Chroma is a lightweight, open-source vector database optimized for fast local development and embedding storage, while Pinecone is a fully managed cloud-native vector database designed for production-scale similarity search with advanced filtering and guaranteed uptime.
Chroma vs Pinecone — Side-by-side comparison on A Versus B
Choose Chroma if you're building prototypes, working locally, or prioritize zero infrastructure costs and open-source flexibility. Choose Pinecone if you need production-grade reliability, advanced search capabilities at scale, managed infrastructure, and enterprise SLA guarantees.
Open-source vector database optimized for rapid prototyping and lightweight deployments.
Fully managed cloud vector database with serverless architecture and enterprise features.
Structured comparison data for Chroma vs Pinecone with 180 attributes.
Query Latency (1M vectors, single query)
Query Latency (1M vectors, 768-dim, 10th percentile)
Enterprise Features (RBAC/Multi-tenancy)
Single-Vector Search Latency (1M vectors)
Setup Time (minutes to first working example)
Maximum Vector Capacity (single instance)
Monthly Cost (1M vectors, 1K queries/day)
Cost for 1M Daily Queries + 100GB Storage/Month
Chroma vs Pinecone — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Chroma vs Pinecone.
Yes, migration is possible by exporting vectors and embeddings from Chroma (via Python API) and importing them into Pinecone using their bulk upsert API. Most migrations take 1-2 hours for datasets under 100M vectors. Metadata must be reformatted to match Pinecone's schema during migration.
For development and testing RAG locally, Chroma is ideal due to zero cost and quick setup. For production RAG systems serving 100+ daily queries, Pinecone wins with its 99.95% uptime, faster retrieval (<100ms), and advanced metadata filtering for precise document filtering. Hybrid approaches (Chroma for dev, Pinecone for prod) are common.
Yes, Chroma is fully offline-capable when running locally or with persistent storage. It requires no external API calls unless you use Chroma's hosted cloud option (Chroma Cloud). Pinecone requires internet connectivity and API authentication for all operations.
What's the cost difference at 1 billion vectors?
At 1B vectors, Chroma costs $0 (self-hosted hardware costs only, ~$5-20K for servers). Pinecone costs approximately $4,000-12,000/month depending on query volume and pod type (s1/p1/p2). For long-term storage at scale, Chroma's hardware investment becomes competitive after 12-18 months.
Pinecone offers dedicated enterprise support, SLA guarantees, SOC 2 compliance, and HIPAA certification. Chroma is community-supported through GitHub issues and Discord, though commercial support partnerships exist. For enterprise requirements, Pinecone is the only option.