Chroma is a specialized vector database optimized for embedding storage and semantic search with simple APIs, while LlamaIndex is a comprehensive data framework that indexes diverse data sources and connects them to LLMs for retrieval-augmented generation (RAG). Chroma excels at vector operations; LlamaIndex excels at multi-source data orchestration.
Chroma vs LlamaIndex — Side-by-side comparison on A Versus B
Lightweight, open-source vector database optimized for Python developers and rapid development.
Data framework for building RAG systems with multi-source indexing and LLM integration
Primary Function. Data Source Support. Query Simplicity. LLM Integration. Setup Time (hours). GitHub Stars (2026). Production Deployments
Choose Chroma if you need a lightweight, fast vector database for embedding-only use cases with minimal setup and straightforward semantic search. Choose LlamaIndex if you're building enterprise RAG systems that require ingesting diverse data sources, complex retrieval logic, and end-to-end LLM orchestration.
Chroma: $0 (free, open-source) | LlamaIndex: N/A
Chroma: ~10M (single instance practical limit) | LlamaIndex: N/A
Chroma: Community-dependent (no SLA) | LlamaIndex: N/A
Chroma: 2-5 (pip install + Python) | LlamaIndex: N/A
Chroma: $0 (self-hosted only) | LlamaIndex: N/A
Chroma: Limited to hardware (~1B) | LlamaIndex: N/A
Chroma: Basic ($where) | LlamaIndex: N/A
Chroma: 0.1 days (2-4 hours) | LlamaIndex: N/A
Chroma: Yes (6+ models included) | LlamaIndex: N/A
Chroma: Native (boolean operators) | LlamaIndex: N/A
Chroma: Experimental/Limited | LlamaIndex: N/A
Query Latency (1M vectors, single query)
Chroma: External (0 native) | LlamaIndex: 25+
Chroma: 1 (similarity search) | LlamaIndex: 6+ (hybrid, fusion, reranking, hierarchical, etc.)
Chroma: 3 (in-memory, SQLite, cloud) | LlamaIndex: 8+ (via supported vector DB integrations)
Chroma: Basic logging | LlamaIndex: Dashboard + eval framework + cost tracking
Query Latency (1M vectors, 768-dim, 10th percentile)
Chroma: Yes (OpenAI, HuggingFace, Ollama) | LlamaIndex: N/A
Chroma: 5-10 minutes (Python package) | LlamaIndex: N/A
Chroma: JSON metadata filters (limited) | LlamaIndex: N/A
Chroma: Heuristic Search Algorithm (HNSW) | LlamaIndex: N/A
Chroma: Python + JavaScript | LlamaIndex: N/A
Chroma: Not recommended; in-process only | LlamaIndex: N/A
Chroma: Basic equality/contains only | LlamaIndex: N/A
Chroma: 2-5 (pip install) | LlamaIndex: N/A
Chroma: In-process, SQLite, HTTP API | LlamaIndex: N/A
Chroma: 1-10M (single node) | LlamaIndex: N/A
Chroma: Basic metadata filters | LlamaIndex: N/A
Chroma: Text embeddings only | LlamaIndex: N/A
Chroma: Not native; runs as Python process | LlamaIndex: N/A
Chroma: Python 3.7+; runs on laptop or serverless | LlamaIndex: N/A
Chroma: Official, first-class integration | LlamaIndex: N/A
Enterprise Features (RBAC/Multi-tenancy)
Chroma: Embedded, Python, Serverless (SaaS beta) | LlamaIndex: N/A
Chroma: 12 embedding models | LlamaIndex: 100+ data connectors
Chroma: 50M (in-memory) | LlamaIndex: Unlimited (via Pinecone/Weaviate)
Chroma: Manual (requires wrapper code) | LlamaIndex: Native (built-in agents)
Chroma: $0 (open-source) | LlamaIndex: N/A
Chroma: $0 (self-hosted infrastructure only) | LlamaIndex: N/A
Chroma: 10M (single machine limit) | LlamaIndex: N/A
Chroma: Not tested (infeasible) | LlamaIndex: N/A
Chroma: No (client libraries only) | LlamaIndex: N/A
Chroma: Yes, official integration | LlamaIndex: N/A
Single-Vector Search Latency (1M vectors)
Chroma: Limited (metadata filtering only) | LlamaIndex: N/A
Chroma: Yes (Hugging Face, OpenAI, etc.) | LlamaIndex: N/A
Chroma: Yes (Apache 2.0) | LlamaIndex: N/A
Chroma: No (requires external database) | LlamaIndex: N/A
Chroma: Basic metadata matching | LlamaIndex: N/A
Chroma: Flat, approximate nearest neighbor | LlamaIndex: N/A
Chroma: Python, JavaScript, Go | LlamaIndex: N/A
Chroma: Basic metadata filters only | LlamaIndex: N/A
Chroma: Apache 2.0 (Fully Open) | LlamaIndex: N/A
Chroma: Community-driven, no SLA | LlamaIndex: N/A
Setup Time (minutes to first working example)
Maximum Vector Capacity (single instance)
Chroma: OpenAI, Cohere, Hugging Face, Ollama (6+ providers) | LlamaIndex: N/A
Chroma: 2 (SQLite, DuckDB) | LlamaIndex: N/A
Chroma: N/A | LlamaIndex: Yes (LlamaIndex Cloud)
Chroma: N/A | LlamaIndex: MIT (open source)
Chroma: N/A | LlamaIndex: 20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.)
Chroma: N/A | LlamaIndex: 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.)
Chroma: N/A | LlamaIndex: 20+ (Slack, Notion, Google Workspace, etc.)
Chroma: N/A | LlamaIndex: Python (primary), TypeScript/JavaScript
Chroma: N/A | LlamaIndex: Minimal (basic Azure OpenAI support)
Chroma: N/A | LlamaIndex: 150+ commits/month
Chroma: N/A | LlamaIndex: 100+ integrations
Chroma: N/A | LlamaIndex: Not tracked separately
Chroma: N/A | LlamaIndex: RAG and retrieval-augmented systems
Chroma: N/A | LlamaIndex: Built-in logging, caching, callback handlers
Chroma: N/A | LlamaIndex: No native inference API
Chroma: N/A | LlamaIndex: Purpose-built with auto-optimization
Chroma: N/A | LlamaIndex: Emerging (basic tool support)
Chroma: N/A | LlamaIndex: 28% of RAG-focused projects
Chroma: N/A | LlamaIndex: Basic logging via LlamaDebug
Chroma: N/A | LlamaIndex: +25-30% faster
Chroma: N/A | LlamaIndex: 5/10 (Moderate)
How to choose between Chroma and LlamaIndex
Chroma vs LlamaIndex — Structured Comparison Data
Machine-readable comparison dataset for Chroma vs LlamaIndex. Contains 139 attribute comparisons, 5 FAQ pairs.
Chroma vs LlamaIndex — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Chroma vs LlamaIndex.
Should I use Chroma or LlamaIndex for my RAG application?
If you only have embeddings and need fast vector search, choose Chroma. If you need to ingest raw documents (PDFs, web pages, databases) and build end-to-end RAG with LLM synthesis, choose LlamaIndex. LlamaIndex can integrate Chroma as its vector store backend, so they're complementary rather than mutually exclusive.
How much faster is Chroma than LlamaIndex for semantic search?
Chroma achieves P95 latencies of 45-120ms for vector search, while LlamaIndex queries (including document retrieval and metadata filtering) typically take 200-500ms. Chroma is 2.5-5x faster for pure vector operations, but LlamaIndex's overhead enables multi-source retrieval and reasoning.
Can I use Chroma as a vector store inside LlamaIndex?
Yes. LlamaIndex supports Chroma as a vector store backend. You can use LlamaIndex's document ingestion and orchestration while delegating vector operations to Chroma for optimal performance. This is a common production pattern.
What's the cost difference between Chroma and LlamaIndex?
Both are open-source and free to use. Chroma's managed cloud service (Chroma Cloud) is in beta. LlamaIndex has no managed offering; you self-host or integrate with third-party vector databases (Pinecone, Weaviate, etc.). Costs depend on your LLM provider, not the frameworks themselves.
Which has better community support and documentation?
LlamaIndex has significantly larger community (32K stars vs 12.5K), more tutorials, and broader adoption. Chroma's documentation is more concise and easier to onboard. For beginners, Chroma is clearer; for complex use cases, LlamaIndex has more resources.
How to Choose Between Chroma and LlamaIndex
Primary Function: Chroma has Vector database for embeddings, while LlamaIndex has Data indexing framework for RAG.
Data Source Support: Chroma has Primarily vector embeddings, while LlamaIndex has 100+ connectors (PDFs, APIs, databases, web).
Query Simplicity: Chroma has Semantic similarity search via API, while LlamaIndex has Complex query construction & metadata filtering.
LLM Integration: Chroma has Requires external LLM integration, while LlamaIndex has Built-in LLM agent & response synthesis.
Setup Time (hours): Chroma has 0.5-1, while LlamaIndex has 2-4.
GitHub Stars (2026): Chroma has 12,000+, while LlamaIndex has 32,000+.
Production Deployments: Chroma has 500+ reported, while LlamaIndex has 2,000+ reported.