Chroma is a lightweight, Python-first vector database optimized for rapid prototyping and small-to-medium datasets, while Qdrant is an enterprise-grade vector database built for production scale with superior performance at 100M+ vectors and advanced filtering capabilities.
Chroma vs Qdrant — Side-by-side comparison on A Versus B
Open-source and managed vector database designed for LLM applications with simple Python API
Enterprise-grade vector database designed for production scale with 1B+ vector capacity and advanced retrieval features
Maximum Vectors Supported. Queries Per Second (QPS) at Scale. Setup Complexity. Production Deployment Model. Hybrid Search Support. Memory Efficiency per Vector. Built-in Access Control
Choose Chroma if you're building MVP applications, doing rapid AI experimentation, or working with <50M vectors where developer speed matters more than production scale. Choose Qdrant if you need production-grade reliability, must handle 100M+ vectors efficiently, require advanced filtering and hybrid search, or are building enterprise applications with complex security requirements.
Chroma: Managed cloud + local/open-source | Qdrant: N/A
Chroma: $0 (free, open-source) | Qdrant: N/A
Chroma: ~10M (single instance practical limit) | Qdrant: N/A
Chroma: Unlimited (backend dependent) | Qdrant: Unlimited (100K+ tested)
Chroma: 50-200ms | Qdrant: 20-40ms (self-hosted)
Chroma: No SLA (community support) | Qdrant: Self-hosted (varies), Managed 99.5%
Chroma: 2-5 (pip install + Python) | Qdrant: N/A
Chroma: $0 (self-hosted only) | Qdrant: N/A
Chroma: Limited to hardware (~1B) | Qdrant: N/A
Chroma: 0.1 days (2-4 hours) | Qdrant: N/A
Chroma: Yes (6+ models included) | Qdrant: N/A
Chroma: Native (full SQL-like support) | Qdrant: N/A
Chroma: Experimental/Limited | Qdrant: N/A
Query Latency (1M vectors, single query)
Chroma: 5 minutes | Qdrant: 15-30 minutes
Chroma: External (0 native) | Qdrant: N/A
Chroma: 1 (similarity search) | Qdrant: N/A
Chroma: 3 (in-memory, SQLite, cloud) | Qdrant: N/A
Query Latency (1M vectors, 768-dim, 10th percentile)
Chroma: Yes (OpenAI, HuggingFace, Ollama) | Qdrant: N/A
Chroma: 5-10 minutes (Python package) | Qdrant: N/A
Chroma: JSON metadata filters (limited) | Qdrant: N/A
Chroma: Heuristic Search Algorithm (HNSW) | Qdrant: N/A
Chroma: 1-2 minutes | Qdrant: 20 minutes
Chroma: Python + JavaScript | Qdrant: Python, JavaScript, Go, Java, Rust, C++, .NET
Chroma: Not recommended; in-process only | Qdrant: Yes; Helm charts, StatefulSet support
Chroma: Basic equality/contains only | Qdrant: Nested fields, range, AND/OR/NOT, geo-spatial
Chroma: In-process, SQLite, HTTP API | Qdrant: N/A
Chroma: 1-10M (single node) | Qdrant: N/A
Chroma: Basic metadata filters | Qdrant: N/A
Chroma: Text embeddings only | Qdrant: N/A
Chroma: Not native; runs as Python process | Qdrant: N/A
Chroma: Python 3.7+; runs on laptop or serverless | Qdrant: N/A
Chroma: Official, first-class integration | Qdrant: N/A
Enterprise Features (RBAC/Multi-tenancy)
Chroma: Embedded, Python, Serverless (SaaS beta) | Qdrant: Self-hosted + managed cloud
Chroma: 12 embedding models | Qdrant: N/A
Chroma: Manual (requires wrapper code) | Qdrant: N/A
Chroma: $0 (self-hosted infrastructure only) | Qdrant: N/A
Chroma: 10M (single machine limit) | Qdrant: 1B+
Chroma: Not tested (infeasible) | Qdrant: N/A
Chroma: No (client libraries only) | Qdrant: N/A
Chroma: Yes, official integration | Qdrant: N/A
Single-Vector Search Latency (1M vectors)
Chroma: Limited (metadata filtering only) | Qdrant: N/A
Chroma: Yes (Hugging Face, OpenAI, etc.) | Qdrant: N/A
Chroma: No (requires external database) | Qdrant: N/A
Chroma: 50-100M | Qdrant: Unlimited (billions with clustering)
Chroma: 2-3 minutes | Qdrant: 15-20 minutes (with Docker)
Chroma: Up to 2048 | Qdrant: Up to 65536
Chroma: Basic metadata matching | Qdrant: Complex nested, geo, range, and boolean queries
Chroma: Flat, approximate nearest neighbor | Qdrant: HNSW, IVF-Flat, Product Quantization
Chroma: Python, JavaScript, Go | Qdrant: Python, JavaScript, TypeScript, Go, Rust, Java, .NET
Chroma: Basic metadata filters only | Qdrant: N/A
Chroma: Apache 2.0 (Fully Open) | Qdrant: AGPL-3.0 (with commercial license)
Chroma: Community-driven, no SLA | Qdrant: N/A
Setup Time (minutes to first working example)
Maximum Vector Capacity (single instance)
Chroma: OpenAI, Cohere, Hugging Face, Ollama (6+ providers) | Qdrant: N/A
Chroma: 2 (SQLite, DuckDB) | Qdrant: N/A
Chroma: 250ms (cloud-hosted) | Qdrant: N/A
Chroma: Basic equality/contains | Qdrant: Advanced boolean/range queries
Chroma: Python, JavaScript, Go, Rust | Qdrant: N/A
Chroma: N/A | Qdrant: Self-hosted free or managed from $25/mo
Chroma: N/A | Qdrant: $25-100 (managed cloud)
Chroma: N/A | Qdrant: Varies by self-hosted setup
Chroma: N/A | Qdrant: $0 (self-hosted) or $25 (managed)
Chroma: N/A | Qdrant: Yes (dense + sparse)
Chroma: N/A | Qdrant: OpenAI API compatible + REST
Chroma: N/A | Qdrant: BUSL-1.1 + Cloud/Enterprise
Chroma: N/A | Qdrant: Yes (BM25 included)
Chroma: N/A | Qdrant: Full RBAC + OAuth2
Chroma vs Qdrant — Structured Comparison Data
Machine-readable comparison dataset for Chroma vs Qdrant. Contains 141 attribute comparisons, 5 FAQ pairs.
Chroma vs Qdrant — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Chroma vs Qdrant.
Which vector database should I use for a startup MVP?
Chroma is the clear choice for MVPs. It installs in 5 minutes, requires no infrastructure, and works in-process or as a Python library. This lets you iterate rapidly on your AI product without DevOps overhead. Qdrant becomes relevant once you exceed 50M vectors or need production SLAs.
Can Chroma handle enterprise-scale datasets?
Chroma can manage 10-50M vectors but degrades significantly beyond that, hitting latency bottlenecks and QPS limits (500-2,000 max). Enterprise applications with 100M+ vectors should use Qdrant, which is specifically architected for scale with 10,000+ QPS capacity and sub-100ms latency.
Does Chroma support hybrid search (keyword + semantic)?
Chroma doesn't natively support hybrid search. You must integrate it with external tools like Elasticsearch or BM25 implementations. Qdrant includes native hybrid search with built-in BM25 ranking, making it production-ready for applications needing both semantic and keyword matching.
Chroma supports in-process mode (single Python process), serverless (AWS Lambda, Vercel), and managed cloud (Chroma Cloud). Qdrant requires Docker/Kubernetes for self-hosting or uses their managed cloud. For simplicity, Chroma wins; for control and enterprise integrations, Qdrant is more flexible.
Chroma uses the MIT license (fully open), while Qdrant uses BUSL-1.1 (business source license), transitioning to open after 4 years. Chroma is more permissive for commercial use without restrictions. Both have active GitHub communities, but Chroma has broader adoption in startups.
Maximum Vectors Supported: Chroma has ~10-50M vectors, while Qdrant has 1B+ vectors.
Consider: Queries Per Second (QPS) at Scale
Queries Per Second (QPS) at Scale: Chroma has 500-2,000 QPS, while Qdrant has 10,000+ QPS.
Setup Complexity: Chroma has 5 minutes (Python pip install), while Qdrant has 15-30 minutes (Docker/Kubernetes).
Production Deployment Model: Chroma has In-process, serverless, or managed cloud, while Qdrant has Self-hosted, cloud, or managed SaaS.
Hybrid Search Support: Chroma has Keyword search via integrations, while Qdrant has Native hybrid search with BM25 ranking.
Memory Efficiency per Vector: Chroma has ~100-200 bytes overhead, while Qdrant has ~50-100 bytes overhead.
Built-in Access Control: Chroma has API key only, while Qdrant has RBAC, API keys, OAuth2 support.