software
Weaviate vs Chroma 2026: Vector Database Comparison
Weaviate is an enterprise-focused vector database with multi-modal support and advanced filtering, while Chroma is a lightweight, developer-friendly alternative optimized for quick prototyping and small-to-medium scale deployments. Weaviate scales to billions of vectors; Chroma is better suited for projects under 10 million vectors.
W
Weaviate
Enterprise-grade vector database with advanced search capabilities, multi-tenancy, and Kubernetes-native deployment.
Enterprises, large-scale RAG systems, production AI applications requiring 100M+ vectors, multi-modal search needs, and teams with DevOps infrastructure
Score63%
VS
C
Chroma
Lightweight, open-source vector database optimized for Python developers and rapid development.
Individual developers, startups, AI researchers, rapid prototyping, small RAG applications, Jupyter-based workflows, and projects that don't require distributed systems
Score63%
Quick Answer
AI SummaryWeaviate is an enterprise-focused vector database with multi-modal support and advanced filtering, while Chroma is a lightweight, developer-friendly alternative optimized for quick prototyping and small-to-medium scale deployments. Weaviate scales to billions of vectors; Chroma is better suited for projects under 10 million vectors.
Our Verdict
AI-assistedChoose Weaviate if you need enterprise-grade scalability, multi-modal search, hybrid filtering, or plan to handle 100M+ vectors with high availability requirements. Choose Chroma if you're prototyping, building a small-to-medium RAG application, want minimal setup overhead, or prefer embedded vector search without infrastructure management.
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W
Weaviate
6.5/10
Chroma
8.5/10
C
W
Choose Weaviate if
Enterprises, large-scale RAG systems, production AI applications requiring 100M+ vectors, multi-modal search needs, and teams with DevOps infrastructure
C
Choose Chroma if
Best pickIndividual developers, startups, AI researchers, rapid prototyping, small RAG applications, Jupyter-based workflows, and projects that don't require distributed systems
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Key Differences at a Glance
- Maximum Scalability:✓ Weaviate wins(Billions of vectors vs ~10-50 million vectors (practical limit))
- Setup Complexity:✓ Chroma wins(Minimal (pip install, embed in Python) vs Moderate (Kubernetes/Docker recommended))
- Multi-modal Support:✓ Weaviate wins(Yes (text, images, audio via modules) vs Text embeddings primarily)
Key Facts & Figures
97 numeric metrics compared
| Metric | Weaviate | Chroma | Ratio |
|---|---|---|---|
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — | — |
| Time to First Query(minutes) | 30-45 minutes (self-hosted) | 1-2 minutes | |
| Maximum Vector Dimensions(dimensions) | Unlimited | 65,536 | — |
| Query Latency (p99)(milliseconds) | 50-150ms | 50-200ms | |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | — | — |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | — | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | — | — |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | — | — |
| API Query Language Support(count) | 2 (GraphQL, REST) | — | — |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | — | — |
| Maximum Collection Size(billion vectors) | 2 billion vectors | — | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | — | — |
| GitHub Community Stars(stars) | 13,000+ stars | — | — |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | — | — |
| Query Latency (95th percentile)(milliseconds) | 100-500 ms | — | — |
| Memory per 1M Vectors(GB) | 8-12 GB | — | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — | — |
| Built-in LLM Integrations(count) | 15+ providers | — | — |
| Managed Cloud Base Price (monthly)(USD) | $25/month | — | — |
| Throughput (vectors/second insert)(vectors/sec) | 5,000-10,000 | — | — |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed) | ~10M | |
| Average Query Latency(milliseconds) | 50-150ms | 10-50ms | |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | 2-5 (pip install) | |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | 1-2GB | |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | 1-10M (single node) | |
| Memory Usage (1M 768-dim vectors)(GB) | 1.2-1.5 GB | — | — |
| Query Latency (1M vectors, 10 concurrent)(ms) | 45-80 ms | — | — |
| Minimum Starting Cost (annual)(USD) | $300 (SaaS) or $0 (self-hosted) | — | — |
| Vector Index Types Supported(count) | 2 (HNSW, Flat) | — | — |
| Query API Types(count) | 3 (GraphQL, REST, Python) | — | — |
| Maximum Vector Dimension Support(dimensions) | Unlimited (tested to 4096+) | — | — |
| Production Deployments (estimated)(count) | ~500 enterprise customers | — | — |
| Maximum Vector Scale(vectors) | 1+ billion | 10-50 million | |
| Query Latency (1M vectors)(ms) | 50-200 ms | 10-50 ms | |
| Minimum Setup Time(minutes) | 30-60 minutes | 2-5 minutes | |
| GitHub Stars(stars) | ~4,000 | ~11,000 | |
| Starting Monthly Cost(USD) | $0 (self-hosted) / $50+ (managed) | — | — |
| Maximum Query Throughput(requests/second) | 2,000,000-3,000,000 | — | — |
| P99 Query Latency(milliseconds) | 50-150ms | — | — |
| Setup Time (first query)(minutes) | 30+ minutes (self-hosted) | 2-5 | |
| GitHub Stars (Community)(stars) | 9,200+ | — | — |
| Vector Indexing Algorithm Options(count) | HNSW, FLAT, IVF, PQ | — | — |
| Scalability Limit (Single Node)(million vectors) | 100+ with optimization | — | — |
| Operational Complexity (1-10 scale)(complexity score) | High (8/10) | — | — |
| Setup Time to Production(minutes) | 24-72 hours | 0.1 days (2-4 hours) | |
| Time to Production (First Query)(minutes) | 25 minutes | 7 minutes | |
| Maximum Recommended Vector Count(millions) | 500M+ vectors | ~10M vectors | |
| Minimum RAM Requirement (Single Node)(MB) | 512 MB | 64 MB | |
| Enterprise Support SLA(uptime %) | 99.5% guaranteed uptime | Community-driven, no SLA | — |
| GitHub Stars (as of 2026)(stars) | 9,500+ stars | 12,000+ stars | |
| Monthly Starting Cost(USD) | $0 (free, open-source) | $0 (free, open-source) | |
| Maximum Vector Storage(Vectors) | ~10M (single instance practical limit) | ~10M (single instance practical limit) | |
| Setup Time (Local Development)(Minutes) | 2-5 (pip install + Python) | 2-5 (pip install + Python) | |
| Cost at 10M Vectors/Month(USD) | $0 (self-hosted only) | $0 (self-hosted only) | |
| Starting Cost (Annual)(USD) | $0 (free) | $0 (free) | |
| Maximum Vectors at Scale(millions) | Limited to hardware (~1B) | Limited to hardware (~1B) | |
| Documentation Quality Score(score) | 8/10 | 8/10 | |
| Metadata Filter Complexity(operators supported) | Basic ($where) | Basic ($where) | |
| Memory Usage (10M vectors)(GB) | 3-5 GB | 3-5 GB | |
| Query Latency (1M vectors, single query)(milliseconds) | 150-300ms | 150-300ms | |
| Maximum Practical Dataset Size(vectors) | ~10 million | ~10 million | |
| Data Connectors(count) | 0 (manual) | 0 (manual) | |
| LLM Provider Support(providers) | External (0 native) | External (0 native) | |
| Minimum Deployment Size(megabytes) | 50 | 50 | |
| Retrieval Strategy Types(strategies) | 1 (similarity search) | 1 (similarity search) | |
| Storage Backends(backend types) | 3 (in-memory, SQLite, cloud) | 3 (in-memory, SQLite, cloud) | |
| Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) | ~50ms | ~50ms | |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | ~800MB | |
| Number of Supported Languages(languages) | Python + JavaScript | Python + JavaScript | |
| Setup Time (Minutes)(minutes) | 15-30 | 15-30 | |
| Supported Data Sources(count) | 12 embedding models | 12 embedding models | |
| Query Latency (P95)(milliseconds) | 45-120 | 45-120 | |
| Maximum Embeddings(millions) | 50M (in-memory) | 50M (in-memory) | |
| GitHub Stars (2026)(stars) | 12,500 | 12,500 | |
| Learning Curve (Hours)(hours) | 2-4 | 2-4 | |
| Production Deployments Reported(count) | 500+ | 500+ | |
| Initial Setup Time(minutes) | 2 minutes | 2 minutes | |
| Minimum Monthly Cost(USD) | $0 (open-source) | $0 (open-source) | |
| Production Plan Cost(USD/month) | $0 (self-hosted infrastructure only) | $0 (self-hosted infrastructure only) | |
| Maximum Vector Capacity(vectors) | 10M (single machine limit) | 10M (single machine limit) | |
| Maximum Vectors Per Index(vectors) | ~10 million | ~10 million | |
| Query Latency (p50, local/optimal)(milliseconds) | 5-20ms | 5-20ms | |
| Monthly Base Cost (starter tier)(USD) | $0 (open-source) | $0 (open-source) | |
| Single-Vector Search Latency (1M vectors)(milliseconds) | 15-25ms | 15-25ms | |
| Maximum Supported Vector Dimensions(dimensions) | 2048 | 2048 | |
| Managed Cloud Cost (1M queries/month)(USD) | $50-150 | $50-150 | |
| Query Latency (1M vectors, p99)(milliseconds) | ~350ms | ~350ms | |
| Maximum Recommended Vectors(millions) | 50-100M | 50-100M | |
| Setup Time (local environment)(minutes) | 2-3 minutes | 2-3 minutes | |
| Supported Embedding Dimensions(max dimensions) | Up to 2048 | Up to 2048 | |
| Language/SDK Support(number of SDKs) | Python, JavaScript, Go | Python, JavaScript, Go | |
| Setup Time (minutes to first working example)(minutes) | 3 minutes | 3 minutes | |
| Maximum Vector Capacity (single instance)(millions of vectors) | 10 million | 10 million | |
| Query Latency at 1M vectors(milliseconds) | 50-150ms | 50-150ms | |
| Memory per Million Vectors(GB) | 1.5-2.0 GB | 1.5-2.0 GB | |
| Index Type Options(count) | 2 (SQLite, DuckDB) | 2 (SQLite, DuckDB) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
W
4WeaviateWeaviate leads
C
3ChromaWeaviate
Attribute
Chroma
- Billions of vectors(winner)Maximum Scalability~10-50 million vectors (practical limit)
- Moderate (Kubernetes/Docker recommended)Setup ComplexityMinimal (pip install, embed in Python)(winner)
- Yes (text, images, audio via modules)(winner)Multi-modal SupportText embeddings primarily
- Yes (BM25 + vector hybrid)(winner)Hybrid Search (Vector + Keyword)Vector-only, limited filtering
- Steep (extensive API, multiple configuration options)Learning CurveShallow (simple Python API, 10 lines to start)(winner)
- RBAC, multi-tenancy, high availability(winner)Enterprise FeaturesBasic auth, single-tenant only
- ~4,000+ GitHub stars, active enterprise useCommunity Adoption~11,000+ GitHub stars, strong among startups(winner)
- Maximum Scalability
Weaviate
Billions of vectors(winner)
Chroma
~10-50 million vectors (practical limit)
- Setup Complexity
Weaviate
Moderate (Kubernetes/Docker recommended)
Chroma
Minimal (pip install, embed in Python)(winner)
- Multi-modal Support
Weaviate
Yes (text, images, audio via modules)(winner)
Chroma
Text embeddings primarily
- Hybrid Search (Vector + Keyword)
Weaviate
Yes (BM25 + vector hybrid)(winner)
Chroma
Vector-only, limited filtering
- Learning Curve
Weaviate
Steep (extensive API, multiple configuration options)
Chroma
Shallow (simple Python API, 10 lines to start)(winner)
- Enterprise Features
Weaviate
RBAC, multi-tenancy, high availability(winner)
Chroma
Basic auth, single-tenant only
- Community Adoption
Weaviate
~4,000+ GitHub stars, active enterprise use
Chroma
~11,000+ GitHub stars, strong among startups(winner)
Full Comparison
| Attribute | Weaviate | Chroma |
|---|---|---|
| Free Tier Vector Limit(vectors) | Unlimited (self-hosted) | — |
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — |
| Time to First Query(minutes) | 30-45 minutes (self-hosted) | 1-2 minutes(winner) |
| Time to Production (First Query)(minutes) | 25 minutes | 7 minutes(winner) |
| Maximum Vector Dimensions(dimensions) | Unlimited | 65,536 |
| Native Hybrid Search Support(null) | BM25 keyword + vector | — |
| Built-in Hybrid Search Support | Native BM25 + vector search | — |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | — |
| Hybrid Search Support (BM25 + Vector) | Yes | No |
Show 20 more attributesMulti-Tenancy Support Native multi-tenancy with data isolation Not supported Query Filtering Support Advanced GraphQL + WHERE clauses with boolean logic Basic metadata filters Multi-Modal Search Text, image, audio, video Text embeddings only Vector Index Types Supported(count) 2 (HNSW, Flat) — Built-in LLM Integration Yes (OpenAI, Cohere, HuggingFace, Azure) — Query API Types(count) 3 (GraphQL, REST, Python) — Hybrid Search (Vector + Keyword) Yes (BM25) No Multi-modal Support Text, image, audio via modules Text only Enterprise Features (RBAC/Multi-tenancy) Yes No Metadata Filter Complexity(operators supported) Basic ($where) — Embedded Tokenizer Support Yes (6+ models included) — Metadata Filtering Support Native (boolean operators) — Retrieval Strategy Types(strategies) 1 (similarity search) — Storage Backends(backend types) 3 (in-memory, SQLite, cloud) — Built-in Embedding Generation Yes (OpenAI, HuggingFace, Ollama) — Supported Index Types(count) Heuristic Search Algorithm (HNSW) — LLM Integration Manual (requires wrapper code) — 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) — | ||
| Query Latency (p99)(milliseconds) | 50-150ms(winner) | 50-200ms |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | — |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | — |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | — |
| Query Latency (95th percentile)(milliseconds) | 100-500 ms | — |
Show 20 more attributesThroughput (vectors/second insert)(vectors/sec) 5,000-10,000 — Average Query Latency(milliseconds) 50-150ms 10-50ms Memory Usage (1M 768-dim vectors)(GB) 1.2-1.5 GB — Query Latency (1M vectors, 10 concurrent)(ms) 45-80 ms — Maximum Vector Scale(vectors) 1+ billion 10-50 million Query Latency (1M vectors)(ms) 50-200 ms 10-50 ms Maximum Query Throughput(requests/second) 2,000,000-3,000,000 — P99 Query Latency(milliseconds) 50-150ms — Vector Indexing Algorithm Options(count) HNSW, FLAT, IVF, PQ — Scalability Limit (Single Node)(million vectors) 100+ with optimization — Uptime Guarantee(percent) No SLA — Query Latency (1M vectors, single query)(milliseconds) 150-300ms — Minimum Deployment Size(megabytes) 50 — Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) ~50ms — Query Latency (P95)(milliseconds) 45-120 — 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 — | ||
| Uptime SLA(%) | User-managed (no SLA) | Community-dependent (no SLA) |
| Deployment Model(type) | Standalone cluster (Kubernetes, Docker, Cloud) | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — |
| Built-in LLM Integrations(count) | 15+ providers | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | — |
| Licensing Cost(USD) | $0-5000+/month (SaaS) | — |
| Native Multi-tenancy Support | Yes, with built-in tenant isolation | — |
| Multi-Tenancy | Full native support with tenant isolation | Not supported |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | — |
| Maximum Collection Size(billion vectors) | 2 billion vectors | — |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed)(winner) | ~10M |
| Max Recommended Vector Count(vectors) | 100M+ (distributed)(winner) | 1-10M (single node) |
| Maximum Vector Dimension Support(dimensions) | Unlimited (tested to 4096+) | — |
Show 9 more attributesMaximum Recommended Vector Count(millions) 500M+ vectors ~10M vectors 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 Embeddings(millions) 50M (in-memory) — 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 — | ||
| API Query Language Support(count) | 2 (GraphQL, REST) | — |
| Minimum Setup Time(minutes) | 30-60 minutes | 2-5 minutes(winner) |
| Setup Time (first query)(minutes) | 30+ minutes (self-hosted) | 2-5(winner) |
| Documentation Quality Score(score) | 8/10 | — |
| Setup Time (minutes to first working example)(minutes) | 3 minutes | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | — |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | 2-5 (pip install)(winner) |
| Setup Time (Local Development)(Minutes) | 2-5 (pip install + Python) | — |
| Setup Time(minutes) | 5 | — |
| Setup Time (Minutes)(minutes) | 15-30 | — |
Show 2 more attributesLearning Curve (Hours)(hours) 2-4 — Setup Time (local environment)(minutes) 2-3 minutes — | ||
| GitHub Community Stars(stars) | 13,000+ stars | — |
| GitHub Stars(stars) | ~4,000 | ~11,000(winner) |
| GitHub Stars (Community)(stars) | 9,200+ | — |
| GitHub Stars (2026)(stars) | 12,500 | — |
| GPU Acceleration Support | Limited (planning phase) | No |
| Memory per 1M Vectors(GB) | 8-12 GB | — |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — |
| Supported Deployment Modes | Docker, Kubernetes, Cloud (AWS/GCP/Azure) | In-process, SQLite, HTTP API |
| Minimum Setup Infrastructure | Docker/Kubernetes cluster (4GB+ RAM minimum) | Python 3.7+; runs on laptop or serverless |
| Setup Time to Production(minutes) | 24-72 hours | 0.1 days (2-4 hours)(winner) |
| Managed Cloud Base Price (monthly)(USD) | $25/month | — |
| Minimum Starting Cost (annual)(USD) | $300 (SaaS) or $0 (self-hosted) | — |
| Starting Monthly Cost(USD) | $0 (self-hosted) / $50+ (managed) | — |
| Free Tier Availability | Unlimited (self-hosted) | — |
| Monthly Starting Cost(USD) | $0 (free, open-source) | — |
Show 6 more attributesCost 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) — Monthly Base Cost (starter tier)(USD) $0 (open-source) — Managed Cloud Cost (1M queries/month)(USD) $50-150 — | ||
| Multi-modal Support (native)(modalities) | 3 (text, image, audio) | — |
| Query Type Flexibility | Vector-first (GraphQL, REST) | — |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | 1-2GB(winner) |
| Kubernetes Support | Native Kubernetes-ready Helm charts | Not native; runs as Python process |
| LangChain Integration Maturity | Supported but secondary to GraphQL API | Official, first-class integration |
| Production Deployments (estimated)(count) | ~500 enterprise customers | — |
| Production Deployments Reported(count) | 500+ | — |
| Deployment Options(types) | Kubernetes, Docker, cloud (AWS/GCP/Azure) | Embedded, Python, Serverless (SaaS beta) |
| Minimum RAM Requirement (Single Node)(MB) | 512 MB | 64 MB(winner) |
| Code Customization(null) | Unlimited (open-source) | — |
| Index Type Options(count) | 2 (SQLite, DuckDB) | — |
| Operational Complexity (1-10 scale)(complexity score) | High (8/10) | — |
| Native RESTful API | Yes (REST + GraphQL) | — |
| Advanced Filtering Support | Complex WHERE clauses, nested conditions, cross-references | Basic metadata filters only |
| Open Source License | BSL 1.1 (Source-available, eventually open) | Apache 2.0 (Fully Open) |
| Open-Source Availability | Yes (Apache 2.0) | — |
| Enterprise Support SLA(uptime %) | 99.5% guaranteed uptime | Community-driven, no SLA |
| GitHub Stars (as of 2026)(stars) | 9,500+ stars | 12,000+ stars(winner) |
| 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) | — |
| LLM Provider Support(providers) | External (0 native) | — |
| Supported Data Sources(count) | 12 embedding models | — |
| REST API Support(yes/no) | No (client libraries only) | — |
| Language/SDK Support(number of SDKs) | Python, JavaScript, Go | — |
| Production Observability(feature count) | Basic logging | — |
| 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) | — |
| Number of Supported Languages(languages) | Python + JavaScript | — |
| Complex Metadata Filtering Support | Basic equality/contains only | — |
| 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 | — |
WWeaviate
CChroma
Free Tier Vector Limit(vectors)
Unlimited (self-hosted)
—
Estimated Monthly Cost (1M vectors)(USD)
$500-800 (managed)
—
Time to First Query(minutes)
30-45 minutes (self-hosted)
1-2 minutes
Time to Production (First Query)(minutes)
25 minutes
7 minutes
Maximum Vector Dimensions(dimensions)
Unlimited
65,536
Native Hybrid Search Support(null)
BM25 keyword + vector
—
Built-in Hybrid Search Support
Native BM25 + vector search
—
Number of Native LLM Integrations(integrations)
20+ LLM providers
—
Hybrid Search Support (BM25 + Vector)
Yes
No
Show 20 more attributes
Multi-Tenancy Support
Native multi-tenancy with data isolation
Not supported
Query Filtering Support
Advanced GraphQL + WHERE clauses with boolean logic
Basic metadata filters
Multi-Modal Search
Text, image, audio, video
Text embeddings only
Vector Index Types Supported(count)
2 (HNSW, Flat)
—
Built-in LLM Integration
Yes (OpenAI, Cohere, HuggingFace, Azure)
—
Query API Types(count)
3 (GraphQL, REST, Python)
—
Hybrid Search (Vector + Keyword)
Yes (BM25)
No
Multi-modal Support
Text, image, audio via modules
Text only
Enterprise Features (RBAC/Multi-tenancy)
Yes
No
Metadata Filter Complexity(operators supported)
Basic ($where)
—
Embedded Tokenizer Support
Yes (6+ models included)
—
Metadata Filtering Support
Native (boolean operators)
—
Retrieval Strategy Types(strategies)
1 (similarity search)
—
Storage Backends(backend types)
3 (in-memory, SQLite, cloud)
—
Built-in Embedding Generation
Yes (OpenAI, HuggingFace, Ollama)
—
Supported Index Types(count)
Heuristic Search Algorithm (HNSW)
—
LLM Integration
Manual (requires wrapper code)
—
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)
—
Query Latency (p99)(milliseconds)
50-150ms
50-200ms
Indexing Methods Supported(count)
3 methods (HNSW, flat, dynamic)
—
Average Query Latency (1M vectors, 384-dim)(milliseconds)
75ms
—
Query Throughput(operations per second (QPS))
100,000 QPS
—
Query Latency (95th percentile)(milliseconds)
100-500 ms
—
Show 20 more attributes
Throughput (vectors/second insert)(vectors/sec)
5,000-10,000
—
Average Query Latency(milliseconds)
50-150ms
10-50ms
Memory Usage (1M 768-dim vectors)(GB)
1.2-1.5 GB
—
Query Latency (1M vectors, 10 concurrent)(ms)
45-80 ms
—
Maximum Vector Scale(vectors)
1+ billion
10-50 million
Query Latency (1M vectors)(ms)
50-200 ms
10-50 ms
Maximum Query Throughput(requests/second)
2,000,000-3,000,000
—
P99 Query Latency(milliseconds)
50-150ms
—
Vector Indexing Algorithm Options(count)
HNSW, FLAT, IVF, PQ
—
Scalability Limit (Single Node)(million vectors)
100+ with optimization
—
Uptime Guarantee(percent)
No SLA
—
Query Latency (1M vectors, single query)(milliseconds)
150-300ms
—
Minimum Deployment Size(megabytes)
50
—
Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds)
~50ms
—
Query Latency (P95)(milliseconds)
45-120
—
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
—
Uptime SLA(%)
User-managed (no SLA)
Community-dependent (no SLA)
Deployment Model(type)
Standalone cluster (Kubernetes, Docker, Cloud)
—
Integrated LLM Providers(count)
20+ providers (OpenAI, Anthropic, Cohere, Hugging Face)
—
Built-in LLM Integrations(count)
15+ providers
—
Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD)
$800
—
Licensing Cost(USD)
$0-5000+/month (SaaS)
—
Native Multi-tenancy Support
Yes, with built-in tenant isolation
—
Multi-Tenancy
Full native support with tenant isolation
Not supported
Maximum Scalability (distributed nodes)(nodes)
100+
—
Maximum Collection Size(billion vectors)
2 billion vectors
—
Maximum Vectors Per Instance(vectors)
100M+ (distributed)
~10M
Max Recommended Vector Count(vectors)
100M+ (distributed)
1-10M (single node)
Maximum Vector Dimension Support(dimensions)
Unlimited (tested to 4096+)
—
Show 9 more attributes
Maximum Recommended Vector Count(millions)
500M+ vectors
~10M vectors
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 Embeddings(millions)
50M (in-memory)
—
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
—
API Query Language Support(count)
2 (GraphQL, REST)
—
Minimum Setup Time(minutes)
30-60 minutes
2-5 minutes
Setup Time (first query)(minutes)
30+ minutes (self-hosted)
2-5
Documentation Quality Score(score)
8/10
—
Setup Time (minutes to first working example)(minutes)
3 minutes
—
Setup Time (Cloud/Self-Hosted)(minutes)
5-10 minutes (cloud)
—
Setup Time to First Query(minutes)
30-60 (with Docker)
2-5 (pip install)
Setup Time (Local Development)(Minutes)
2-5 (pip install + Python)
—
Setup Time(minutes)
5
—
Setup Time (Minutes)(minutes)
15-30
—
Show 2 more attributes
Learning Curve (Hours)(hours)
2-4
—
Setup Time (local environment)(minutes)
2-3 minutes
—
GitHub Community Stars(stars)
13,000+ stars
—
GitHub Stars(stars)
~4,000
~11,000
GitHub Stars (Community)(stars)
9,200+
—
GitHub Stars (2026)(stars)
12,500
—
GPU Acceleration Support
Limited (planning phase)
No
Memory per 1M Vectors(GB)
8-12 GB
—
Memory Footprint (at rest, 1M vectors)(MB)
~800MB
—
Startup Time (empty instance)(seconds)
20-30 seconds
—
Supported Deployment Modes
Docker, Kubernetes, Cloud (AWS/GCP/Azure)
In-process, SQLite, HTTP API
Minimum Setup Infrastructure
Docker/Kubernetes cluster (4GB+ RAM minimum)
Python 3.7+; runs on laptop or serverless
Setup Time to Production(minutes)
24-72 hours
0.1 days (2-4 hours)
Managed Cloud Base Price (monthly)(USD)
$25/month
—
Minimum Starting Cost (annual)(USD)
$300 (SaaS) or $0 (self-hosted)
—
Starting Monthly Cost(USD)
$0 (self-hosted) / $50+ (managed)
—
Free Tier Availability
Unlimited (self-hosted)
—
Monthly Starting Cost(USD)
$0 (free, open-source)
—
Show 6 more attributes
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)
—
Monthly Base Cost (starter tier)(USD)
$0 (open-source)
—
Managed Cloud Cost (1M queries/month)(USD)
$50-150
—
Multi-modal Support (native)(modalities)
3 (text, image, audio)
—
Query Type Flexibility
Vector-first (GraphQL, REST)
—
Minimum Memory for 1M Vectors(GB)
4-8GB
1-2GB
Kubernetes Support
Native Kubernetes-ready Helm charts
Not native; runs as Python process
LangChain Integration Maturity
Supported but secondary to GraphQL API
Official, first-class integration
Production Deployments (estimated)(count)
~500 enterprise customers
—
Production Deployments Reported(count)
500+
—
Deployment Options(types)
Kubernetes, Docker, cloud (AWS/GCP/Azure)
Embedded, Python, Serverless (SaaS beta)
Minimum RAM Requirement (Single Node)(MB)
512 MB
64 MB
Code Customization(null)
Unlimited (open-source)
—
Index Type Options(count)
2 (SQLite, DuckDB)
—
Operational Complexity (1-10 scale)(complexity score)
High (8/10)
—
Native RESTful API
Yes (REST + GraphQL)
—
Advanced Filtering Support
Complex WHERE clauses, nested conditions, cross-references
Basic metadata filters only
Open Source License
BSL 1.1 (Source-available, eventually open)
Apache 2.0 (Fully Open)
Open-Source Availability
Yes (Apache 2.0)
—
Enterprise Support SLA(uptime %)
99.5% guaranteed uptime
Community-driven, no SLA
GitHub Stars (as of 2026)(stars)
9,500+ stars
12,000+ stars
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)
—
LLM Provider Support(providers)
External (0 native)
—
Supported Data Sources(count)
12 embedding models
—
REST API Support(yes/no)
No (client libraries only)
—
Language/SDK Support(number of SDKs)
Python, JavaScript, Go
—
Production Observability(feature count)
Basic logging
—
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)
—
Number of Supported Languages(languages)
Python + JavaScript
—
Complex Metadata Filtering Support
Basic equality/contains only
—
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
—
Pros & Cons
10 pros·6 cons across both
W
C
W
Weaviate
+5-3
Pros
- Scales to billions of vectors across distributed clusters
- Hybrid search combining BM25 keyword search with vector similarity
- Multi-modal AI modules for text, image, and audio embeddings
- Built-in RBAC, multi-tenancy, and replication for enterprise
- GraphQL API with fine-grained filtering and metadata search
Cons
- Requires Kubernetes or Docker for production, increasing infrastructure complexity
- Steep learning curve with extensive configuration options
- Higher operational overhead and resource consumption vs. lightweight alternatives
C
Chroma
+5-3
Pros
- Dead-simple setup: pip install + 5-line Python code to embed
- In-process and serverless options eliminate infrastructure overhead
- Excellent documentation with clear RAG examples and tutorials
- Perfect for local development, Jupyter notebooks, and MVP validation
- Minimal dependencies and sub-second query latency for small datasets
Cons
- Practical limit of 10-50M vectors; not designed for billion-scale deployments
- No hybrid search (BM25 + vector); filtering capabilities are basic
- Single-tenant only; lacks RBAC, multi-tenancy, and replication for enterprises
Frequently Asked Questions
5 questions
Chroma wins decisively. You can have a working RAG system running in your local Python environment in under 5 minutes. Weaviate requires Docker/Kubernetes setup and is overkill for prototyping. Start with Chroma; migrate to Weaviate only when you exceed 50M vectors or need enterprise features.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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