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
Open-source vector database optimized for rapid prototyping and lightweight deployments.
Fully managed cloud vector database with serverless architecture and enterprise features.
Deployment Model. Maximum Vectors Supported. Uptime SLA. Setup Complexity. Free Tier Cost. Metadata Filtering. Use Case Maturity
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.
Chroma: 0.1-1B (managed) | Pinecone: N/A
Chroma: Managed cloud + local/open-source | Pinecone: N/A
Chroma: $0 (free, open-source) | Pinecone: $70 (minimum pod + index)
Chroma: ~10M (single instance practical limit) | Pinecone: 100M+ (unlimited with multi-pod)
Chroma: None (community) | Pinecone: 99.95%
Chroma: 2-5 (pip install + Python) | Pinecone: 15-20 (account + API key setup)
Chroma: $0 (self-hosted only) | Pinecone: ~$150-200 (pod + index + compute)
Chroma: $0 (free) | Pinecone: $50 (Starter tier minimum)
Chroma: Limited to hardware (~1B) | Pinecone: 10B+ (unlimited)
Chroma: Basic ($where) | Pinecone: Advanced (AND/OR/NOT)
Chroma: 0.1 days (2-4 hours) | Pinecone: 3-5 minutes
Chroma: Yes (6+ models included) | Pinecone: N/A
Chroma: Native (full SQL-like support) | Pinecone: Native, advanced filtering on metadata
Chroma: Experimental/Limited | Pinecone: N/A
Query Latency (1M vectors, single query)
Chroma: 5 minutes | Pinecone: <5 minutes
Chroma: External (0 native) | Pinecone: N/A
Chroma: 1 (similarity search) | Pinecone: N/A
Chroma: 3 (in-memory, SQLite, cloud) | Pinecone: N/A
Query Latency (1M vectors, 768-dim, 10th percentile)
Chroma: Yes (OpenAI, HuggingFace, Ollama) | Pinecone: N/A
Chroma: 5-10 minutes (Python package) | Pinecone: N/A
Chroma: JSON metadata filters (limited) | Pinecone: N/A
Chroma: Heuristic Search Algorithm (HNSW) | Pinecone: 3 (pod, serverless, custom)
Chroma: 1-2 minutes | Pinecone: 5-10 minutes
Chroma: Python + JavaScript | Pinecone: N/A
Chroma: Not recommended; in-process only | Pinecone: N/A
Chroma: Basic equality/contains only | Pinecone: N/A
Chroma: In-process, SQLite, HTTP API | Pinecone: N/A
Chroma: 1-10M (single node) | Pinecone: N/A
Chroma: Basic metadata filters | Pinecone: N/A
Chroma: Text embeddings only | Pinecone: N/A
Chroma: Not native; runs as Python process | Pinecone: N/A
Chroma: Python 3.7+; runs on laptop or serverless | Pinecone: N/A
Chroma: Official, first-class integration | Pinecone: N/A
Chroma: 2-5 minutes | Pinecone: 15-30 minutes
Enterprise Features (RBAC/Multi-tenancy)
Chroma: 12,500+ | Pinecone: Not public (proprietary)
Chroma: Embedded, Python, Serverless (SaaS beta) | Pinecone: SaaS only (managed)
Chroma: 12 embedding models | Pinecone: N/A
Chroma: 45-120 | Pinecone: <100ms global
Chroma: Manual (requires wrapper code) | Pinecone: N/A
Chroma: 2 minutes | Pinecone: 10 minutes
Chroma: $0 (open-source) | Pinecone: $0 (free tier with limits)
Chroma: $0 (self-hosted infrastructure only) | Pinecone: $84 (Pro plan, 5M vectors)
Chroma: 10M (single machine limit) | Pinecone: 1B+ (distributed)
Chroma: Not tested (infeasible) | Pinecone: < 100ms
Chroma: No (client libraries only) | Pinecone: Yes (REST + gRPC)
Chroma: No | Pinecone: Yes (SOC 2 Type II, HIPAA)
Chroma: ~10 million | Pinecone: 100 billion
Chroma: $0 (open-source) | Pinecone: $25-50
Chroma: Unlimited | Pinecone: Up to 20,000
Chroma: Yes, official integration | Pinecone: Yes, official integration
Single-Vector Search Latency (1M vectors)
Chroma: Limited (metadata filtering only) | Pinecone: N/A
Chroma: Yes (Hugging Face, OpenAI, etc.) | Pinecone: N/A
Chroma: Yes (Apache 2.0) | Pinecone: N/A
Chroma: No (requires external database) | Pinecone: N/A
Chroma: Basic metadata matching | Pinecone: N/A
Chroma: Flat, approximate nearest neighbor | Pinecone: N/A
Chroma: Python, JavaScript, Go | Pinecone: N/A
Chroma: Basic metadata filters only | Pinecone: N/A
Chroma: Apache 2.0 (Fully Open) | Pinecone: N/A
Chroma: Community-driven, no SLA | Pinecone: N/A
Setup Time (minutes to first working example)
Maximum Vector Capacity (single instance)
Chroma: OpenAI, Cohere, Hugging Face, Ollama (6+ providers) | Pinecone: N/A
Chroma: 2 (SQLite, DuckDB) | Pinecone: N/A
Chroma: 250ms (cloud-hosted) | Pinecone: 25ms
Chroma: Basic equality/contains | Pinecone: Boolean operators, ranges, sparse-dense hybrid
Chroma: Python, JavaScript, Go, Rust | Pinecone: Python, JavaScript, Go, Java, REST API
Chroma: 2-3 minutes | Pinecone: 5-10 minutes
Chroma: No (metadata filters only) | Pinecone: N/A
Chroma: $0 (self-hosted) | Pinecone: $150-300
Chroma: N/A | Pinecone: $0 (free tier limited to 1M vectors)
Chroma: N/A | Pinecone: 0 (standalone database)
Chroma: N/A | Pinecone: Pay-per-usage (storage + queries)
Chroma: N/A | Pinecone: $200-400 (managed pricing)
Chroma: N/A | Pinecone: Up to 20,000 dimensions
Chroma: N/A | Pinecone: No (separate system)
Chroma: N/A | Pinecone: ~2,500 (closed-source)
Chroma: N/A | Pinecone: Metadata filtering only
Monthly Cost (1M vectors, 1K queries/day)
Chroma: N/A | Pinecone: 5 billion (enterprise)
Chroma: N/A | Pinecone: 25+ (LangChain, LlamaIndex, OpenAI)
Chroma: N/A | Pinecone: Limited; JSON export only, subject to egress costs
Chroma: N/A | Pinecone: 100,000 free vectors
Chroma: N/A | Pinecone: Limited (SaaS constraints)
Chroma: N/A | Pinecone: $4.00 + query fees
Chroma: N/A | Pinecone: Yes (dense + BM25)
Chroma: N/A | Pinecone: Proprietary SDK + REST
Chroma: N/A | Pinecone: 6+ languages (Python, Node.js, Go, Java, Rust, gRPC)
Chroma: N/A | Pinecone: SOC 2 Type II, HIPAA-ready, GDPR compliant
Chroma: N/A | Pinecone: Yes (sparse-dense vectors)
Chroma: N/A | Pinecone: Proprietary optimized (HNSW variant)
Chroma: N/A | Pinecone: None (fully managed)
Cost for 1M Daily Queries + 100GB Storage/Month
Chroma: N/A | Pinecone: 20,000 dimensions
How to choose between Chroma and Pinecone
Chroma vs Pinecone — Structured Comparison Data
Machine-readable comparison dataset for Chroma vs Pinecone. Contains 180 attribute comparisons, 5 FAQ pairs.
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.
How to Choose Between Chroma and Pinecone
Deployment Model: Chroma has Open-source, self-hosted or in-memory, while Pinecone has Managed SaaS cloud platform.
Maximum Vectors Supported: Chroma has Unlimited (limited by hardware), while Pinecone has Millions to billions depending on tier.
Uptime SLA: Chroma has No SLA guarantee, while Pinecone has 99.95% uptime SLA.
Setup Complexity: Chroma has Minutes for local setup, while Pinecone has Minutes for cloud setup, no infrastructure management.
Free Tier Cost: Chroma has Free (open-source), while Pinecone has $0 starter tier with 100K vectors.
Metadata Filtering: Chroma has Basic filtering with $where clauses, while Pinecone has Advanced filtering with complex boolean logic.
Use Case Maturity: Chroma has Best for prototyping and small projects, while Pinecone has Production-ready for enterprise applications.