Chroma vs Pinecone

Chroma - the open-source data infrastructure for AI.

| | Docs | Homepage

pip install chromadb # python client
## for javascript, npm install chromadb!
## for client-server mode, chroma run --path /chroma_db_path

Chroma Cloud

Our hosted service, Chroma Cloud, powers serverless vector, hybrid, and full-text search. It's extremely fast, cost-effective, scalable and painless. Create a DB and try it out in under 30 seconds with $5 of free credits.

Get started with Chroma Cloud

API

The core API is only 4 functions (run our 💡 Google Colab):

import chromadb
## setup Chroma in-memory, for easy prototyping. Can add persistence easily!
client = chromadb.Client()

## Create collection. get_collection, get_or_create_collection, delete_collection also available!
collection = client.create_collection("all-my-documents")

## Add docs to the collection. Can also update and delete. Row-based API coming soon!
collection.add(
    documents=["This is document1", "This is document2"], # we handle tokenization, embedding, and indexing automatically. You can skip that and add your own embeddings as well
    metadatas=[{"source": "notion"}, {"source": "google-docs"}], # filter on these!
    ids=["doc1", "doc2"], # unique for each doc
)

## Query/search 2 most similar results. You can also .get by id
results = collection.query(
    query_texts=["This is a query document"],
    n_results=2,
    # where={"metadata_field": "is_equal_to_this"}, # optional filter
    # where_document={"$contains":"search_string"}  # optional filter
)

Learn about all features on our Docs

Get involved

Chroma is a rapidly developing project. We welcome PR contributors and ideas for how to improve the project.

• Join the conversation on Discord - #contributing channel
• Review the 🛣️ Roadmap and contribute your ideas
• Grab an issue and open a PR - Good first issue tag
• Read our contributing guide

Release Cadence
We currently release new tagged versions of the pypi and npm packages on Mondays. Hotfixes go out at any time during the week.

License

Apache 2.0

Overview

Pinecone is a managed vector database for retrieval, recommendations and long-term memory in AI applications. Its current platform is built around the Documents API: you write JSON documents with your own field names, and the index schema decides how each searchable field is indexed — full-text, Pinecone-generated embeddings, or dense/sparse vectors you bring. Everything else is stored as filterable metadata.

The product line now includes Pinecone Database (serverless vector DB), Nexus (a knowledge layer for agents), Dedicated Read Nodes, Assistant, BYOC and the open-source pg_pinecone Postgres extension.

Core capabilities

• Multiple search modes in one index — full-text (BM25 with Lucene syntax, stemming, stop words, n-grams), semantic, sparse-vector and hybrid search.
• Integrated embedding — upsert and query with text; Pinecone generates vectors server-side, so you do not run a separate embedding pipeline.
• Metadata filtering and reranking — narrow by metadata, then rerank for relevance.
• Namespaces — keep each tenant's data separate inside one index for multi-tenant apps.
• Serverless scaling — object-storage-based architecture that scales reads and writes without capacity planning.
• Dedicated Read Nodes — provisioned read capacity for sustained high query volumes.
• Backups and BYOC — back up indexes and deploy Pinecone in your own cloud account with zero-access networking.
• MCP server — connect MCP-compatible agents to Pinecone for search and index management.
• Assistant and Nexus — higher-level services for retrieval-augmented apps and agent knowledge.

What it is good at

Pinecone fits RAG, semantic search, recommendations, agent memory and multi-tenant retrieval where you want a managed service and predictable latency. The Documents API reduces the glue code around embeddings and lets one index serve keyword, vector and hybrid queries.

Getting started

1. Create a Pinecone account and an index; choose the schema fields you want searchable.
2. Write documents through the Documents API (text, vectors or both).
3. Query with text or vectors; add metadata filters and reranking as needed.
4. Use namespaces for tenant isolation.
5. For agents, connect the Pinecone MCP server or use an SDK integration.
6. For production, configure backups and consider BYOC or dedicated read nodes.

Pricing

Pinecone offers a free starter index and usage-based pricing after that, with Standard and Enterprise plans plus BYOC options. See pinecone.io/pricing for current rates.

Links

• Website: pinecone.io
• Product overview: pinecone.io/product
• Docs: docs.pinecone.io
• MCP server: docs.pinecone.io/guides/agents/mcp-server