software
Chroma vs Qdrant: Vector DB Comparison 2026
Chroma is a lightweight, Python-first vector database optimized for rapid prototyping and small-to-medium datasets, while Qdrant is an enterprise-grade vector database built for production scale with superior performance at 100M+ vectors and advanced filtering capabilities.
C
Chroma
Open-source and managed vector database designed for LLM applications with simple Python API
Startups, AI researchers, small teams building chatbots/RAG systems, MVP development, local experimentation
Score56%
VS
Q
Qdrant
Enterprise-grade vector database designed for production scale with 1B+ vector capacity and advanced retrieval features
Enterprise SaaS platforms, large-scale search systems, production ML pipelines, companies needing compliance and RBAC, applications with 100M+ vectors
Score56%
Quick Answer
AI SummaryChroma is a lightweight, Python-first vector database optimized for rapid prototyping and small-to-medium datasets, while Qdrant is an enterprise-grade vector database built for production scale with superior performance at 100M+ vectors and advanced filtering capabilities.
Our Verdict
AI-assistedChoose Chroma if you're building MVP applications, doing rapid AI experimentation, or working with <50M vectors where developer speed matters more than production scale. Choose Qdrant if you need production-grade reliability, must handle 100M+ vectors efficiently, require advanced filtering and hybrid search, or are building enterprise applications with complex security requirements.
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C
Chroma
6.1/10
Qdrant
8.9/10
Q
C
Choose Chroma if
Startups, AI researchers, small teams building chatbots/RAG systems, MVP development, local experimentation
Q
Choose Qdrant if
Best pickEnterprise SaaS platforms, large-scale search systems, production ML pipelines, companies needing compliance and RBAC, applications with 100M+ vectors
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Key Differences at a Glance
- Maximum Vectors Supported:✓ Qdrant wins(1B+ vectors vs ~10-50M vectors)
- Queries Per Second (QPS) at Scale:✓ Qdrant wins(10,000+ QPS vs 500-2,000 QPS)
- Setup Complexity:✓ Chroma wins(5 minutes (Python pip install) vs 15-30 minutes (Docker/Kubernetes))
Key Facts & Figures
87 numeric metrics compared
| Metric | Chroma | Qdrant | Ratio |
|---|---|---|---|
| Startup Time to First Query(minutes) | 5 minutes | — | — |
| Max Practical Vector Capacity(billion vectors) | 0.1-1B (managed) | — | — |
| Query Latency (1M vectors, CPU)(milliseconds) | 50-200ms | — | — |
| Learning Curve (hours for LLM RAG)(hours) | 0.5-2 hours | — | — |
| Production Users at Scale(companies) | 500+ | — | — |
| Monthly Starting Cost(USD) | $0 (free, open-source) | — | — |
| Maximum Vector Storage(Vectors) | ~10M (single instance practical limit) | — | — |
| Maximum Vector Dimensions(dimensions) | Unlimited (backend dependent) | Unlimited (100K+ tested) | — |
| Query Latency (p99)(milliseconds) | 50-200ms | 20-40ms (self-hosted) | |
| Uptime SLA(percent) | No SLA (community support) | Self-hosted (varies), Managed 99.5% | — |
| 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) | — | — |
| 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 | 10-50ms | |
| Maximum Practical Dataset Size(petabytes) | ~10 million | Billions+ | |
| 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 | — | — |
| Time to First Query(minutes) | 1-2 minutes | 20 minutes | |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | ~200MB | |
| Number of Supported Languages(languages) | Python + JavaScript | Python, JavaScript, Go, Java, Rust, C++, .NET | |
| 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) | 12,500+ | 28,000+ stars | |
| 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) | 1B+ | |
| 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 | ~75ms | |
| Maximum Recommended Vectors(millions) | 50-100M | Unlimited (billions with clustering) | |
| Setup Time (local environment)(minutes) | 2-3 minutes | 15-20 minutes (with Docker) | |
| Supported Embedding Dimensions(max dimensions) | Up to 2048 | Up to 65536 | |
| Language/SDK Support(number of SDKs) | Python, JavaScript, Go | Python, JavaScript, TypeScript, Go, Rust, Java, .NET | |
| Time to Production (First Query)(minutes) | 7 minutes | — | — |
| Maximum Recommended Vector Count(millions) | ~10M vectors | — | — |
| Minimum RAM Requirement (Single Node)(MB) | 64 MB | — | — |
| 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) | — | — |
| p50 Query Latency (Global)(milliseconds) | 250ms (cloud-hosted) | — | — |
| Storage Cost (1M vectors, 1536-dim)(USD per month) | $0 | — | — |
| Supported Programming Languages(languages) | Python, JavaScript, Go, Rust | — | — |
| Estimated Monthly Cost at 100GB(USD) | $25-100 (managed cloud) | $25-100 (managed cloud) | |
| Vector Dimension Limit(dimensions) | 65,536 | 65,536 | |
| GitHub Stars/Community Size(stars) | 18,000+ stars | 18,000+ stars | |
| Query Latency (95th percentile)(milliseconds) | 10-50 ms | 10-50 ms | |
| Memory per 1M Vectors(GB) | 2-4 GB | 2-4 GB | |
| Startup Time (empty instance)(seconds) | 2-5 seconds | 2-5 seconds | |
| Built-in LLM Integrations(count) | 0 (custom only) | 0 (custom only) | |
| Managed Cloud Base Price (monthly)(USD) | $10/month | $10/month | |
| Throughput (vectors/second insert)(vectors/sec) | 50,000-100,000 | 50,000-100,000 | |
| Monthly Cost (1M vectors, 768 dims)(USD) | $0 (self-hosted) or $25 (managed) | $0 (self-hosted) or $25 (managed) | |
| Time to Production(days) | 30-120 minutes | 30-120 minutes | |
| Query Throughput (QPS)(queries/second) | 10,000+ QPS | 10,000+ QPS | |
| Memory Overhead per Vector(bytes) | 50-100 bytes | 50-100 bytes | |
| Latency at 100M Vectors(milliseconds) | 50-150ms | 50-150ms |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
C
1ChromaQdrant leads1 tie
Q
5QdrantChroma
Attribute
Qdrant
- ~10-50M vectorsMaximum Vectors Supported1B+ vectors(winner)
- 500-2,000 QPSQueries Per Second (QPS) at Scale10,000+ QPS(winner)
- 5 minutes (Python pip install)(winner)Setup Complexity15-30 minutes (Docker/Kubernetes)
- In-process, serverless, or managed cloudProduction Deployment ModelSelf-hosted, cloud, or managed SaaS
- Keyword search via integrationsHybrid Search SupportNative hybrid search with BM25 ranking(winner)
- ~100-200 bytes overheadMemory Efficiency per Vector~50-100 bytes overhead(winner)
- API key onlyBuilt-in Access ControlRBAC, API keys, OAuth2 support(winner)
- Maximum Vectors Supported
Chroma
~10-50M vectors
Qdrant
1B+ vectors(winner)
- Queries Per Second (QPS) at Scale
Chroma
500-2,000 QPS
Qdrant
10,000+ QPS(winner)
- Setup Complexity
Chroma
5 minutes (Python pip install)(winner)
Qdrant
15-30 minutes (Docker/Kubernetes)
- Production Deployment Model
Chroma
In-process, serverless, or managed cloud
Qdrant
Self-hosted, cloud, or managed SaaS
- Hybrid Search Support
Chroma
Keyword search via integrations
Qdrant
Native hybrid search with BM25 ranking(winner)
- Memory Efficiency per Vector
Chroma
~100-200 bytes overhead
Qdrant
~50-100 bytes overhead(winner)
- Built-in Access Control
Chroma
API key only
Qdrant
RBAC, API keys, OAuth2 support(winner)
Full Comparison
| Attribute | Chroma | Qdrant |
|---|---|---|
| Startup Time to First Query(minutes) | 5 minutes | — |
| Learning Curve (hours for LLM RAG)(hours) | 0.5-2 hours | — |
| Documentation Quality Score(score) | 8/10 | — |
| Setup Time (first query)(minutes) | 2-5 | — |
| Setup Time (minutes to first working example)(minutes) | 3 minutes | — |
| Max Practical Vector Capacity(billion vectors) | 0.1-1B (managed) | — |
| Maximum Vector Storage(Vectors) | ~10M (single instance practical limit) | — |
| Maximum Vector Dimensions(dimensions) | Unlimited (backend dependent) | Unlimited (100K+ tested) |
| Maximum Vectors at Scale(millions) | Limited to hardware (~1B) | — |
| Maximum Practical Dataset Size(petabytes) | ~10 million | Billions+(winner) |
Show 7 more attributesMaximum Vectors Per Instance(vectors) ~10M — Max Recommended Vector Count(vectors) 1-10M (single node) — Maximum Embeddings(millions) 50M (in-memory) — Maximum Vectors Per Index(vectors) ~10 million — Maximum Recommended Vectors(millions) 50-100M Unlimited (billions with clustering) Maximum Recommended Vector Count(millions) ~10M vectors — Maximum Vector Capacity (single instance)(millions of vectors) 10 million — | ||
| Query Latency (1M vectors, CPU)(milliseconds) | 50-200ms | — |
| GPU Acceleration | Not available | — |
| Query Latency (p99)(milliseconds) | 50-200ms | 20-40ms (self-hosted)(winner) |
| Query Latency (1M vectors)(ms) | 10-50 ms | — |
| Query Latency (1M vectors, single query)(milliseconds) | 150-300ms | 10-50ms(winner) |
Show 13 more attributesMinimum Deployment Size(megabytes) 50 — Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) ~50ms — Average Query Latency(milliseconds) 10-50ms — Maximum Vector Scale(vectors) 10-50 million — 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 ~75ms Query Latency at 1M vectors(milliseconds) 50-150ms — p50 Query Latency (Global)(milliseconds) 250ms (cloud-hosted) — Query Latency (95th percentile)(milliseconds) 10-50 ms — Throughput (vectors/second insert)(vectors/sec) 50,000-100,000 — | ||
| Hosting Flexibility | Managed cloud + local/open-source | — |
| Deployment Options | Embedded, Python, Serverless (SaaS beta) | Self-hosted + managed cloud |
| Minimum RAM Requirement (Single Node)(MB) | 64 MB | — |
| Production Users at Scale(companies) | 500+ | — |
| 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 5 more attributesMonthly Base Cost (starter tier)(USD) $0 (open-source) — Managed Cloud Cost (1M queries/month)(USD) $50-150 — Storage Cost (1M vectors, 1536-dim)(USD per month) $0 — Managed Cloud Base Price (monthly)(USD) $10/month — Monthly Cost (1M vectors, 768 dims)(USD) $0 (self-hosted) or $25 (managed) — | ||
| Uptime SLA(percent) | No SLA (community support) | Self-hosted (varies), Managed 99.5% |
| Uptime Guarantee(%) | No SLA | — |
| SLA Uptime Guarantee(percent) | Varies by self-hosted setup | — |
| Setup Time (Local Development)(Minutes) | 2-5 (pip install + Python) | — |
| Setup Time(minutes) | 5 minutes(winner) | 15-30 minutes |
| Setup Time to First Query(minutes) | 2-5 (pip install) | — |
| Setup Time (Minutes)(minutes) | 15-30 | — |
| Learning Curve (Hours)(hours) | 2-4 | — |
Show 2 more attributesInitial Setup Time(minutes) 2 minutes — Setup Time (local environment)(minutes) 2-3 minutes 15-20 minutes (with Docker) | ||
| Metadata Filter Complexity(operators supported) | Basic ($where) | — |
| Embedded Tokenizer Support | Yes (6+ models included) | — |
| Metadata Filtering Support | Native (full SQL-like support) | — |
| Retrieval Strategy Types(strategies) | 1 (similarity search) | — |
| Storage Backends(backend types) | 3 (in-memory, SQLite, cloud) | — |
Show 16 more attributesBuilt-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) — Supported Embedding Dimensions(max dimensions) Up to 2048 Up to 65536 Filtering Query Support(complexity level) Basic metadata matching Complex nested, geo, range, and boolean queries Built-in Embedding Model Support OpenAI, Cohere, Hugging Face, Ollama (6+ providers) — Metadata Filtering Complexity(feature count) Basic equality/contains Advanced boolean/range queries Hybrid Search Support Yes (dense + sparse) — Native Hybrid Search Yes (BM25 included) — | ||
| Setup Time to Production(minutes) | 0.1 days (2-4 hours) | — |
| Supported Deployment Modes | In-process, SQLite, HTTP API | — |
| Minimum Setup Infrastructure | Python 3.7+; runs on laptop or serverless | — |
| Self-Hosting Available | Yes (open-source) | — |
| Startup Time (empty instance)(seconds) | 2-5 seconds | — |
Show 1 more attributeTime to Production(days) 30-120 minutes — | ||
| 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 | Python, JavaScript, TypeScript, Go, Rust, Java, .NET(winner) |
Show 1 more attributeAPI Compatibility OpenAI API compatible + REST — | ||
| Production Observability | Basic logging | — |
| 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 | — |
| GitHub Stars/Community Size(stars) | 18,000+ stars | — |
| Time to First Query(minutes) | 1-2 minutes(winner) | 20 minutes |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | ~200MB(winner) |
| Memory per 1M Vectors(GB) | 2-4 GB | — |
| Number of Supported Languages(languages) | Python + JavaScript | Python, JavaScript, Go, Java, Rust, C++, .NET(winner) |
| Kubernetes-Native Deployment | Not recommended; in-process only | Yes; Helm charts, StatefulSet support |
| Complex Metadata Filtering Support | Basic equality/contains only | Nested fields, range, AND/OR/NOT, geo-spatial |
| Minimum Memory for 1M Vectors(GB) | 1-2GB | — |
| Kubernetes Support | Not native; runs as Python process | — |
| LangChain Integration Maturity | Official, first-class integration | — |
| Minimum Setup Time(minutes) | 2-5 minutes | — |
| GitHub Stars(stars) | 12,500+ | 28,000+ stars(winner) |
| GitHub Stars (2026)(stars) | 12,500 | — |
| Production Deployments Reported(count) | 500+ | — |
| Maximum Vector Capacity(vectors) | 10M (single machine limit) | 1B+(winner) |
| Query Throughput (QPS)(queries/second) | 10,000+ QPS | — |
| Latency at 100M Vectors(milliseconds) | 50-150ms | — |
| RBAC & Enterprise Security(yes/no) | No | — |
| Multi-tenant RBAC Support | Full RBAC + OAuth2 | — |
| 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) | AGPL-3.0 (with commercial license) |
| License Model | BUSL-1.1 + Cloud/Enterprise | — |
| Primary Indexing Algorithm(algorithm type) | Flat, approximate nearest neighbor | HNSW, IVF-Flat, Product Quantization |
| Time to Production (First Query)(minutes) | 7 minutes | — |
| Advanced Filtering Support | Basic metadata filters only | — |
| Multi-Tenancy | Not supported | — |
| Enterprise Support SLA | Community-driven, no SLA | — |
| Index Type Options(count) | 2 (SQLite, DuckDB) | — |
| GPU Acceleration Support | No | — |
| Supported Programming Languages(languages) | Python, JavaScript, Go, Rust | — |
| Pricing Model | Self-hosted free or managed from $25/mo | — |
| Estimated Monthly Cost at 100GB(USD) | $25-100 (managed cloud) | — |
| Vector Dimension Limit(dimensions) | 65,536 | — |
| Built-in LLM Integrations(count) | 0 (custom only) | — |
| Multi-modal Support (native)(modalities) | 1 (vectors only) | — |
| Memory Overhead per Vector(bytes) | 50-100 bytes | — |
CChroma
QQdrant
Startup Time to First Query(minutes)
5 minutes
—
Learning Curve (hours for LLM RAG)(hours)
0.5-2 hours
—
Documentation Quality Score(score)
8/10
—
Setup Time (first query)(minutes)
2-5
—
Setup Time (minutes to first working example)(minutes)
3 minutes
—
Max Practical Vector Capacity(billion vectors)
0.1-1B (managed)
—
Maximum Vector Storage(Vectors)
~10M (single instance practical limit)
—
Maximum Vector Dimensions(dimensions)
Unlimited (backend dependent)
Unlimited (100K+ tested)
Maximum Vectors at Scale(millions)
Limited to hardware (~1B)
—
Maximum Practical Dataset Size(petabytes)
~10 million
Billions+
Show 7 more attributes
Maximum Vectors Per Instance(vectors)
~10M
—
Max Recommended Vector Count(vectors)
1-10M (single node)
—
Maximum Embeddings(millions)
50M (in-memory)
—
Maximum Vectors Per Index(vectors)
~10 million
—
Maximum Recommended Vectors(millions)
50-100M
Unlimited (billions with clustering)
Maximum Recommended Vector Count(millions)
~10M vectors
—
Maximum Vector Capacity (single instance)(millions of vectors)
10 million
—
Query Latency (1M vectors, CPU)(milliseconds)
50-200ms
—
GPU Acceleration
Not available
—
Query Latency (p99)(milliseconds)
50-200ms
20-40ms (self-hosted)
Query Latency (1M vectors)(ms)
10-50 ms
—
Query Latency (1M vectors, single query)(milliseconds)
150-300ms
10-50ms
Show 13 more attributes
Minimum Deployment Size(megabytes)
50
—
Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds)
~50ms
—
Average Query Latency(milliseconds)
10-50ms
—
Maximum Vector Scale(vectors)
10-50 million
—
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
~75ms
Query Latency at 1M vectors(milliseconds)
50-150ms
—
p50 Query Latency (Global)(milliseconds)
250ms (cloud-hosted)
—
Query Latency (95th percentile)(milliseconds)
10-50 ms
—
Throughput (vectors/second insert)(vectors/sec)
50,000-100,000
—
Hosting Flexibility
Managed cloud + local/open-source
—
Deployment Options
Embedded, Python, Serverless (SaaS beta)
Self-hosted + managed cloud
Minimum RAM Requirement (Single Node)(MB)
64 MB
—
Production Users at Scale(companies)
500+
—
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 5 more attributes
Monthly Base Cost (starter tier)(USD)
$0 (open-source)
—
Managed Cloud Cost (1M queries/month)(USD)
$50-150
—
Storage Cost (1M vectors, 1536-dim)(USD per month)
$0
—
Managed Cloud Base Price (monthly)(USD)
$10/month
—
Monthly Cost (1M vectors, 768 dims)(USD)
$0 (self-hosted) or $25 (managed)
—
Uptime SLA(percent)
No SLA (community support)
Self-hosted (varies), Managed 99.5%
Uptime Guarantee(%)
No SLA
—
SLA Uptime Guarantee(percent)
Varies by self-hosted setup
—
Setup Time (Local Development)(Minutes)
2-5 (pip install + Python)
—
Setup Time(minutes)
5 minutes
15-30 minutes
Setup Time to First Query(minutes)
2-5 (pip install)
—
Setup Time (Minutes)(minutes)
15-30
—
Learning Curve (Hours)(hours)
2-4
—
Show 2 more attributes
Initial Setup Time(minutes)
2 minutes
—
Setup Time (local environment)(minutes)
2-3 minutes
15-20 minutes (with Docker)
Metadata Filter Complexity(operators supported)
Basic ($where)
—
Embedded Tokenizer Support
Yes (6+ models included)
—
Metadata Filtering Support
Native (full SQL-like support)
—
Retrieval Strategy Types(strategies)
1 (similarity search)
—
Storage Backends(backend types)
3 (in-memory, SQLite, cloud)
—
Show 16 more attributes
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)
—
Supported Embedding Dimensions(max dimensions)
Up to 2048
Up to 65536
Filtering Query Support(complexity level)
Basic metadata matching
Complex nested, geo, range, and boolean queries
Built-in Embedding Model Support
OpenAI, Cohere, Hugging Face, Ollama (6+ providers)
—
Metadata Filtering Complexity(feature count)
Basic equality/contains
Advanced boolean/range queries
Hybrid Search Support
Yes (dense + sparse)
—
Native Hybrid Search
Yes (BM25 included)
—
Setup Time to Production(minutes)
0.1 days (2-4 hours)
—
Supported Deployment Modes
In-process, SQLite, HTTP API
—
Minimum Setup Infrastructure
Python 3.7+; runs on laptop or serverless
—
Self-Hosting Available
Yes (open-source)
—
Startup Time (empty instance)(seconds)
2-5 seconds
—
Show 1 more attribute
Time to Production(days)
30-120 minutes
—
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
Python, JavaScript, TypeScript, Go, Rust, Java, .NET
Show 1 more attribute
API Compatibility
OpenAI API compatible + REST
—
Production Observability
Basic logging
—
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
—
GitHub Stars/Community Size(stars)
18,000+ stars
—
Time to First Query(minutes)
1-2 minutes
20 minutes
Memory Footprint (at rest, 1M vectors)(MB)
~800MB
~200MB
Memory per 1M Vectors(GB)
2-4 GB
—
Number of Supported Languages(languages)
Python + JavaScript
Python, JavaScript, Go, Java, Rust, C++, .NET
Kubernetes-Native Deployment
Not recommended; in-process only
Yes; Helm charts, StatefulSet support
Complex Metadata Filtering Support
Basic equality/contains only
Nested fields, range, AND/OR/NOT, geo-spatial
Minimum Memory for 1M Vectors(GB)
1-2GB
—
Kubernetes Support
Not native; runs as Python process
—
LangChain Integration Maturity
Official, first-class integration
—
Minimum Setup Time(minutes)
2-5 minutes
—
GitHub Stars(stars)
12,500+
28,000+ stars
GitHub Stars (2026)(stars)
12,500
—
Production Deployments Reported(count)
500+
—
Maximum Vector Capacity(vectors)
10M (single machine limit)
1B+
Query Throughput (QPS)(queries/second)
10,000+ QPS
—
Latency at 100M Vectors(milliseconds)
50-150ms
—
RBAC & Enterprise Security(yes/no)
No
—
Multi-tenant RBAC Support
Full RBAC + OAuth2
—
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)
AGPL-3.0 (with commercial license)
License Model
BUSL-1.1 + Cloud/Enterprise
—
Primary Indexing Algorithm(algorithm type)
Flat, approximate nearest neighbor
HNSW, IVF-Flat, Product Quantization
Time to Production (First Query)(minutes)
7 minutes
—
Advanced Filtering Support
Basic metadata filters only
—
Multi-Tenancy
Not supported
—
Enterprise Support SLA
Community-driven, no SLA
—
Index Type Options(count)
2 (SQLite, DuckDB)
—
GPU Acceleration Support
No
—
Supported Programming Languages(languages)
Python, JavaScript, Go, Rust
—
Pricing Model
Self-hosted free or managed from $25/mo
—
Estimated Monthly Cost at 100GB(USD)
$25-100 (managed cloud)
—
Vector Dimension Limit(dimensions)
65,536
—
Built-in LLM Integrations(count)
0 (custom only)
—
Multi-modal Support (native)(modalities)
1 (vectors only)
—
Memory Overhead per Vector(bytes)
50-100 bytes
—
Pros & Cons
10 pros·8 cons across both
C
Q
C
Chroma
+5-4
Pros
- Sub-5-minute setup with 'pip install chromadb'
- Native Python-first API with minimal dependencies
- Supports in-process mode for zero infrastructure
- Excellent for LLM chains and RAG applications
- Free open-source with MIT license
Cons
- Struggles above 50M vectors with noticeable latency degradation
- Limited to 500-2,000 QPS before performance drops
- No native RBAC or advanced access control
- Hybrid search requires third-party integrations
Q
Qdrant
+5-4
Pros
- Handles 1B+ vectors at 10,000+ QPS with sub-100ms latency
- Native hybrid search combining dense and sparse vectors
- Advanced RBAC with OAuth2 and multi-tenant support
- Optimized memory usage at ~50-100 bytes per vector
- Built-in payload filtering and complex query support
Cons
- Steeper learning curve requiring Docker/Kubernetes familiarity
- 15-30 minute initial setup vs Chroma's 5 minutes
- Overkill for small datasets or prototypes (<10M vectors)
- Higher operational overhead for self-hosted deployments
Frequently Asked Questions
5 questions
Chroma is the clear choice for MVPs. It installs in 5 minutes, requires no infrastructure, and works in-process or as a Python library. This lets you iterate rapidly on your AI product without DevOps overhead. Qdrant becomes relevant once you exceed 50M vectors or need production SLAs.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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