Chroma is a developer-friendly vector database with built-in LLM integrations and minimal setup, while FAISS is a high-performance similarity search library optimized for massive-scale indexing of billions of vectors. Chroma prioritizes ease-of-use for RAG applications, while FAISS prioritizes raw speed and scalability for research and production ML workloads.
Chroma vs FAISS — Side-by-side comparison on A Versus B
Developer-friendly open-source vector database optimized for LLM and RAG applications
High-performance vector similarity search library from Meta for indexing billions of vectors
Primary Use Case. Setup Complexity. Vector Capacity (Single Instance). Query Latency at 1M vectors. Built-in LLM Integration. Memory Efficiency. Multi-tenancy Support
Choose Chroma if you're building RAG systems, LLM applications, or semantic search features where developer velocity and ease-of-use matter most—it handles up to 10M vectors efficiently with built-in embeddings support. Choose FAISS if you need to index billions of vectors, require sub-5ms query latency, or are building research infrastructure and ML pipelines where raw performance and scalability are critical.
Chroma: $0 (free, open-source) | FAISS (Facebook AI Similarity Search): N/A
Chroma: ~10M (single instance practical limit) | FAISS (Facebook AI Similarity Search): N/A
Chroma: 65,536 | FAISS (Facebook AI Similarity Search): N/A
Chroma: 50-200ms | FAISS (Facebook AI Similarity Search): N/A
Chroma: Community-dependent (no SLA) | FAISS (Facebook AI Similarity Search): N/A
Chroma: 2-5 (pip install + Python) | FAISS (Facebook AI Similarity Search): N/A
Chroma: 15,400+ | FAISS (Facebook AI Similarity Search): N/A
Chroma: $0 (self-hosted only) | FAISS (Facebook AI Similarity Search): N/A
Chroma: $0 (free) | FAISS (Facebook AI Similarity Search): N/A
Chroma: Limited to hardware (~1B) | FAISS (Facebook AI Similarity Search): N/A
Chroma: 50-200ms local | FAISS (Facebook AI Similarity Search): N/A
Chroma: No SLA | FAISS (Facebook AI Similarity Search): N/A
Chroma: 8/10 | FAISS (Facebook AI Similarity Search): N/A
Chroma: Basic ($where) | FAISS (Facebook AI Similarity Search): N/A
Chroma: 0.1 days (2-4 hours) | FAISS (Facebook AI Similarity Search): N/A
Chroma: ~10 million efficiently | FAISS (Facebook AI Similarity Search): N/A
Chroma: Yes (6+ models included) | FAISS (Facebook AI Similarity Search): N/A
Chroma: Native (boolean operators) | FAISS (Facebook AI Similarity Search): N/A
Chroma: Experimental/Limited | FAISS (Facebook AI Similarity Search): N/A
Chroma: 3-5 GB | FAISS (Facebook AI Similarity Search): N/A
Query Latency (1M vectors, single query)
Chroma: 150-300ms | FAISS (Facebook AI Similarity Search): N/A
Chroma: ~10 million | FAISS (Facebook AI Similarity Search): N/A
Chroma: 0 (manual) | FAISS (Facebook AI Similarity Search): N/A
Chroma: 5 | FAISS (Facebook AI Similarity Search): N/A
Chroma: External (0 native) | FAISS (Facebook AI Similarity Search): N/A
Chroma: 50 | FAISS (Facebook AI Similarity Search): N/A
Chroma: 1 (similarity search) | FAISS (Facebook AI Similarity Search): N/A
Chroma: 3 (in-memory, SQLite, cloud) | FAISS (Facebook AI Similarity Search): N/A
Chroma: Basic logging | FAISS (Facebook AI Similarity Search): N/A
Query Latency (1M vectors, 768-dim, 10th percentile)
Chroma: ~50ms | FAISS (Facebook AI Similarity Search): N/A
Chroma: Yes (OpenAI, HuggingFace, Ollama) | FAISS (Facebook AI Similarity Search): N/A
Chroma: 5-10 minutes (Python package) | FAISS (Facebook AI Similarity Search): N/A
Chroma: JSON metadata filters (limited) | FAISS (Facebook AI Similarity Search): N/A
Chroma: Apache 2.0 | FAISS (Facebook AI Similarity Search): N/A
Chroma: 10,500+ stars | FAISS (Facebook AI Similarity Search): 26,000+ stars
Chroma: Heuristic Search Algorithm (HNSW) | FAISS (Facebook AI Similarity Search): N/A
Chroma: 1-2 minutes | FAISS (Facebook AI Similarity Search): N/A
Chroma: ~800MB | FAISS (Facebook AI Similarity Search): N/A
Chroma: Python + JavaScript | FAISS (Facebook AI Similarity Search): N/A
Chroma: Not recommended; in-process only | FAISS (Facebook AI Similarity Search): N/A
Chroma: Basic equality/contains only | FAISS (Facebook AI Similarity Search): N/A
Chroma: ~10M | FAISS (Facebook AI Similarity Search): N/A
Chroma: 10-50ms | FAISS (Facebook AI Similarity Search): N/A
Chroma: 2-5 (pip install) | FAISS (Facebook AI Similarity Search): N/A
Chroma: No | FAISS (Facebook AI Similarity Search): N/A
Chroma: Not supported | FAISS (Facebook AI Similarity Search): N/A
Chroma: 1-2GB | FAISS (Facebook AI Similarity Search): N/A
Chroma: In-process, SQLite, HTTP API | FAISS (Facebook AI Similarity Search): N/A
Chroma: 2-5 | FAISS (Facebook AI Similarity Search): N/A
Chroma: 1-10M (single node) | FAISS (Facebook AI Similarity Search): N/A
Chroma: Basic metadata filters | FAISS (Facebook AI Similarity Search): N/A
Chroma: Text embeddings only | FAISS (Facebook AI Similarity Search): N/A
Chroma: Not native; runs as Python process | FAISS (Facebook AI Similarity Search): N/A
Chroma: Python 3.7+; runs on laptop or serverless | FAISS (Facebook AI Similarity Search): N/A
Chroma: Official, first-class integration | FAISS (Facebook AI Similarity Search): N/A
Chroma: 2 minutes | FAISS (Facebook AI Similarity Search): N/A
Chroma: $0 (open-source) | FAISS (Facebook AI Similarity Search): N/A
Chroma: $0 (self-hosted infrastructure only) | FAISS (Facebook AI Similarity Search): N/A
Chroma: 10M (single machine limit) | FAISS (Facebook AI Similarity Search): N/A
Chroma: Not tested (infeasible) | FAISS (Facebook AI Similarity Search): N/A
Chroma: No (client libraries only) | FAISS (Facebook AI Similarity Search): N/A
Chroma: 5-20ms | FAISS (Facebook AI Similarity Search): N/A
Chroma: Unlimited | FAISS (Facebook AI Similarity Search): N/A
Chroma: Yes, official integration | FAISS (Facebook AI Similarity Search): N/A
Single-Vector Search Latency (1M vectors)
Chroma: 15-25ms | FAISS (Facebook AI Similarity Search): N/A
Chroma: 2048 | FAISS (Facebook AI Similarity Search): N/A
Chroma: Limited (metadata filtering only) | FAISS (Facebook AI Similarity Search): N/A
Chroma: Yes (Hugging Face, OpenAI, etc.) | FAISS (Facebook AI Similarity Search): N/A
Chroma: Yes (Apache 2.0) | FAISS (Facebook AI Similarity Search): N/A
Chroma: $50-150 | FAISS (Facebook AI Similarity Search): N/A
Chroma: No (requires external database) | FAISS (Facebook AI Similarity Search): N/A
Chroma: ~350ms | FAISS (Facebook AI Similarity Search): N/A
Chroma: 50-100M | FAISS (Facebook AI Similarity Search): N/A
Chroma: 2-3 minutes | FAISS (Facebook AI Similarity Search): N/A
Chroma: Up to 2048 | FAISS (Facebook AI Similarity Search): N/A
Chroma: Basic metadata matching | FAISS (Facebook AI Similarity Search): N/A
Chroma: Flat, approximate nearest neighbor | FAISS (Facebook AI Similarity Search): N/A
Chroma: Python, JavaScript, Go | FAISS (Facebook AI Similarity Search): N/A
Setup Time (minutes to first working example)
Chroma: 3 minutes | FAISS (Facebook AI Similarity Search): 20 minutes
Maximum Vector Capacity (single instance)
Chroma: 10 million | FAISS (Facebook AI Similarity Search): 100,000+ million
Chroma: 50-150ms | FAISS (Facebook AI Similarity Search): 1-5ms
Chroma: 1.5-2.0 GB | FAISS (Facebook AI Similarity Search): 0.5-0.8 GB
Chroma: OpenAI, Cohere, Hugging Face, Ollama (6+ providers) | FAISS (Facebook AI Similarity Search): None - requires external service
Chroma: 2 (SQLite, DuckDB) | FAISS (Facebook AI Similarity Search): 8+ (IVF, HNSW, PQ, LSH, etc.)
Chroma: No | FAISS (Facebook AI Similarity Search): Yes - CUDA, Metal (Apple Silicon)
FAISS (Facebook AI Similarity Search) — Wikipedia
How to choose between Chroma and FAISS (Facebook AI Similarity Search)
Compare Chroma vs FAISS (Facebook AI Similarity Search)
Chroma vs FAISS — Structured Comparison Data
Machine-readable comparison dataset for Chroma vs FAISS. Contains 89 attribute comparisons, 5 FAQ pairs.
Chroma vs FAISS — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Chroma vs FAISS.
Use Chroma for RAG systems, LLM applications, and semantic search where you have <10M vectors and want minimal setup. Use FAISS if you're searching billions of vectors, need <5ms latency, or are building research infrastructure. Chroma is for product features; FAISS is for research and extreme-scale production.
Can I migrate from Chroma to FAISS or vice versa?
Yes, both store vectors as numerical arrays. You can export vectors from Chroma and import them into FAISS, but you'll need to reimplement the embedding generation and query logic. Migration is straightforward but requires rebuilding your application layer.
Chroma is cheaper for small-to-medium deployments (sub-1M vectors) due to lower infrastructure needs. FAISS becomes cheaper at massive scale (100M+ vectors) because its compression and efficiency reduce compute and storage costs. For <10M vectors, Chroma on a single instance costs ~$10-50/month; FAISS at 1B vectors on optimized hardware costs similar due to efficiency gains.
Chroma includes built-in persistence (SQLite/DuckDB) and can work standalone. FAISS is a search library only—you manage storage yourself using custom code, cloud buckets (S3), or databases. Chroma handles DevOps; FAISS requires you to build a serving layer.
Which has better documentation for beginners?
Chroma has significantly better documentation with tutorials focused on LLM use cases, RAG examples, and integrations. FAISS documentation is technical and research-oriented, assuming familiarity with vector indexing concepts. For learning, Chroma is 5x easier to onboard to.
How to Choose Between Chroma and FAISS (Facebook AI Similarity Search)
Primary Use Case: Chroma has RAG applications, LLM memory, semantic search, while FAISS (Facebook AI Similarity Search) has Large-scale similarity search, research, ML pipelines.
Setup Complexity: Chroma has Minimal - pip install, 3 lines of code to start, while FAISS (Facebook AI Similarity Search) has Moderate - requires index creation and parameter tuning.
Consider: Vector Capacity (Single Instance)
Vector Capacity (Single Instance): Chroma has Up to 10 million vectors (practical limit), while FAISS (Facebook AI Similarity Search) has 100+ billion vectors with partitioning.
Query Latency at 1M vectors: Chroma has 50-150ms average, while FAISS (Facebook AI Similarity Search) has 1-5ms average.
Built-in LLM Integration: Chroma has Yes - OpenAI, Cohere, Hugging Face native support, while FAISS (Facebook AI Similarity Search) has No - requires custom implementation.
Memory Efficiency: Chroma has 1.5-2GB per million vectors, while FAISS (Facebook AI Similarity Search) has 0.5-0.8GB per million vectors (with compression).
Multi-tenancy Support: Chroma has Limited - basic collection isolation, while FAISS (Facebook AI Similarity Search) has Advanced - enterprise-grade isolation.