM Traffic Delta | -128.88K (-31%) | -2.77K (-26.8%) |
| Primary Market & Share | 🇺🇸 United States (30.2%) | 🇺🇸 United States (47.5%) |
| Direct Visits / mo | 118.64K | 2.99K |
| User Rating | 4.8 | 4.4 |
| Supported Platforms | Website | Website |
The managed vector database and knowledge engine for production AI
Store and query vector embeddings with automatic indexing that requires no manual tuning or algorithm selection.
Writes are acknowledged in under 100ms and become searchable within seconds, while all data is searched in parallel so speed holds at scale.
Connect agents to Pinecone through plugins for Claude Code, Cursor, Copilot, Codex, Gemini CLI, MCP, or a direct REST API with one key.
Serve compiled, governed enterprise knowledge to agents through a single query, cutting token usage and latency versus multi-step RAG loops.

The managed context engine for agents, assistants and apps
Ragie builds and combines vector, keyword and summary indexes so retrieval surfaces accurate context rather than leaning on embeddings alone.
Sync content automatically from Google Drive, Notion, Confluence, Slack and many other sources, or embed connectors so your own customers connect their data.
Text, PDFs, images, audio and video all flow through one unified pipeline, producing clean context packs regardless of the source format.
Extract structured elements including tables, forms, charts and key-value pairs from documents, with precise bounding boxes for full traceability.

Web search API and crawler built for AI agents
AI search and retrieval platform for modern apps and commerce

Free / Open-sourceTurn any document into AI-ready context with agentic parsing and extraction
Free / Open-sourceTurn any document into AI-ready context with agentic parsing and extraction

Search foundation built from embeddings, rerankers, and reader APIs

SpecialistVector-graph memory API designed for persistent AI agent context, offering higher-level agent memory features than Pinecone.
SpecialistVector-graph memory API designed for persistent AI agent context, offering higher-level agent memory features than Pinecone.

SpecialistLightning-fast open-source search engine optimized for keyword and text retrieval, but less tailored for dense vector embeddings.
SpecialistLightning-fast open-source search engine optimized for keyword and text retrieval, but less tailored for dense vector embeddings.

Fully managed Vector Lakebase and vector database for enterprise AI, built by the creators of Milvus
Fully managed Vector Lakebase and vector database for enterprise AI, built by the creators of Milvus
Free / Open-sourceOpen-source, high-performance vector search at any scale

A multimodal data warehouse that turns video, audio and documents into queryable rows

Free / Open-sourceGraph database built on object storage for agent memory and context
Free / Open-sourceGraph database built on object storage for agent memory and context
SQL-compatible vector database combining relational queries with similarity search, ideal for complex relational data workflows.
SQL-compatible vector database combining relational queries with similarity search, ideal for complex relational data workflows.

SpecialistRelevance engine combining vector search with behavioral machine learning models, optimized for recommendation systems rather than raw retrieval.
SpecialistRelevance engine combining vector search with behavioral machine learning models, optimized for recommendation systems rather than raw retrieval.