software
Chroma vs Weaviate 2026: Vector DB Comparison
Chroma is a lightweight, Python-first vector database optimized for rapid prototyping and small-to-medium projects, while Weaviate is a more feature-rich, enterprise-grade vector database with advanced filtering, multi-tenancy, and production-scale capabilities.
C
Chroma
Lightweight, open-source vector database optimized for Python developers and rapid development.
ML engineers, researchers, startups, and teams building proof-of-concepts with datasets under 10M vectors.
Score63%
VS
W
Weaviate
Enterprise-grade vector database with advanced search capabilities, multi-tenancy, and Kubernetes-native deployment.
Enterprise teams, SaaS platforms, large-scale search applications, and organizations requiring multi-tenant isolation and compliance.
Score63%
Quick Answer
AI SummaryChroma is a lightweight, Python-first vector database optimized for rapid prototyping and small-to-medium projects, while Weaviate is a more feature-rich, enterprise-grade vector database with advanced filtering, multi-tenancy, and production-scale capabilities.
Our Verdict
AI-assistedChoose Chroma if you're building prototypes, learning RAG systems, or need a lightweight embedded vector store that gets you working in minutes. Choose Weaviate if you're deploying production search infrastructure, need multi-tenancy, advanced filtering, or require enterprise-grade support and scalability for 100M+ vectors.
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C
Chroma
8.5/10
Weaviate
6.5/10
W
C
Choose Chroma if
Best pickML engineers, researchers, startups, and teams building proof-of-concepts with datasets under 10M vectors.
W
Choose Weaviate if
Enterprise teams, SaaS platforms, large-scale search applications, and organizations requiring multi-tenant isolation and compliance.
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Key Differences at a Glance
- Primary Use Case:✓ Weaviate wins(Enterprise search, semantic search at scale, multi-tenant deployments vs Rapid prototyping, ML pipelines, RAG applications)
- Deployment Options:✓ Weaviate wins(Kubernetes, managed cloud, hybrid cloud, on-premises clusters vs In-process (embedded), Docker, minimal infrastructure)
- Setup Complexity (Time to First Vector):✓ Chroma wins(5-10 minutes with Python SDK vs 20-30 minutes with configuration requirements)
Key Facts & Figures
97 numeric metrics compared
| Metric | Chroma | Weaviate | Ratio |
|---|---|---|---|
| Monthly Starting Cost(USD) | $0 (free, open-source) | — | — |
| Maximum Vector Storage(Vectors) | ~10M (single instance practical limit) | — | — |
| Maximum Vector Dimensions(dimensions) | 65,536 | Unlimited | — |
| Query Latency (p99)(milliseconds) | 50-200ms | 50-150ms | |
| Setup Time (Local Development)(Minutes) | 2-5 (pip install + Python) | — | — |
| Cost at 10M Vectors/Month(USD) | $0 (self-hosted only) | — | — |
| Starting Cost (Annual)(USD) | $0 (free) | — | — |
| Maximum Vectors at Scale(millions) | Limited to hardware (~1B) | — | — |
| Documentation Quality Score(score) | 8/10 | — | — |
| Metadata Filter Complexity(operators supported) | Basic ($where) | — | — |
| Setup Time to Production(minutes) | 0.1 days (2-4 hours) | 24-72 hours | |
| Query Latency (1M vectors)(ms) | 10-50 ms | 50-200 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) | — | — |
| LLM Provider Support(providers) | External (0 native) | — | — |
| Minimum Deployment Size(megabytes) | 50 | — | — |
| Retrieval Strategy Types(strategies) | 1 (similarity search) | — | — |
| Storage Backends(backend types) | 3 (in-memory, SQLite, cloud) | — | — |
| Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) | ~50ms | — | — |
| GitHub Stars (as of 2026)(stars) | 12,000+ stars | 9,500+ stars | |
| Time to First Query(minutes) | 1-2 minutes | 30-45 minutes (self-hosted) | |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | — | — |
| Number of Supported Languages(languages) | Python + JavaScript | — | — |
| Maximum Vectors Per Instance(vectors) | ~10M | 100M+ (distributed) | |
| Average Query Latency(milliseconds) | 10-50ms | 50-150ms | |
| Setup Time to First Query(minutes) | 2-5 (pip install) | 30-60 (with Docker) | |
| Minimum Memory for 1M Vectors(GB) | 1-2GB | 4-8GB | |
| Setup Time (first query)(minutes) | 2-5 | 30+ minutes (self-hosted) | |
| Max Recommended Vector Count(vectors) | 1-10M (single node) | 100M+ (distributed) | |
| Maximum Vector Scale(vectors) | 10-50 million | 1+ billion | |
| Minimum Setup Time(minutes) | 2-5 minutes | 30-60 minutes | |
| GitHub Stars(stars) | ~11,000 | ~4,000 | |
| Setup Time (Minutes)(minutes) | 15-30 | — | — |
| Supported Data Sources(count) | 12 embedding models | — | — |
| Query Latency (P95)(milliseconds) | 45-120 | — | — |
| Maximum Embeddings(millions) | 50M (in-memory) | — | — |
| GitHub Stars (2026)(stars) | 12,500 | — | — |
| Learning Curve (Hours)(hours) | 2-4 | — | — |
| Production Deployments Reported(count) | 500+ | — | — |
| 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 | — | — |
| Time to Production (First Query)(minutes) | 7 minutes | 25 minutes | |
| Maximum Recommended Vector Count(millions) | ~10M vectors | 500M+ vectors | |
| Minimum RAM Requirement (Single Node)(MB) | 64 MB | 512 MB | |
| Enterprise Support SLA(uptime %) | Community-driven, no SLA | 99.5% guaranteed uptime | — |
| 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) | — | — |
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | $500-800 (managed) | |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | 3 methods (HNSW, flat, dynamic) | |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | 75ms | |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | $800 | |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | 100+ | |
| API Query Language Support(count) | 2 (GraphQL, REST) | 2 (GraphQL, REST) | |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | 100,000 QPS | |
| Maximum Collection Size(billion vectors) | 2 billion vectors | 2 billion vectors | |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | 5-10 minutes (cloud) | |
| GitHub Community Stars(stars) | 13,000+ stars | 13,000+ stars | |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | 20+ LLM providers | |
| Query Latency (95th percentile)(milliseconds) | 100-500 ms | 100-500 ms | |
| Memory per 1M Vectors(GB) | 8-12 GB | 8-12 GB | |
| Startup Time (empty instance)(seconds) | 20-30 seconds | 20-30 seconds | |
| Built-in LLM Integrations(count) | 15+ providers | 15+ providers | |
| Managed Cloud Base Price (monthly)(USD) | $25/month | $25/month | |
| Throughput (vectors/second insert)(vectors/sec) | 5,000-10,000 | 5,000-10,000 | |
| Memory Usage (1M 768-dim vectors)(GB) | 1.2-1.5 GB | 1.2-1.5 GB | |
| Query Latency (1M vectors, 10 concurrent)(ms) | 45-80 ms | 45-80 ms | |
| Minimum Starting Cost (annual)(USD) | $300 (SaaS) or $0 (self-hosted) | $300 (SaaS) or $0 (self-hosted) | |
| Vector Index Types Supported(count) | 2 (HNSW, Flat) | 2 (HNSW, Flat) | |
| Query API Types(count) | 3 (GraphQL, REST, Python) | 3 (GraphQL, REST, Python) | |
| Maximum Vector Dimension Support(dimensions) | Unlimited (tested to 4096+) | Unlimited (tested to 4096+) | |
| Production Deployments (estimated)(count) | ~500 enterprise customers | ~500 enterprise customers | |
| Starting Monthly Cost(USD) | $0 (self-hosted) / $50+ (managed) | $0 (self-hosted) / $50+ (managed) | |
| Maximum Query Throughput(requests/second) | 2,000,000-3,000,000 | 2,000,000-3,000,000 | |
| P99 Query Latency(milliseconds) | 50-150ms | 50-150ms | |
| GitHub Stars (Community)(stars) | 9,200+ | 9,200+ | |
| Vector Indexing Algorithm Options(count) | HNSW, FLAT, IVF, PQ | HNSW, FLAT, IVF, PQ | |
| Scalability Limit (Single Node)(million vectors) | 100+ with optimization | 100+ with optimization | |
| Operational Complexity (1-10 scale)(complexity score) | High (8/10) | High (8/10) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
C
2ChromaWeaviate leads
W
5WeaviateChroma
Attribute
Weaviate
- Rapid prototyping, ML pipelines, RAG applicationsPrimary Use CaseEnterprise search, semantic search at scale, multi-tenant deployments(winner)
- In-process (embedded), Docker, minimal infrastructureDeployment OptionsKubernetes, managed cloud, hybrid cloud, on-premises clusters(winner)
- 5-10 minutes with Python SDK(winner)Setup Complexity (Time to First Vector)20-30 minutes with configuration requirements
- Basic metadata filtering onlyAdvanced Filtering CapabilitiesComplex WHERE clauses, nested filtering, cross-reference queries(winner)
- Not natively supportedMulti-Tenancy SupportFull multi-tenancy with tenant-level isolation(winner)
- 50-150 MB RAM for embedded mode(winner)Memory Footprint (Typical Small Deployment)500 MB - 2 GB for minimal cluster setup
- Community-driven, no official SLAsEnterprise Support & SLAsPaid enterprise support with 99.5% uptime SLA(winner)
- Primary Use Case
Chroma
Rapid prototyping, ML pipelines, RAG applications
Weaviate
Enterprise search, semantic search at scale, multi-tenant deployments(winner)
- Deployment Options
Chroma
In-process (embedded), Docker, minimal infrastructure
Weaviate
Kubernetes, managed cloud, hybrid cloud, on-premises clusters(winner)
- Setup Complexity (Time to First Vector)
Chroma
5-10 minutes with Python SDK(winner)
Weaviate
20-30 minutes with configuration requirements
- Advanced Filtering Capabilities
Chroma
Basic metadata filtering only
Weaviate
Complex WHERE clauses, nested filtering, cross-reference queries(winner)
- Multi-Tenancy Support
Chroma
Not natively supported
Weaviate
Full multi-tenancy with tenant-level isolation(winner)
- Memory Footprint (Typical Small Deployment)
Chroma
50-150 MB RAM for embedded mode(winner)
Weaviate
500 MB - 2 GB for minimal cluster setup
- Enterprise Support & SLAs
Chroma
Community-driven, no official SLAs
Weaviate
Paid enterprise support with 99.5% uptime SLA(winner)
Full Comparison
| Attribute | Chroma | Weaviate |
|---|---|---|
| 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 6 more attributesMonthly Base Cost (starter tier)(USD) $0 (open-source) — Managed Cloud Cost (1M queries/month)(USD) $50-150 — 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) / $50+ (managed) — Free Tier Availability Unlimited (self-hosted) — | ||
| Maximum Vector Storage(Vectors) | ~10M (single instance practical limit) | — |
| Maximum Vectors at Scale(millions) | Limited to hardware (~1B) | — |
| Maximum Practical Dataset Size(vectors) | ~10 million | — |
| Maximum Vectors Per Instance(vectors) | ~10M | 100M+ (distributed)(winner) |
| Max Recommended Vector Count(vectors) | 1-10M (single node) | 100M+ (distributed)(winner) |
Show 9 more attributesMaximum Embeddings(millions) 50M (in-memory) — Maximum Vector Capacity(vectors) 10M (single machine limit) — Maximum Vectors Per Index(vectors) ~10 million — Maximum Recommended Vectors(millions) 50-100M — Maximum Recommended Vector Count(millions) ~10M vectors 500M+ vectors Maximum Vector Capacity (single instance)(millions of vectors) 10 million — Maximum Scalability (distributed nodes)(nodes) 100+ — Maximum Collection Size(billion vectors) 2 billion vectors — Maximum Vector Dimension Support(dimensions) Unlimited (tested to 4096+) — | ||
| Maximum Vector Dimensions(dimensions) | 65,536 | Unlimited |
| Metadata Filter Complexity(operators supported) | Basic ($where) | — |
| Embedded Tokenizer Support | Yes (6+ models included) | — |
| Metadata Filtering Support | Native (boolean operators) | — |
| Retrieval Strategy Types(strategies) | 1 (similarity search) | — |
Show 20 more attributesStorage Backends(backend types) 3 (in-memory, SQLite, cloud) — Built-in Embedding Generation Yes (OpenAI, HuggingFace, Ollama) — Supported Index Types(count) Heuristic Search Algorithm (HNSW) — Hybrid Search Support (BM25 + Vector) No Yes Multi-Tenancy Support Not supported Native multi-tenancy with data isolation Query Filtering Support Basic metadata filters Advanced GraphQL + WHERE clauses with boolean logic Multi-Modal Search Text embeddings only Text, image, audio, video Hybrid Search (Vector + Keyword) No Yes (BM25) Multi-modal Support Text only Text, image, audio via modules Enterprise Features (RBAC/Multi-tenancy) No Yes 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) — 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 — 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) — | ||
| Query Latency (p99)(milliseconds) | 50-200ms | 50-150ms(winner) |
| Uptime Guarantee(percent) | No SLA | — |
| Query Latency (1M vectors)(ms) | 10-50 ms(winner) | 50-200 ms |
| Query Latency (1M vectors, single query)(milliseconds) | 150-300ms | — |
| Minimum Deployment Size(megabytes) | 50 | — |
Show 20 more attributesQuery Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) ~50ms — Average Query Latency(milliseconds) 10-50ms 50-150ms Maximum Vector Scale(vectors) 10-50 million 1+ billion 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 — 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 — Throughput (vectors/second insert)(vectors/sec) 5,000-10,000 — Memory Usage (1M 768-dim vectors)(GB) 1.2-1.5 GB — Query Latency (1M vectors, 10 concurrent)(ms) 45-80 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 SLA(%) | Community-dependent (no SLA) | User-managed (no SLA) |
| Setup Time (Local Development)(Minutes) | 2-5 (pip install + Python) | — |
| Setup Time(minutes) | 5 | — |
| Setup Time to First Query(minutes) | 2-5 (pip install)(winner) | 30-60 (with Docker) |
| Setup Time (Minutes)(minutes) | 15-30 | — |
| Learning Curve (Hours)(hours) | 2-4 | — |
Show 2 more attributesSetup Time (local environment)(minutes) 2-3 minutes — Setup Time (Cloud/Self-Hosted)(minutes) 5-10 minutes (cloud) — | ||
| Documentation Quality Score(score) | 8/10 | — |
| Setup Time (first query)(minutes) | 2-5(winner) | 30+ minutes (self-hosted) |
| Minimum Setup Time(minutes) | 2-5 minutes(winner) | 30-60 minutes |
| Setup Time (minutes to first working example)(minutes) | 3 minutes | — |
| API Query Language Support(count) | 2 (GraphQL, REST) | — |
| Setup Time to Production(minutes) | 0.1 days (2-4 hours)(winner) | 24-72 hours |
| Supported Deployment Modes | In-process, SQLite, HTTP API | Docker, Kubernetes, Cloud (AWS/GCP/Azure) |
| Minimum Setup Infrastructure | Python 3.7+; runs on laptop or serverless | Docker/Kubernetes cluster (4GB+ RAM minimum) |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — |
| 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) | — |
| GitHub Stars (as of 2026)(stars) | 12,000+ stars(winner) | 9,500+ stars |
| Time to First Query(minutes) | 1-2 minutes(winner) | 30-45 minutes (self-hosted) |
| Time to Production (First Query)(minutes) | 7 minutes(winner) | 25 minutes |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | — |
| Memory per 1M Vectors(GB) | 8-12 GB | — |
| Number of Supported Languages(languages) | Python + JavaScript | — |
| Complex Metadata Filtering Support | Basic equality/contains only | — |
| Minimum Memory for 1M Vectors(GB) | 1-2GB(winner) | 4-8GB |
| Kubernetes Support | Not native; runs as Python process | Native Kubernetes-ready Helm charts |
| LangChain Integration Maturity | Official, first-class integration | Supported but secondary to GraphQL API |
| GitHub Stars(stars) | ~11,000(winner) | ~4,000 |
| GitHub Stars (2026)(stars) | 12,500 | — |
| GitHub Community Stars(stars) | 13,000+ stars | — |
| GitHub Stars (Community)(stars) | 9,200+ | — |
| Deployment Options(types) | Embedded, Python, Serverless (SaaS beta) | Kubernetes, Docker, cloud (AWS/GCP/Azure) |
| Minimum RAM Requirement (Single Node)(MB) | 64 MB(winner) | 512 MB |
| Production Deployments Reported(count) | 500+ | — |
| Production Deployments (estimated)(count) | ~500 enterprise customers | — |
| 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.) | — |
| Open-Source Availability | Yes (Apache 2.0) | — |
| Open Source License | Apache 2.0 (Fully Open) | BSL 1.1 (Source-available, eventually open) |
| Primary Indexing Algorithm(algorithm type) | Flat, approximate nearest neighbor | — |
| Advanced Filtering Support | Basic metadata filters only | Complex WHERE clauses, nested conditions, cross-references |
| Multi-Tenancy | Not supported | Full native support with tenant isolation |
| Native Multi-tenancy Support | Yes, with built-in tenant isolation | — |
| Enterprise Support SLA(uptime %) | Community-driven, no SLA | 99.5% guaranteed uptime |
| Index Type Options(count) | 2 (SQLite, DuckDB) | — |
| Code Customization(null) | Unlimited (open-source) | — |
| GPU Acceleration Support | No | Limited (planning phase) |
| Free Tier Vector Limit(vectors) | Unlimited (self-hosted) | — |
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — |
| 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) | — |
| Multi-modal Support (native)(modalities) | 3 (text, image, audio) | — |
| Query Type Flexibility | Vector-first (GraphQL, REST) | — |
| Operational Complexity (1-10 scale)(complexity score) | High (8/10) | — |
| Native RESTful API | Yes (REST + GraphQL) | — |
CChroma
WWeaviate
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 6 more attributes
Monthly Base Cost (starter tier)(USD)
$0 (open-source)
—
Managed Cloud Cost (1M queries/month)(USD)
$50-150
—
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) / $50+ (managed)
—
Free Tier Availability
Unlimited (self-hosted)
—
Maximum Vector Storage(Vectors)
~10M (single instance practical limit)
—
Maximum Vectors at Scale(millions)
Limited to hardware (~1B)
—
Maximum Practical Dataset Size(vectors)
~10 million
—
Maximum Vectors Per Instance(vectors)
~10M
100M+ (distributed)
Max Recommended Vector Count(vectors)
1-10M (single node)
100M+ (distributed)
Show 9 more attributes
Maximum Embeddings(millions)
50M (in-memory)
—
Maximum Vector Capacity(vectors)
10M (single machine limit)
—
Maximum Vectors Per Index(vectors)
~10 million
—
Maximum Recommended Vectors(millions)
50-100M
—
Maximum Recommended Vector Count(millions)
~10M vectors
500M+ vectors
Maximum Vector Capacity (single instance)(millions of vectors)
10 million
—
Maximum Scalability (distributed nodes)(nodes)
100+
—
Maximum Collection Size(billion vectors)
2 billion vectors
—
Maximum Vector Dimension Support(dimensions)
Unlimited (tested to 4096+)
—
Maximum Vector Dimensions(dimensions)
65,536
Unlimited
Metadata Filter Complexity(operators supported)
Basic ($where)
—
Embedded Tokenizer Support
Yes (6+ models included)
—
Metadata Filtering Support
Native (boolean operators)
—
Retrieval Strategy Types(strategies)
1 (similarity search)
—
Show 20 more attributes
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)
—
Hybrid Search Support (BM25 + Vector)
No
Yes
Multi-Tenancy Support
Not supported
Native multi-tenancy with data isolation
Query Filtering Support
Basic metadata filters
Advanced GraphQL + WHERE clauses with boolean logic
Multi-Modal Search
Text embeddings only
Text, image, audio, video
Hybrid Search (Vector + Keyword)
No
Yes (BM25)
Multi-modal Support
Text only
Text, image, audio via modules
Enterprise Features (RBAC/Multi-tenancy)
No
Yes
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)
—
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
—
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)
—
Query Latency (p99)(milliseconds)
50-200ms
50-150ms
Uptime Guarantee(percent)
No SLA
—
Query Latency (1M vectors)(ms)
10-50 ms
50-200 ms
Query Latency (1M vectors, single query)(milliseconds)
150-300ms
—
Minimum Deployment Size(megabytes)
50
—
Show 20 more attributes
Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds)
~50ms
—
Average Query Latency(milliseconds)
10-50ms
50-150ms
Maximum Vector Scale(vectors)
10-50 million
1+ billion
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
—
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
—
Throughput (vectors/second insert)(vectors/sec)
5,000-10,000
—
Memory Usage (1M 768-dim vectors)(GB)
1.2-1.5 GB
—
Query Latency (1M vectors, 10 concurrent)(ms)
45-80 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 SLA(%)
Community-dependent (no SLA)
User-managed (no SLA)
Setup Time (Local Development)(Minutes)
2-5 (pip install + Python)
—
Setup Time(minutes)
5
—
Setup Time to First Query(minutes)
2-5 (pip install)
30-60 (with Docker)
Setup Time (Minutes)(minutes)
15-30
—
Learning Curve (Hours)(hours)
2-4
—
Show 2 more attributes
Setup Time (local environment)(minutes)
2-3 minutes
—
Setup Time (Cloud/Self-Hosted)(minutes)
5-10 minutes (cloud)
—
Documentation Quality Score(score)
8/10
—
Setup Time (first query)(minutes)
2-5
30+ minutes (self-hosted)
Minimum Setup Time(minutes)
2-5 minutes
30-60 minutes
Setup Time (minutes to first working example)(minutes)
3 minutes
—
API Query Language Support(count)
2 (GraphQL, REST)
—
Setup Time to Production(minutes)
0.1 days (2-4 hours)
24-72 hours
Supported Deployment Modes
In-process, SQLite, HTTP API
Docker, Kubernetes, Cloud (AWS/GCP/Azure)
Minimum Setup Infrastructure
Python 3.7+; runs on laptop or serverless
Docker/Kubernetes cluster (4GB+ RAM minimum)
Startup Time (empty instance)(seconds)
20-30 seconds
—
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)
—
GitHub Stars (as of 2026)(stars)
12,000+ stars
9,500+ stars
Time to First Query(minutes)
1-2 minutes
30-45 minutes (self-hosted)
Time to Production (First Query)(minutes)
7 minutes
25 minutes
Memory Footprint (at rest, 1M vectors)(MB)
~800MB
—
Memory per 1M Vectors(GB)
8-12 GB
—
Number of Supported Languages(languages)
Python + JavaScript
—
Complex Metadata Filtering Support
Basic equality/contains only
—
Minimum Memory for 1M Vectors(GB)
1-2GB
4-8GB
Kubernetes Support
Not native; runs as Python process
Native Kubernetes-ready Helm charts
LangChain Integration Maturity
Official, first-class integration
Supported but secondary to GraphQL API
GitHub Stars(stars)
~11,000
~4,000
GitHub Stars (2026)(stars)
12,500
—
GitHub Community Stars(stars)
13,000+ stars
—
GitHub Stars (Community)(stars)
9,200+
—
Deployment Options(types)
Embedded, Python, Serverless (SaaS beta)
Kubernetes, Docker, cloud (AWS/GCP/Azure)
Minimum RAM Requirement (Single Node)(MB)
64 MB
512 MB
Production Deployments Reported(count)
500+
—
Production Deployments (estimated)(count)
~500 enterprise customers
—
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.)
—
Open-Source Availability
Yes (Apache 2.0)
—
Open Source License
Apache 2.0 (Fully Open)
BSL 1.1 (Source-available, eventually open)
Primary Indexing Algorithm(algorithm type)
Flat, approximate nearest neighbor
—
Advanced Filtering Support
Basic metadata filters only
Complex WHERE clauses, nested conditions, cross-references
Multi-Tenancy
Not supported
Full native support with tenant isolation
Native Multi-tenancy Support
Yes, with built-in tenant isolation
—
Enterprise Support SLA(uptime %)
Community-driven, no SLA
99.5% guaranteed uptime
Index Type Options(count)
2 (SQLite, DuckDB)
—
Code Customization(null)
Unlimited (open-source)
—
GPU Acceleration Support
No
Limited (planning phase)
Free Tier Vector Limit(vectors)
Unlimited (self-hosted)
—
Estimated Monthly Cost (1M vectors)(USD)
$500-800 (managed)
—
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)
—
Multi-modal Support (native)(modalities)
3 (text, image, audio)
—
Query Type Flexibility
Vector-first (GraphQL, REST)
—
Operational Complexity (1-10 scale)(complexity score)
High (8/10)
—
Native RESTful API
Yes (REST + GraphQL)
—
Pros & Cons
10 pros·6 cons across both
C
W
C
Chroma
+5-3
Pros
- Embedded mode allows zero-infrastructure setup within Python applications
- Minimal learning curve with intuitive Python-first API design
- Sub-100MB memory footprint ideal for resource-constrained environments
- Fast iteration on LLM/RAG experiments with 5-minute onboarding
- Built-in integrations with LangChain, LlamaIndex, and Hugging Face
Cons
- Lacks advanced query filtering—only supports basic metadata filtering on vectors
- No native multi-tenancy or role-based access control for shared deployments
- Scales to ~5-10M vectors before performance degrades; not designed for 100M+ collections
W
Weaviate
+5-3
Pros
- Complex WHERE clause filtering with nested conditions and cross-references (GraphQL queries)
- Native multi-tenancy with complete data isolation and per-tenant configuration
- Proven production scalability: handles 500M+ vectors in distributed clusters
- Enterprise SLA support (99.5% uptime guarantee) with dedicated account management
- RBAC, audit logging, and compliance features for regulated industries
Cons
- Steeper learning curve requiring understanding of GraphQL and schema design
- Higher operational overhead: requires Kubernetes or managed cloud ($500+/month base cost)
- Overkill for small projects or prototypes (<1M vectors)
Frequently Asked Questions
5 questions
Chroma is the better choice for prototyping. Its embedded Python mode lets you spin up a vector database in your notebook in seconds, making it ideal for experimenting with LLM retrieval pipelines. Weaviate adds unnecessary complexity for early-stage projects.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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