Chroma is a lightweight, Python-first vector database optimized for rapid prototyping and smaller deployments, while Qdrant is a production-grade vector database designed for high-performance, large-scale applications with advanced filtering and clustering capabilities.
Chroma vs Qdrant — Side-by-side comparison on A Versus B
Lightweight, open-source vector database optimized for LLM applications and rapid prototyping.
Production-grade vector database with distributed architecture, built for high-performance similarity search at scale.
Primary Use Case. Query Performance (1M vectors). Setup Complexity. Maximum Vectors Per Shard. Filtering Capabilities. Embedding Model Support. Community & Documentation
Choose Chroma if you're building LLM applications, prototyping quickly, or need a simple drop-in vector database with minimal configuration. Choose Qdrant if you're deploying to production at scale, need sub-100ms latency, advanced filtering, or plan to process billions of vectors across distributed systems.
Chroma: $0 (free, open-source) | Qdrant: N/A
Chroma: ~10M (single instance practical limit) | Qdrant: N/A
Chroma: 65,536 | Qdrant: Unlimited (100K+ tested)
Chroma: 50-200ms | Qdrant: 20-40ms (self-hosted)
Chroma: Community-dependent (no SLA) | 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: ~10 million efficiently | Qdrant: N/A
Chroma: Yes (6+ models included) | Qdrant: N/A
Chroma: Native (boolean operators) | Qdrant: N/A
Chroma: Experimental/Limited | Qdrant: N/A
Query Latency (1M vectors, single query)
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: Apache 2.0 | Qdrant: AGPL-3.0 (with commercial license)
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
Chroma: $0 (self-hosted infrastructure only) | Qdrant: N/A
Chroma: 10M (single machine limit) | Qdrant: N/A
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: N/A | Qdrant: Self-hosted free or managed from $25/mo
Chroma: N/A | Qdrant: $25-100 (managed cloud)
Chroma: N/A | Qdrant: Advanced boolean/range queries
Chroma: N/A | Qdrant: Varies by self-hosted setup
Chroma: N/A | Qdrant: $0 (self-hosted) or $25 (managed)
Chroma: N/A | Qdrant: Self-hosted + managed cloud
Chroma: N/A | Qdrant: Yes (dense + sparse)
Chroma: N/A | Qdrant: OpenAI API compatible + REST
Chroma vs Qdrant — Structured Comparison Data
Machine-readable comparison dataset for Chroma vs Qdrant. Contains 101 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 choose for a production RAG application?
For production RAG at scale, Qdrant is recommended due to its sub-100ms latency, horizontal scaling capabilities, and advanced filtering. However, if you're deploying a smaller RAG system (under 10M embeddings) with limited budget, Chroma's simplicity and LangChain integration make it a viable choice. Production systems typically benefit from Qdrant's reliability, replication, and monitoring features.
Can Chroma handle billions of vectors like Qdrant?
No. Chroma is optimized for 50-100M vectors maximum before performance degradation becomes significant. Qdrant is architected for billion-scale deployments with distributed storage, sharding, and clustering across multiple nodes. For applications requiring billions of vectors, Qdrant is the only viable option.
On 1M vectors, Qdrant typically delivers 75ms p99 latency vs Chroma's 350ms—approximately 4-5x faster. This gap widens significantly at larger scales (10M+ vectors) where Chroma's performance degrades linearly while Qdrant maintains sub-100ms latency thanks to optimized HNSW indexing.
Is Chroma suitable for enterprise production use?
Chroma can work for smaller enterprise use cases (under 50M vectors, non-critical latency requirements), but lacks enterprise features like HA replication, horizontal scaling, and advanced SLA guarantees. For mission-critical enterprise deployments, Qdrant's distributed architecture, operational monitoring, and commercial support are better suited.
What are the total cost differences between Chroma and Qdrant?
Chroma is free and open-source with minimal infrastructure costs (can run on single CPU). Qdrant is free open-source but requires Kubernetes/multi-node infrastructure for production, increasing operational costs by $500-5,000/month depending on scale. Qdrant also offers paid cloud hosting ($99-5,000+/month) with managed infrastructure.
Primary Use Case: Chroma has Rapid prototyping, small to medium projects, LLM applications, while Qdrant has Enterprise production, large-scale deployments, complex queries.
Consider: Query Performance (1M vectors)
Query Performance (1M vectors): Chroma has ~200-500ms for complex queries, while Qdrant has ~50-100ms for complex queries.
Setup Complexity: Chroma has Minimal, pip install and run, while Qdrant has Requires Docker/Kubernetes for production.
Maximum Vectors Per Shard: Chroma has 10-100 million (limited), while Qdrant has Billions with horizontal scaling.
Filtering Capabilities: Chroma has Basic metadata filtering, while Qdrant has Advanced nested filtering, geo-filtering, range queries.
Embedding Model Support: Chroma has Native integration with OpenAI, HuggingFace, while Qdrant has Framework-agnostic, any embedding model.
Community & Documentation: Chroma has Growing community, good LLM-focused docs, while Qdrant has Mature documentation, enterprise-grade support.