Chroma is an open-source, lightweight vector database designed for embedded AI applications with zero infrastructure overhead, while Pinecone is a managed cloud vector database optimized for production-scale similarity search at enterprise levels. Chroma runs locally; Pinecone is fully hosted and serverless.
Chroma vs Pinecone — Side-by-side comparison on A Versus B
Open-source vector database for embedding storage and similarity search, optimized for AI apps and local development.
Fully managed vector database as a service with enterprise SLAs and zero infrastructure overhead.
Deployment Model. Setup Time. Starting Cost. Max Vectors Supported. Query Latency at Scale. Infrastructure Management. API-First Design
Choose Chroma if you're building prototypes, demos, or small-scale AI applications (< 1M vectors) where cost is critical and you control infrastructure. Choose Pinecone if you need production-grade reliability, auto-scaling to billions of vectors, enterprise SLAs, and don't want to manage infrastructure yourself.
Chroma: $0 (free, open-source) | Pinecone: $70 (minimum pod + index)
Chroma: ~10M (single instance practical limit) | Pinecone: 100M+ (unlimited with multi-pod)
Chroma: N/A (user-managed) | Pinecone: 99.95%
Chroma: 2-5 (pip install + Python) | Pinecone: 15-20 (account + API key setup)
Chroma: ~15,000 stars (as of 2026) | Pinecone: Not open-source
Chroma: $0 (self-hosted only) | Pinecone: ~$150-200 (pod + index + compute)
Chroma: $0 (free) | Pinecone: $50 (Starter tier minimum)
Chroma: Limited to hardware (~1B) | Pinecone: 10B+ (unlimited)
Chroma: 50-200ms local | Pinecone: <100ms global
Chroma: Basic ($where) | Pinecone: Advanced (AND/OR/NOT)
Chroma: 0.1 days (2-4 hours) | Pinecone: 3-5 minutes
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 | Pinecone: N/A
Query Latency (1M vectors, single query)
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: Apache 2.0 (fully open) | Pinecone: N/A
Chroma: Heuristic Search Algorithm (HNSW) | Pinecone: 1 (vector-only)
Chroma: 5 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: 2-5 minutes | Pinecone: < 5 minutes
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 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: 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: 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: 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: Proprietary (not open-source)
Chroma: N/A | Pinecone: Limited (SaaS constraints)
Chroma: N/A | 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
How to choose between Chroma and Pinecone
Chroma vs Pinecone — Structured Comparison Data
Machine-readable comparison dataset for Chroma vs Pinecone. Contains 102 attribute comparisons, 5 FAQ pairs.
Chroma vs Pinecone — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Chroma vs Pinecone.
Can I use Chroma for production applications?
Yes, Chroma can be used in production for small to medium workloads (< 1M vectors) where you control infrastructure and accept single-machine limitations. However, for applications expecting high throughput, auto-scaling, or 24/7 uptime SLAs, Pinecone is safer. Many startups run Chroma in production on a single server until they scale beyond its limits.
Chroma is limited by your machine's RAM and disk; practically maxes out around 10M vectors on a standard server. Pinecone stores 1B+ vectors across distributed infrastructure with built-in replication. For reference, 1M vectors at 1536 dimensions consumes ~6GB of RAM in Chroma.
Pinecone is faster at scale: < 100ms p99 latency even at 100M+ vectors. Chroma latency depends on your hardware; on a single machine with 1M vectors, expect 50-200ms. However, Chroma can be faster for small datasets (< 100K vectors) on local machines due to no network overhead.
Can I migrate from Chroma to Pinecone later?
Yes, migration is straightforward: export vectors from Chroma, transform to Pinecone's format, and bulk-upsert via API. Both use standard vector embedding formats. However, metadata schemas may differ; plan 1-2 weeks for large migrations (> 50M vectors). Pinecone provides migration guides.
Do I need to choose one? Can I use both?
Many teams use Chroma for local development/testing and Pinecone for production. This hybrid approach lets you prototype cheaply, then scale to production without rewriting integrations. Both have similar Python APIs, making the transition straightforward.
How to Choose Between Chroma and Pinecone
Deployment Model: Chroma has Open-source, self-hosted or in-process, while Pinecone has Fully managed SaaS cloud platform.
Setup Time: Chroma has < 5 minutes (pip install), while Pinecone has 5-15 minutes (API key + configuration).
Starting Cost: Chroma has $0 (open-source, self-hosted), while Pinecone has $0-$84/month (starter plan).
Max Vectors Supported: Chroma has Limited by local hardware (typically < 10M), while Pinecone has 1B+ vectors across indexes.
Query Latency at Scale: Chroma has 100-500ms (single machine, 1M vectors), while Pinecone has < 100ms (p99 latency, 100M+ vectors).
Infrastructure Management: Chroma has User responsible (scaling, updates, backups), while Pinecone has Fully managed by Pinecone.
API-First Design: Chroma has Python/JavaScript client libraries; no REST API in free tier, while Pinecone has Full REST + gRPC API; multi-language SDKs.