Chroma is a lightweight, open-source vector database optimized for local development and embedded use cases with zero infrastructure costs, while Pinecone is a managed cloud service designed for production-scale applications with built-in redundancy, advanced filtering, and serverless scalability.
Chroma vs Pinecone — Side-by-side comparison on A Versus B
Open-source vector database for building AI applications with built-in embedding support.
Managed serverless vector database with advanced filtering and global infrastructure.
Deployment Model. Cost for 1M vectors. Production Readiness. Query Latency (p50). Metadata Filtering. Learning Curve. Data Persistence
Choose Chroma if you're building prototypes, running local RAG applications, or need zero infrastructure costs with fast development cycles. Choose Pinecone if you're deploying production systems requiring sub-50ms latency, advanced filtering capabilities, automatic scaling, and enterprise-grade reliability with SLAs.
Chroma: $0 (free, open-source) | Pinecone: $70 (minimum pod + index)
Chroma: ~10M (single instance practical limit) | Pinecone: 100M+ (unlimited with multi-pod)
Chroma: Unlimited (backend dependent) | Pinecone: 20,480
Chroma: No SLA (community support) | 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 (boolean operators) | Pinecone: N/A
Chroma: Experimental/Limited | Pinecone: N/A
Query Latency (1M vectors, single query)
Chroma: 5 minutes | Pinecone: 15 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: 2-5 (pip install) | 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: 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: Proprietary (not open-source)
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)
How to choose between Chroma and Pinecone
Chroma vs Pinecone — Structured Comparison Data
Machine-readable comparison dataset for Chroma vs Pinecone. Contains 160 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 straightforward since both use standard vector embeddings. Export vectors from Chroma (with metadata), then bulk-import into Pinecone using their REST API or Python SDK. Typical migration: 2-4 hours for 1M vectors. Vector format compatibility is 100%; metadata schema may require minor mapping.
What's the total cost of ownership for each at 10M vectors?
Chroma: $0-500/month (self-hosting servers, ~2 x m5.large EC2 instances). Pinecone: $500-1,000/month (standard tier with redundancy). Chroma's TCO increases with ops overhead (monitoring, backups, scaling management); Pinecone's is all-inclusive.
Which is better for RAG (Retrieval-Augmented Generation) applications?
For prototypes and local use: Chroma. For production RAG: Pinecone. Chroma excels in development (instant setup, zero cost); Pinecone's sub-50ms latency, metadata filtering, and 99.95% uptime are critical for user-facing RAG chatbots serving millions of requests daily.
Does Chroma support multi-node deployment?
Chroma has experimental multi-node support via Kubernetes, but it's not production-hardened. Pinecone is natively distributed across 8 global regions with built-in replication, making it the only enterprise-ready option for HA (high-availability) deployments.
Chroma: 30 minutes to understand (simple Python API). Pinecone: 2-3 hours (requires understanding API keys, index management, sparse-dense hybrid queries, quota limits). Chroma wins for rapid onboarding; Pinecone's complexity is justified by advanced features.
How to Choose Between Chroma and Pinecone
Deployment Model: Chroma has Open-source, self-hosted or in-process, while Pinecone has Fully managed cloud service (SaaS).
Cost for 1M vectors: Chroma has $0 (self-hosted), while Pinecone has $50-200/month depending on tier.
Production Readiness: Chroma has Good for prototypes, scaling requires management, while Pinecone has Enterprise-grade with 99.95% uptime SLA.
Query Latency (p50): Chroma has 50-100ms (local), 200-500ms (cloud), while Pinecone has 20-50ms globally distributed.
Metadata Filtering: Chroma has Basic filtering, limited operators, while Pinecone has Advanced boolean filters, range queries, sparse-dense hybrid.
Learning Curve: Chroma has Easier for developers, minimal setup, while Pinecone has Moderate, requires API key management and cloud concepts.
Data Persistence: Chroma has SQLite, DuckDB, or in-memory (ephemeral), while Pinecone has Multi-region replication with automatic backups.