Pinecone is a managed cloud vector database optimized for production-scale applications with 1M+ vectors, while Chroma is a lightweight, open-source vector database designed for rapid prototyping and local development with simpler deployment.
Pinecone vs Chroma — Side-by-side comparison on A Versus B
Managed cloud vector database for production AI/ML applications at scale.
Open-source, lightweight vector database for rapid prototyping and local development.
Deployment Model. Scalability Limit. Setup Time. Cost Model. Production Readiness. Query Latency (p50). Learning Curve
Choose Pinecone if you're building production applications requiring sub-100ms latency, supporting 1B+ vectors, and need enterprise-grade reliability with managed infrastructure. Choose Chroma if you're prototyping, running locally, prioritize cost savings, or work with small-to-medium datasets (<10M vectors) where development speed matters more than managed scalability.
Pinecone: $0 (free tier limited to 1M vectors) | Chroma: N/A
Pinecone: 1 (vector-only) | Chroma: Heuristic Search Algorithm (HNSW)
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: 5-10 minutes | Chroma: 5 minutes
Pinecone: Basic payload filtering | Chroma: N/A
Pinecone: 99.95% (enterprise tier) | 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: $70 (minimum pod + index) | Chroma: $0 (free, open-source)
Pinecone: 100M+ (unlimited with multi-pod) | Chroma: ~10M (single instance practical limit)
Pinecone: 15-20 (account + API key setup) | Chroma: 2-5 (pip install + Python)
Pinecone: ~$150-200 (pod + index + compute) | Chroma: $0 (self-hosted only)
Pinecone: Metadata filtering only | Chroma: N/A
Monthly Cost (1M vectors, 1K queries/day)
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: 3-5 minutes | Chroma: 0.1 days (2-4 hours)
Pinecone: $50 (Starter tier minimum) | Chroma: $0 (free)
Pinecone: 10B+ (unlimited) | Chroma: Limited to hardware (~1B)
Pinecone: <100ms global | Chroma: 50-200ms local
Pinecone: Advanced (AND/OR/NOT) | Chroma: Basic ($where)
Pinecone: Proprietary (not open-source) | Chroma: N/A
Pinecone: Limited (SaaS constraints) | Chroma: N/A
Pinecone: 10 minutes | Chroma: 2 minutes
Pinecone: $0 (free tier with limits) | Chroma: $0 (open-source)
Pinecone: $84 (Pro plan, 5M vectors) | Chroma: $0 (self-hosted infrastructure only)
Pinecone: 1B+ (distributed) | Chroma: 10M (single machine limit)
Pinecone: < 100ms | Chroma: Not tested (infeasible)
Pinecone: Yes (REST + gRPC) | Chroma: No (client libraries only)
Pinecone: Yes (SOC 2 Type II, HIPAA) | Chroma: No
Pinecone: $4.00 + query fees | Chroma: N/A
Pinecone: SaaS only (managed) | Chroma: N/A
Pinecone: Yes (dense + BM25) | Chroma: N/A
Pinecone: Proprietary SDK + REST | Chroma: N/A
Pinecone: 100 billion | Chroma: ~10 million
Pinecone: $25-50 | Chroma: $0 (open-source)
Pinecone: 99.95% | Chroma: Community-dependent (no SLA)
Pinecone: Up to 20,000 | Chroma: Unlimited
Pinecone: Yes, official integration | Chroma: Yes, official integration
Pinecone: N/A | Chroma: ~10 million efficiently
Pinecone: N/A | Chroma: Yes (6+ models included)
Pinecone: N/A | Chroma: Native (boolean operators)
Pinecone: N/A | Chroma: Experimental/Limited
Query Latency (1M vectors, single query)
Pinecone: N/A | Chroma: External (0 native)
Pinecone: N/A | Chroma: 1 (similarity search)
Pinecone: N/A | Chroma: 3 (in-memory, SQLite, cloud)
Query Latency (1M vectors, 768-dim, 10th percentile)
Pinecone: N/A | 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: Apache 2.0 (fully open)
Pinecone: N/A | 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
How to choose between Pinecone and Chroma
Pinecone vs Chroma — Structured Comparison Data
Machine-readable comparison dataset for Pinecone vs Chroma. Contains 107 attribute comparisons, 5 FAQ pairs.
Pinecone vs Chroma — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Pinecone vs Chroma.
Which vector database should I use for my RAG application?
Use Pinecone if your RAG system ingests >1M documents and requires <100ms query latency in production. Use Chroma if you're prototyping, running locally, or have <10M vectors. Pinecone scales to enterprise datasets, while Chroma excels at rapid development and cost efficiency for smaller workloads.
Can I migrate from Chroma to Pinecone later?
Yes, migration is relatively straightforward—both use standard vector embeddings and support bulk import via APIs. Chroma's open format makes exporting data easy, and Pinecone provides bulk upsert endpoints. Plan 2-4 hours of engineering time for seamless migration of 10M+ vectors.
What's the total cost difference for 5M vectors over 12 months?
Chroma costs ~$0 (open-source, self-hosted infrastructure only). Pinecone costs approximately $1,500-3,000/year for 5M vectors depending on index size, API calls, and storage (estimated $0.25 per 100K stored vectors + compute fees). Cost break-even favors Chroma unless you value managed uptime and global infrastructure.
How do query speeds compare between Pinecone and Chroma?
Chroma achieves 5-20ms latency for local queries due to in-memory operations. Pinecone achieves 50-100ms p50 latency from global cloud infrastructure (includes network overhead). For latency-critical applications <50ms requirement, Chroma's local deployment wins; Pinecone trades latency for scalability and reliability.
Is Chroma production-ready without managed infrastructure?
Chroma can run in production on your own infrastructure (Kubernetes, Docker, etc.), but you assume operational burden—monitoring, backups, scaling, uptime. Pinecone is production-ready out-of-the-box with enterprise SLA. For mission-critical applications, Pinecone's managed service reduces operational risk.
How to Choose Between Pinecone and Chroma
Deployment Model: Pinecone has Managed cloud service (SaaS), while Chroma has Open-source, self-hosted or lightweight cloud.
Scalability Limit: Pinecone has 100B+ vectors per index, while Chroma has Up to ~10M vectors efficiently.
Setup Time: Pinecone has 15-30 minutes (API keys, cloud config), while Chroma has 2-5 minutes (pip install locally).
Cost Model: Pinecone has $0.25 per 100K vectors stored + compute, while Chroma has Free open-source, hosting costs if cloud.
Production Readiness: Pinecone has 99.95% uptime SLA, enterprise support, while Chroma has Community support, no SLA.
Query Latency (p50): Pinecone has 50-100ms (global infrastructure), while Chroma has 5-20ms (local) to 200ms+ (cloud).
Learning Curve: Pinecone has Moderate (REST API, Python SDK), while Chroma has Minimal (simple Python syntax).