software
Chroma vs LlamaIndex 2026: Vector DB vs RAG Framework
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.
C
Chroma
Open-source vector database for embeddings and semantic search
Teams building vector search features, recommendation engines, or semantic similarity tools who want fast deployment without heavy data orchestration
Score63%
VS
L
LlamaIndex
Data framework for building RAG systems with multi-source indexing and LLM integration
Enterprise teams building document-centric RAG applications, knowledge bases, and Q&A systems that ingest diverse data types
Score63%
Quick Answer
AI SummaryChroma 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.
Our Verdict
AI-assistedChoose 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.
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C
Chroma
6.9/10
LlamaIndex
8.1/10
L
C
Choose Chroma if
Teams building vector search features, recommendation engines, or semantic similarity tools who want fast deployment without heavy data orchestration
L
Choose LlamaIndex if
Best pickEnterprise teams building document-centric RAG applications, knowledge bases, and Q&A systems that ingest diverse data types
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Key Differences at a Glance
- Primary Function:Vector database for embeddings vs Data indexing framework for RAG
- Data Source Support:✓ LlamaIndex wins(100+ connectors (PDFs, APIs, databases, web) vs Primarily vector embeddings)
- Query Simplicity:✓ Chroma wins(Semantic similarity search via API vs Complex query construction & metadata filtering)
Key Facts & Figures
81 numeric metrics compared
| Metric | Chroma | LlamaIndex | Ratio |
|---|---|---|---|
| Monthly Starting Cost(USD) | $0 (free, open-source) | — | — |
| Maximum Vector Storage(Vectors) | ~10M (single instance practical limit) | — | — |
| Maximum Vector Dimensions(dimensions) | 65,536 | — | — |
| Query Latency (p99)(milliseconds) | 50-200ms | — | — |
| Setup Time (Local Development)(Minutes) | 2-5 (pip install + Python) | — | — |
| Cost at 10M Vectors/Month(USD) | $0 (self-hosted only) | — | — |
| Starting Cost (Annual)(USD) | $0 (free) | — | — |
| Maximum Vectors at Scale(millions) | Limited to hardware (~1B) | — | — |
| Documentation Quality Score(out of 10) | 8/10 | — | — |
| Metadata Filter Complexity(operators supported) | Basic ($where) | — | — |
| Setup Time to Production(hours) | 0.1 days (2-4 hours) | — | — |
| Query Latency (1M vectors)(ms) | 10-50 ms | — | — |
| Memory Usage (10M vectors)(GB) | 3-5 GB | — | — |
| Query Latency (1M vectors, single query)(milliseconds) | 150-300ms | — | — |
| Maximum Practical Dataset Size(vectors) | ~10 million | — | — |
| Data Connectors(count) | 0 (manual) | 100+ | |
| LLM Provider Support(providers) | External (0 native) | 25+ | |
| Minimum Deployment Size(megabytes) | 50 | 200 | |
| Retrieval Strategy Types(strategies) | 1 (similarity search) | 6+ (hybrid, fusion, reranking, hierarchical, etc.) | |
| Storage Backends(backend types) | 3 (in-memory, SQLite, cloud) | 8+ (via supported vector DB integrations) | |
| Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) | ~50ms | — | — |
| GitHub Stars (as of 2026)(stars) | 10,500+ stars | — | — |
| Time to First Query(minutes) | 1-2 minutes | — | — |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | — | — |
| Number of Supported Languages(languages) | Python + JavaScript | — | — |
| Maximum Vectors Per Instance(vectors) | ~10M | — | — |
| Average Query Latency(milliseconds) | 10-50ms | — | — |
| Setup Time to First Query(minutes) | 2-5 (pip install) | — | — |
| Minimum Memory for 1M Vectors(GB) | 1-2GB | — | — |
| Setup Time (first query)(minutes) | 2-5 | — | — |
| Max Recommended Vector Count(vectors) | 1-10M (single node) | — | — |
| Maximum Vector Scale(vectors) | 10-50 million | — | — |
| Minimum Setup Time(minutes) | 2-5 minutes | — | — |
| GitHub Stars(stars) | ~11,000 | 35,000+ | |
| Setup Time (Minutes)(minutes) | 15-30 | 120-240 | |
| Supported Data Sources(count) | 12 embedding models | 100+ data connectors | |
| Query Latency (P95)(milliseconds) | 45-120 | 200-500 | |
| Maximum Embeddings(millions) | 50M (in-memory) | Unlimited (via Pinecone/Weaviate) | — |
| GitHub Stars (2026)(stars) | 12,500 | 32,000 | |
| Learning Curve (Hours)(hours) | 2-4 | 8-20 | |
| Production Deployments Reported(count) | 500+ | 2,000+ | |
| Initial Setup Time(minutes) | 2 minutes | — | — |
| Minimum Monthly Cost(USD) | $0 (open-source) | — | — |
| Production Plan Cost(USD/month) | $0 (self-hosted infrastructure only) | — | — |
| Maximum Vector Capacity(vectors) | 10M (single machine limit) | — | — |
| Maximum Vectors Per Index(vectors) | ~10 million | — | — |
| Query Latency (p50, local/optimal)(milliseconds) | 5-20ms | — | — |
| Monthly Base Cost (starter tier)(USD) | $0 (open-source) | — | — |
| Single-Vector Search Latency (1M vectors)(milliseconds) | 15-25ms | — | — |
| Maximum Supported Vector Dimensions(dimensions) | 2048 | — | — |
| Managed Cloud Cost (1M queries/month)(USD) | $50-150 | — | — |
| Query Latency (1M vectors, p99)(milliseconds) | ~350ms | — | — |
| Maximum Recommended Vectors(millions) | 50-100M | — | — |
| Setup Time (local environment)(minutes) | 2-3 minutes | — | — |
| Supported Embedding Dimensions(max dimensions) | Up to 2048 | — | — |
| Language/SDK Support(number of SDKs) | Python, JavaScript, Go | — | — |
| Setup Time (minutes to first working example)(minutes) | 3 minutes | — | — |
| Maximum Vector Capacity (single instance)(millions of vectors) | 10 million | — | — |
| Query Latency at 1M vectors(milliseconds) | 50-150ms | — | — |
| Memory per Million Vectors(GB) | 1.5-2.0 GB | — | — |
| Index Type Options(count) | 2 (SQLite, DuckDB) | — | — |
| Vector Store Integrations(count) | 35+ | 35+ | |
| Monthly NPM/PyPI Downloads(downloads) | 180,000+ | 180,000+ | |
| Documentation Pages(pages) | 500+ | 500+ | |
| Vector Database Integrations(integrations) | 20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.) | 20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.) | |
| LLM Model Providers Supported(providers) | 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.) | 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.) | |
| Average Setup Time(days) | 2-4 hours | 2-4 hours | |
| Enterprise Connectors(connectors) | 20+ (Slack, Notion, Google Workspace, etc.) | 20+ (Slack, Notion, Google Workspace, etc.) | |
| Latest Release Activity | 150+ commits/month | 150+ commits/month | |
| Pre-trained Models(models) | 100+ integrations | 100+ integrations | |
| Data Connectors/Loaders(connectors) | 200+ | 200+ | |
| Learning Curve (weeks to productivity)(weeks) | 1-2 weeks | 1-2 weeks | |
| LLM Integrations(providers) | 45+ providers | 45+ providers | |
| Vector Store Support(count) | 50+ | 50+ | |
| Enterprise Market Share(percentage) | 28% of RAG-focused projects | 28% of RAG-focused projects | |
| Setup Time for Basic RAG(minutes) | 5-10 minutes | 5-10 minutes | |
| LLM Model Integrations(count) | 70+ | 70+ | |
| Memory Types Available(count) | 3 | 3 | |
| RAG Retrieval Speed (vs baseline)(% faster) | +25-30% faster | +25-30% faster | |
| Community Discord Members(members) | 18,000+ | 18,000+ | |
| Monthly Active Commits(count) | 3,500+ | 3,500+ |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
C
2ChromaLlamaIndex leads1 tie
L
4LlamaIndexChroma
Attribute
LlamaIndex
- Vector database for embeddingsPrimary FunctionData indexing framework for RAG
- Primarily vector embeddingsData Source Support100+ connectors (PDFs, APIs, databases, web)(winner)
- Semantic similarity search via API(winner)Query SimplicityComplex query construction & metadata filtering
- Requires external LLM integrationLLM IntegrationBuilt-in LLM agent & response synthesis(winner)
- 0.5-1(winner)Setup Time (hours)2-4
- 12,000+GitHub Stars (2026)32,000+(winner)
- 500+ reportedProduction Deployments2,000+ reported(winner)
- Primary Function
Chroma
Vector database for embeddings
LlamaIndex
Data indexing framework for RAG
- Data Source Support
Chroma
Primarily vector embeddings
LlamaIndex
100+ connectors (PDFs, APIs, databases, web)(winner)
- Query Simplicity
Chroma
Semantic similarity search via API(winner)
LlamaIndex
Complex query construction & metadata filtering
- LLM Integration
Chroma
Requires external LLM integration
LlamaIndex
Built-in LLM agent & response synthesis(winner)
- Setup Time (hours)
Chroma
0.5-1(winner)
LlamaIndex
2-4
- GitHub Stars (2026)
Chroma
12,000+
LlamaIndex
32,000+(winner)
- Production Deployments
Chroma
500+ reported
LlamaIndex
2,000+ reported(winner)
Full Comparison
| Attribute | Chroma | LlamaIndex |
|---|---|---|
| Monthly Starting Cost(USD) | $0 (free, open-source) | — |
| Cost at 10M Vectors/Month(USD) | $0 (self-hosted only) | — |
| Starting Cost (Annual)(USD) | $0 (free) | — |
| Minimum Monthly Cost(USD) | $0 (open-source) | — |
| Production Plan Cost(USD/month) | $0 (self-hosted infrastructure only) | — |
Show 2 more attributesMonthly Base Cost (starter tier)(USD) $0 (open-source) — Managed Cloud Cost (1M queries/month)(USD) $50-150 — | ||
| Maximum Vector Storage(Vectors) | ~10M (single instance practical limit) | — |
| Maximum Vectors at Scale(millions) | Limited to hardware (~1B) | — |
| Maximum Practical Dataset Size(vectors) | ~10 million | — |
| Maximum Vectors Per Instance(vectors) | ~10M | — |
| Max Recommended Vector Count(vectors) | 1-10M (single node) | — |
Show 5 more attributesMaximum Embeddings(millions) 50M (in-memory) Unlimited (via Pinecone/Weaviate) Maximum Vector Capacity(vectors) 10M (single machine limit) — Maximum Vectors Per Index(vectors) ~10 million — Maximum Recommended Vectors(millions) 50-100M — Maximum Vector Capacity (single instance)(millions of vectors) 10 million — | ||
| Maximum Vector Dimensions(dimensions) | 65,536 | — |
| Metadata Filter Complexity(operators supported) | Basic ($where) | — |
| Embedded Tokenizer Support | Yes (6+ models included) | — |
| Metadata Filtering Support | Native (boolean operators) | — |
| Retrieval Strategy Types(strategies) | 1 (similarity search) | 6+ (hybrid, fusion, reranking, hierarchical, etc.)(winner) |
Show 20 more attributesStorage Backends(backend types) 3 (in-memory, SQLite, cloud) 8+ (via supported vector DB integrations) Built-in Embedding Generation Yes (OpenAI, HuggingFace, Ollama) — Supported Index Types(count) Heuristic Search Algorithm (HNSW) — Hybrid Search Support (BM25 + Vector) No — Multi-Tenancy Support Not supported — Query Filtering Support Basic metadata filters — Multi-Modal Search Text embeddings only — Hybrid Search (Vector + Keyword) No — Multi-modal Support Text only — Enterprise Features (RBAC/Multi-tenancy) No — LLM Integration Manual (requires wrapper code) Native (built-in agents) Supported Embedding Dimensions(max dimensions) Up to 2048 — Filtering Query Support(complexity level) Basic metadata matching — Built-in Embedding Model Support OpenAI, Cohere, Hugging Face, Ollama (6+ providers) — Vector Store Integrations(count) 35+ — Primary Use Case Optimization(null) RAG and retrieval-augmented systems — LLM Integrations(providers) 45+ providers — Vector Store Support(count) 50+ — LLM Model Integrations(count) 70+ — Memory Types Available(count) 3 — | ||
| Query Latency (p99)(milliseconds) | 50-200ms | — |
| Query Latency (1M vectors)(ms) | 10-50 ms | — |
| Query Latency (1M vectors, single query)(milliseconds) | 150-300ms | — |
| Minimum Deployment Size(megabytes) | 50(winner) | 200 |
| Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) | ~50ms | — |
Show 9 more attributesAverage Query Latency(milliseconds) 10-50ms — Maximum Vector Scale(vectors) 10-50 million — Query Latency (P95)(milliseconds) 45-120 200-500 Query Latency (p99) at 100M Vectors(milliseconds) Not tested (infeasible) — Query Latency (p50, local/optimal)(milliseconds) 5-20ms — Single-Vector Search Latency (1M vectors)(milliseconds) 15-25ms — Query Latency (1M vectors, p99)(milliseconds) ~350ms — Query Latency at 1M vectors(milliseconds) 50-150ms — RAG Retrieval Speed (vs baseline)(% faster) +25-30% faster — | ||
| Uptime SLA(percent) | Community-dependent (no SLA) | — |
| Uptime Guarantee(percent) | No SLA | — |
| Setup Time (Local Development)(Minutes) | 2-5 (pip install + Python) | — |
| Setup Time(minutes) | 5(winner) | 20 |
| Setup Time to First Query(minutes) | 2-5 (pip install) | — |
| Setup Time (Minutes)(minutes) | 15-30(winner) | 120-240 |
| Learning Curve (Hours)(hours) | 2-4(winner) | 8-20 |
Show 2 more attributesSetup Time (local environment)(minutes) 2-3 minutes — Setup Time for Basic RAG(minutes) 5-10 minutes — | ||
| Documentation Quality Score(out of 10) | 8/10 | — |
| Documentation Pages(pages) | 500+ | — |
| Enterprise Support Available | Yes (LlamaIndex Cloud) | — |
| Setup Time to Production(hours) | 0.1 days (2-4 hours) | — |
| Average Setup Time(days) | 2-4 hours | — |
| GPU Support | Experimental/Limited | — |
| Memory Usage (10M vectors)(GB) | 3-5 GB | — |
| Memory per Million Vectors(GB) | 1.5-2.0 GB | — |
| Data Connectors(count) | 0 (manual) | 100+(winner) |
| LLM Provider Support(providers) | External (0 native) | 25+(winner) |
| Supported Data Sources(count) | 12 embedding models | 100+ data connectors(winner) |
| REST API Support(yes/no) | No (client libraries only) | — |
| Language/SDK Support(number of SDKs) | Python, JavaScript, Go | — |
Show 1 more attributeData Connectors/Loaders(connectors) 200+ — | ||
| Production Observability(feature count) | Basic logging | Dashboard + eval framework + cost tracking |
| Kubernetes-Native Deployment | Not recommended; in-process only | — |
| Installation Complexity(steps) | 5-10 minutes (Python package) | — |
| SQL Filtering Capability | JSON metadata filters (limited) | — |
| Native SQL Support | Limited (metadata filtering only) | — |
| Open Source License(license type) | Apache 2.0 | — |
| Open-Source Availability | Yes (Apache 2.0) | — |
| GitHub Stars (as of 2026)(stars) | 10,500+ stars | — |
| Monthly NPM/PyPI Downloads(downloads) | 180,000+ | — |
| Time to First Query(minutes) | 1-2 minutes | — |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | — |
| Number of Supported Languages(languages) | Python + JavaScript | — |
| Vector Database Integrations(integrations) | 20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.) | — |
| Complex Metadata Filtering Support | Basic equality/contains only | — |
| Minimum Memory for 1M Vectors(GB) | 1-2GB | — |
| Supported Deployment Modes | In-process, SQLite, HTTP API | — |
| Minimum Setup Infrastructure | Python 3.7+; runs on laptop or serverless | — |
| API Inference Service(null) | No native inference API | — |
| Setup Time (first query)(minutes) | 2-5 | — |
| Minimum Setup Time(minutes) | 2-5 minutes | — |
| Setup Time (minutes to first working example)(minutes) | 3 minutes | — |
| Primary Language Support(count) | Python (primary), TypeScript/JavaScript | — |
| Kubernetes Support | Not native; runs as Python process | — |
| LangChain Integration Maturity | Official, first-class integration | — |
| Pre-trained Models(models) | 100+ integrations | — |
| GitHub Stars(stars) | ~11,000 | 35,000+(winner) |
| GitHub Stars (2026)(stars) | 12,500 | 32,000(winner) |
| Community Discord Members(members) | 18,000+ | — |
| Deployment Options | Embedded, Python, Serverless (SaaS beta) | — |
| Production Deployments Reported(count) | 500+ | 2,000+(winner) |
| Transformers Library Monthly Downloads(downloads) | Not tracked separately | — |
| Initial Setup Time(minutes) | 2 minutes | — |
| RBAC & Enterprise Security(yes/no) | No | — |
| Supported Vector Dimensions(dimensions) | Unlimited | — |
| Maximum Supported Vector Dimensions(dimensions) | 2048 | — |
| Relational Data Integration | No (requires external database) | — |
| LangChain Integration Native Support | Yes, official integration | — |
| Embedding Auto-Generation | Yes (Hugging Face, OpenAI, etc.) | — |
| Primary Indexing Algorithm(algorithm type) | Flat, approximate nearest neighbor | — |
| Index Type Options(count) | 2 (SQLite, DuckDB) | — |
| GPU Acceleration Support | No | — |
| License Type | MIT (open source) | — |
| LLM Model Providers Supported(providers) | 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.) | — |
| Enterprise Connectors(connectors) | 20+ (Slack, Notion, Google Workspace, etc.) | — |
| Azure/Microsoft Ecosystem Integration(integration level) | Minimal (basic Azure OpenAI support) | — |
| Latest Release Activity | 150+ commits/month | — |
| Production Observability Features(null) | Built-in logging, caching, callback handlers | — |
| Production Monitoring Tools(tool availability) | Basic logging via LlamaDebug | — |
| Learning Curve (weeks to productivity)(weeks) | 1-2 weeks | — |
| Learning Curve Complexity(1-5 scale) | 5/10 (Moderate) | — |
| RAG Pipeline Maturity(maturity level) | Purpose-built with auto-optimization | — |
| Agent Framework Maturity(maturity level) | Emerging (basic tool support) | — |
| Enterprise Market Share(percentage) | 28% of RAG-focused projects | — |
| Monthly Active Commits(count) | 3,500+ | — |
CChroma
LLlamaIndex
Monthly Starting Cost(USD)
$0 (free, open-source)
—
Cost at 10M Vectors/Month(USD)
$0 (self-hosted only)
—
Starting Cost (Annual)(USD)
$0 (free)
—
Minimum Monthly Cost(USD)
$0 (open-source)
—
Production Plan Cost(USD/month)
$0 (self-hosted infrastructure only)
—
Show 2 more attributes
Monthly Base Cost (starter tier)(USD)
$0 (open-source)
—
Managed Cloud Cost (1M queries/month)(USD)
$50-150
—
Maximum Vector Storage(Vectors)
~10M (single instance practical limit)
—
Maximum Vectors at Scale(millions)
Limited to hardware (~1B)
—
Maximum Practical Dataset Size(vectors)
~10 million
—
Maximum Vectors Per Instance(vectors)
~10M
—
Max Recommended Vector Count(vectors)
1-10M (single node)
—
Show 5 more attributes
Maximum Embeddings(millions)
50M (in-memory)
Unlimited (via Pinecone/Weaviate)
Maximum Vector Capacity(vectors)
10M (single machine limit)
—
Maximum Vectors Per Index(vectors)
~10 million
—
Maximum Recommended Vectors(millions)
50-100M
—
Maximum Vector Capacity (single instance)(millions of vectors)
10 million
—
Maximum Vector Dimensions(dimensions)
65,536
—
Metadata Filter Complexity(operators supported)
Basic ($where)
—
Embedded Tokenizer Support
Yes (6+ models included)
—
Metadata Filtering Support
Native (boolean operators)
—
Retrieval Strategy Types(strategies)
1 (similarity search)
6+ (hybrid, fusion, reranking, hierarchical, etc.)
Show 20 more attributes
Storage Backends(backend types)
3 (in-memory, SQLite, cloud)
8+ (via supported vector DB integrations)
Built-in Embedding Generation
Yes (OpenAI, HuggingFace, Ollama)
—
Supported Index Types(count)
Heuristic Search Algorithm (HNSW)
—
Hybrid Search Support (BM25 + Vector)
No
—
Multi-Tenancy Support
Not supported
—
Query Filtering Support
Basic metadata filters
—
Multi-Modal Search
Text embeddings only
—
Hybrid Search (Vector + Keyword)
No
—
Multi-modal Support
Text only
—
Enterprise Features (RBAC/Multi-tenancy)
No
—
LLM Integration
Manual (requires wrapper code)
Native (built-in agents)
Supported Embedding Dimensions(max dimensions)
Up to 2048
—
Filtering Query Support(complexity level)
Basic metadata matching
—
Built-in Embedding Model Support
OpenAI, Cohere, Hugging Face, Ollama (6+ providers)
—
Vector Store Integrations(count)
35+
—
Primary Use Case Optimization(null)
RAG and retrieval-augmented systems
—
LLM Integrations(providers)
45+ providers
—
Vector Store Support(count)
50+
—
LLM Model Integrations(count)
70+
—
Memory Types Available(count)
3
—
Query Latency (p99)(milliseconds)
50-200ms
—
Query Latency (1M vectors)(ms)
10-50 ms
—
Query Latency (1M vectors, single query)(milliseconds)
150-300ms
—
Minimum Deployment Size(megabytes)
50
200
Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds)
~50ms
—
Show 9 more attributes
Average Query Latency(milliseconds)
10-50ms
—
Maximum Vector Scale(vectors)
10-50 million
—
Query Latency (P95)(milliseconds)
45-120
200-500
Query Latency (p99) at 100M Vectors(milliseconds)
Not tested (infeasible)
—
Query Latency (p50, local/optimal)(milliseconds)
5-20ms
—
Single-Vector Search Latency (1M vectors)(milliseconds)
15-25ms
—
Query Latency (1M vectors, p99)(milliseconds)
~350ms
—
Query Latency at 1M vectors(milliseconds)
50-150ms
—
RAG Retrieval Speed (vs baseline)(% faster)
+25-30% faster
—
Uptime SLA(percent)
Community-dependent (no SLA)
—
Uptime Guarantee(percent)
No SLA
—
Setup Time (Local Development)(Minutes)
2-5 (pip install + Python)
—
Setup Time(minutes)
5
20
Setup Time to First Query(minutes)
2-5 (pip install)
—
Setup Time (Minutes)(minutes)
15-30
120-240
Learning Curve (Hours)(hours)
2-4
8-20
Show 2 more attributes
Setup Time (local environment)(minutes)
2-3 minutes
—
Setup Time for Basic RAG(minutes)
5-10 minutes
—
Documentation Quality Score(out of 10)
8/10
—
Documentation Pages(pages)
500+
—
Enterprise Support Available
Yes (LlamaIndex Cloud)
—
Setup Time to Production(hours)
0.1 days (2-4 hours)
—
Average Setup Time(days)
2-4 hours
—
GPU Support
Experimental/Limited
—
Memory Usage (10M vectors)(GB)
3-5 GB
—
Memory per Million Vectors(GB)
1.5-2.0 GB
—
Data Connectors(count)
0 (manual)
100+
LLM Provider Support(providers)
External (0 native)
25+
Supported Data Sources(count)
12 embedding models
100+ data connectors
REST API Support(yes/no)
No (client libraries only)
—
Language/SDK Support(number of SDKs)
Python, JavaScript, Go
—
Show 1 more attribute
Data Connectors/Loaders(connectors)
200+
—
Production Observability(feature count)
Basic logging
Dashboard + eval framework + cost tracking
Kubernetes-Native Deployment
Not recommended; in-process only
—
Installation Complexity(steps)
5-10 minutes (Python package)
—
SQL Filtering Capability
JSON metadata filters (limited)
—
Native SQL Support
Limited (metadata filtering only)
—
Open Source License(license type)
Apache 2.0
—
Open-Source Availability
Yes (Apache 2.0)
—
GitHub Stars (as of 2026)(stars)
10,500+ stars
—
Monthly NPM/PyPI Downloads(downloads)
180,000+
—
Time to First Query(minutes)
1-2 minutes
—
Memory Footprint (at rest, 1M vectors)(MB)
~800MB
—
Number of Supported Languages(languages)
Python + JavaScript
—
Vector Database Integrations(integrations)
20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.)
—
Complex Metadata Filtering Support
Basic equality/contains only
—
Minimum Memory for 1M Vectors(GB)
1-2GB
—
Supported Deployment Modes
In-process, SQLite, HTTP API
—
Minimum Setup Infrastructure
Python 3.7+; runs on laptop or serverless
—
API Inference Service(null)
No native inference API
—
Setup Time (first query)(minutes)
2-5
—
Minimum Setup Time(minutes)
2-5 minutes
—
Setup Time (minutes to first working example)(minutes)
3 minutes
—
Primary Language Support(count)
Python (primary), TypeScript/JavaScript
—
Kubernetes Support
Not native; runs as Python process
—
LangChain Integration Maturity
Official, first-class integration
—
Pre-trained Models(models)
100+ integrations
—
GitHub Stars(stars)
~11,000
35,000+
GitHub Stars (2026)(stars)
12,500
32,000
Community Discord Members(members)
18,000+
—
Deployment Options
Embedded, Python, Serverless (SaaS beta)
—
Production Deployments Reported(count)
500+
2,000+
Transformers Library Monthly Downloads(downloads)
Not tracked separately
—
Initial Setup Time(minutes)
2 minutes
—
RBAC & Enterprise Security(yes/no)
No
—
Supported Vector Dimensions(dimensions)
Unlimited
—
Maximum Supported Vector Dimensions(dimensions)
2048
—
Relational Data Integration
No (requires external database)
—
LangChain Integration Native Support
Yes, official integration
—
Embedding Auto-Generation
Yes (Hugging Face, OpenAI, etc.)
—
Primary Indexing Algorithm(algorithm type)
Flat, approximate nearest neighbor
—
Index Type Options(count)
2 (SQLite, DuckDB)
—
GPU Acceleration Support
No
—
License Type
MIT (open source)
—
LLM Model Providers Supported(providers)
40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.)
—
Enterprise Connectors(connectors)
20+ (Slack, Notion, Google Workspace, etc.)
—
Azure/Microsoft Ecosystem Integration(integration level)
Minimal (basic Azure OpenAI support)
—
Latest Release Activity
150+ commits/month
—
Production Observability Features(null)
Built-in logging, caching, callback handlers
—
Production Monitoring Tools(tool availability)
Basic logging via LlamaDebug
—
Learning Curve (weeks to productivity)(weeks)
1-2 weeks
—
Learning Curve Complexity(1-5 scale)
5/10 (Moderate)
—
RAG Pipeline Maturity(maturity level)
Purpose-built with auto-optimization
—
Agent Framework Maturity(maturity level)
Emerging (basic tool support)
—
Enterprise Market Share(percentage)
28% of RAG-focused projects
—
Monthly Active Commits(count)
3,500+
—
Pros & Cons
10 pros·6 cons across both
C
L
C
Chroma
+5-3
Pros
- Sub-second semantic search on millions of embeddings
- Minimal setup time (<30 minutes) with Python/HTTP interfaces
- Lightweight in-memory and persistent storage options
- Native support for 10+ embedding models (OpenAI, Hugging Face, Cohere)
- 99.2% uptime in production deployments
Cons
- Limited to vector data; cannot ingest raw documents natively
- Lacks built-in LLM integration and query synthesis
- Smaller ecosystem (12K GitHub stars vs competitors at 30K+)
L
LlamaIndex
+5-3
Pros
- Indexes 100+ data sources (PDFs, SQL, APIs, web, Notion, SharePoint)
- Automatic document chunking, metadata extraction, and multi-modal embedding
- Built-in LLM agents for multi-step reasoning and response synthesis
- Query optimization with metadata filtering and hybrid search
- 32K GitHub stars; used by 2,000+ production applications
Cons
- Steeper learning curve requiring 4+ hours to master advanced features
- Heavier memory footprint due to indexing pipeline overhead
- Query costs scale with document complexity (5-15% higher LLM token usage)
Frequently Asked Questions
5 questions
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.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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