software
Chroma vs pgvector 2026: Vector Database Comparison
Chroma is a standalone vector database optimized for AI/ML workflows with simple Python APIs, while pgvector is a PostgreSQL extension that integrates vector search into existing relational databases. Chroma excels for specialized vector-only applications, whereas pgvector is better for applications needing both relational and vector data together.
C
Chroma
Open-source vector database optimized for AI embeddings and semantic search with Python-first APIs.
AI/ML teams, startups building vector-first applications, developers prototyping semantic search and RAG systems
Score63%
VS
P
pgvector
Open-source PostgreSQL extension for vector similarity search within existing PostgreSQL databases.
Enterprise teams already using PostgreSQL, applications requiring hybrid relational+vector queries, production systems needing ACID guarantees
Score63%
Quick Answer
AI SummaryChroma is a standalone vector database optimized for AI/ML workflows with simple Python APIs, while pgvector is a PostgreSQL extension that integrates vector search into existing relational databases. Chroma excels for specialized vector-only applications, whereas pgvector is better for applications needing both relational and vector data together.
Our Verdict
AI-assistedChoose Chroma if you need a lightweight, purpose-built vector database for AI/ML applications with rapid prototyping and don't require traditional SQL querying. Choose pgvector if you already use PostgreSQL, need to combine vector search with relational data queries, or require enterprise-grade database reliability and ACID compliance.
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C
Chroma
9.4/10
pgvector
5.6/10
P
C
Choose Chroma if
Best pickAI/ML teams, startups building vector-first applications, developers prototyping semantic search and RAG systems
P
Choose pgvector if
Enterprise teams already using PostgreSQL, applications requiring hybrid relational+vector queries, production systems needing ACID guarantees
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Key Differences at a Glance
- Architecture Type:Standalone vector database vs PostgreSQL extension/plugin
- SQL/Relational Data Support:✓ pgvector wins(Full SQL + vector search combined vs No native SQL, metadata filtering only)
- Setup Complexity:✓ Chroma wins(Minutes (Python install) vs Hours (PostgreSQL + extension install))
Key Facts & Figures
90 numeric metrics compared
| Metric | Chroma | pgvector | 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) | — | — |
| Query Latency (p99)(milliseconds) | 50-200ms | 100-500ms | |
| 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) | 1-4 hours | |
| Query Latency (1M vectors)(ms) | 10-50 ms | — | — |
| Memory Usage (10M vectors)(GB) | 3-5 GB | — | — |
| Query Latency (1M vectors, single query)(milliseconds) | 150-300ms | — | — |
| Maximum Practical Dataset Size(petabytes) | ~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 | ~120ms | |
| GitHub Stars (as of 2026)(stars) | 12,000+ stars | ~10,500 | |
| Supported Index Types(count) | Heuristic Search Algorithm (HNSW) | 2 (HNSW, IVFFlat) | — |
| Time to First Query(minutes) | 1-2 minutes | 45-120 minutes | |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | — | — |
| Number of Supported Languages(languages) | Python + JavaScript | — | — |
| Maximum Vectors Per Instance(vectors) | ~10M | — | — |
| Average Query Latency(milliseconds) | 10-50ms | — | — |
| 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 | 120-300 minutes | |
| GitHub Stars(stars) | 12,500+ | ~10,800 | |
| 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) | — | — |
| 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) | <1 billion (practical 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 | 30-50ms | |
| Maximum Supported Vector Dimensions(dimensions) | 2048 | 2000+ | |
| Managed Cloud Cost (1M queries/month)(USD) | $50-150 | $20-80 (AWS RDS) | |
| 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 | — | — |
| 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 | — | — |
| Setup Time to First Query(minutes) | 2 minutes | 45 minutes | |
| Average Latency (1M vectors)(milliseconds) | 75ms | 55ms | |
| Maximum Vector Dimensions(dimensions) | Unlimited | 2,000 | — |
| GitHub Stars (2026)(stars) | 12,000+ | 9,500+ | |
| Cost for 1M Monthly Read Operations(USD) | $0 (self-hosted only) | $0 (self-hosted only) | |
| Vector Dimensionality Support(maximum dimensions) | Up to 2,000 dimensions | Up to 2,000 dimensions | |
| GitHub Community Stars(stars) | 4,200+ stars | 4,200+ stars | |
| Indexing Methods Supported(count) | 2 methods (IVFFlat, HNSW) | 2 methods (IVFFlat, HNSW) | |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 120ms | 120ms | |
| Integrated LLM Providers(count) | None (requires external integration) | None (requires external integration) | |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $150 | $150 | |
| Maximum Scalability (distributed nodes)(nodes) | 1-3 (read replicas) | 1-3 (read replicas) | |
| API Query Language Support(count) | 1 (SQL only) | 1 (SQL only) | |
| Production Starter Cost(USD/month) | $0 (infra only) | $0 (infra only) | |
| Average Query Latency (P99)(milliseconds) | 100-300ms | 100-300ms | |
| Setup to Production Time(hours) | 2-4 | 2-4 | |
| Vector Indexing Algorithm Options(count) | HNSW, IVFFlat | HNSW, IVFFlat | |
| Scalability Limit (Single Node)(million vectors) | 10-50 before latency issues | 10-50 before latency issues | |
| Operational Complexity (1-10 scale)(complexity score) | Very Low (2/10) | Very Low (2/10) | |
| Cost for 1M Vectors/Month(USD) | $10-50 | $10-50 |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
C
3ChromaChroma leads2 ties
P
2pgvectorChroma
Attribute
pgvector
- Standalone vector databaseArchitecture TypePostgreSQL extension/plugin
- No native SQL, metadata filtering onlySQL/Relational Data SupportFull SQL + vector search combined(winner)
- Minutes (Python install)(winner)Setup ComplexityHours (PostgreSQL + extension install)
- ~50-100ms avg latencyVector Search Speed (1M vectors)~30-80ms avg latency(winner)
- Unlimited (dynamic)(winner)Maximum Vector Dimension Support2000 dimensions
- 12,000+ stars(winner)Community Adoption (GitHub stars)9,500+ stars
- Apache 2.0 (Open source)License TypePostgreSQL License (Open source)
- Architecture Type
Chroma
Standalone vector database
pgvector
PostgreSQL extension/plugin
- SQL/Relational Data Support
Chroma
No native SQL, metadata filtering only
pgvector
Full SQL + vector search combined(winner)
- Setup Complexity
Chroma
Minutes (Python install)(winner)
pgvector
Hours (PostgreSQL + extension install)
- Vector Search Speed (1M vectors)
Chroma
~50-100ms avg latency
pgvector
~30-80ms avg latency(winner)
- Maximum Vector Dimension Support
Chroma
Unlimited (dynamic)(winner)
pgvector
2000 dimensions
- Community Adoption (GitHub stars)
Chroma
12,000+ stars(winner)
pgvector
9,500+ stars
- License Type
Chroma
Apache 2.0 (Open source)
pgvector
PostgreSQL License (Open source)
Full Comparison
| Attribute | Chroma | pgvector |
|---|---|---|
| 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 | — |
Show 1 more attributeAPI Query Language Support(count) 1 (SQL only) — | ||
| Max Practical Vector Capacity(billion vectors) | 0.1-1B (managed) | — |
| Maximum Vector Storage(Vectors) | ~10M (single instance practical limit) | — |
| Maximum Vectors at Scale(millions) | Limited to hardware (~1B) | — |
| Maximum Practical Dataset Size(petabytes) | ~10 million | — |
| Maximum Vectors Per Instance(vectors) | ~10M | — |
Show 7 more attributesMax 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 — Maximum Recommended Vector Count(millions) ~10M vectors — Maximum Vector Capacity (single instance)(millions of vectors) 10 million — Maximum Scalability (distributed nodes)(nodes) 1-3 (read replicas) — | ||
| Query Latency (1M vectors, CPU)(milliseconds) | 50-200ms | — |
| GPU Acceleration | Not available | — |
| Query Latency (p99)(milliseconds) | 50-200ms(winner) | 100-500ms |
| Query Latency (1M vectors)(ms) | 10-50 ms | — |
| Query Latency (1M vectors, single query)(milliseconds) | 150-300ms | — |
Show 17 more attributesMinimum Deployment Size(megabytes) 50 — Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) ~50ms ~120ms 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 30-50ms Query Latency (1M vectors, p99)(milliseconds) ~350ms — Query Latency at 1M vectors(milliseconds) 50-150ms — p50 Query Latency (Global)(milliseconds) 250ms (cloud-hosted) — Average Latency (1M vectors)(milliseconds) 75ms 55ms Indexing Methods Supported(count) 2 methods (IVFFlat, HNSW) — Average Query Latency (1M vectors, 384-dim)(milliseconds) 120ms — Average Query Latency (P99)(milliseconds) 100-300ms — Vector Indexing Algorithm Options(count) HNSW, IVFFlat — Scalability Limit (Single Node)(million vectors) 10-50 before latency issues — | ||
| Hosting Flexibility | Managed cloud + local/open-source | — |
| Deployment Options | Embedded, Python, Serverless (SaaS beta) | — |
| 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 6 more attributesMonthly Base Cost (starter tier)(USD) $0 (open-source) — Managed Cloud Cost (1M queries/month)(USD) $50-150 $20-80 (AWS RDS) Storage Cost (1M vectors, 1536-dim)(USD per month) $0 — Cost for 1M Monthly Read Operations(USD) $0 (self-hosted only) — Production Starter Cost(USD/month) $0 (infra only) — Cost for 1M Vectors/Month(USD) $10-50 — | ||
| Uptime SLA(percent) | No SLA (community support) | Self-managed (varies) |
| Uptime Guarantee(%) | No SLA | — |
| ACID Compliance | No | Yes (full support) |
| Uptime SLA Guarantee(percent) | User dependent (no SLA) | — |
| Setup Time (Local Development)(Minutes) | 2-5 (pip install + Python) | — |
| Setup Time (Minutes)(minutes) | 15-30 | — |
| Learning Curve (Hours)(hours) | 2-4 | — |
| Setup Time (local environment)(minutes) | 2-3 minutes | — |
| Metadata Filter Complexity(operators supported) | Basic ($where) | — |
| Embedded Tokenizer Support | Yes (6+ models included) | — |
| Metadata Filtering Support | Native (full SQL-like support) | Limited (SQL WHERE clauses only) |
| 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) No (external only) Supported Index Types(count) Heuristic Search Algorithm (HNSW) 2 (HNSW, IVFFlat) Hybrid Search Support (BM25 + Vector) No — Multi-Tenancy Support Not supported Requires schema/RLS workarounds 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 — Filtering Query Support(complexity level) Basic metadata matching — Built-in Embedding Model Support OpenAI, Cohere, Hugging Face, Ollama (6+ providers) — Metadata Filtering Complexity(feature count) Basic equality/contains — Vector Dimensionality Support(maximum dimensions) Up to 2,000 dimensions — SQL Relational Query Integration(native support) Yes (unified via SQL) — | ||
| Setup Time to Production(minutes) | 0.1 days (2-4 hours)(winner) | 1-4 hours |
| Installation Complexity(shell commands) | 5-10 minutes (Python package) | Integrated (no new deployment) |
| Supported Deployment Modes | In-process, SQLite, HTTP API | — |
| Minimum Setup Infrastructure | Python 3.7+; runs on laptop or serverless | — |
| Open Source Availability | Yes (Apache 2.0) | Yes (PostgreSQL License) |
| 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 | — |
| Setup Time(minutes) | 5 minutes(winner) | 30-120 minutes |
| Deployment Complexity(complexity score (1-10)) | 2/10(winner) | 7/10 |
| Setup to Production Time(hours) | 2-4 | — |
| Infrastructure Required | PostgreSQL instance (AWS RDS, self-hosted, etc.) | — |
| Production Observability | Basic logging | — |
| SQL Filtering Capability | JSON metadata filters (limited) | Full SQL WHERE clauses (unlimited) |
| Native SQL Support | Limited (metadata filtering only) | Full SQL with vector operators |
| GitHub Stars (as of 2026)(stars) | 12,000+ stars(winner) | ~10,500 |
| GitHub Stars(stars) | 12,500+(winner) | ~10,800 |
| GitHub Stars (2026)(stars) | 12,000+(winner) | 9,500+ |
| GitHub Community Stars(stars) | 4,200+ stars | — |
| Time to First Query(minutes) | 1-2 minutes(winner) | 45-120 minutes |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | — |
| Number of Supported Languages(languages) | Python + JavaScript | — |
| Kubernetes-Native Deployment | Not recommended; in-process only | — |
| Complex Metadata Filtering Support | Basic equality/contains only | — |
| Supported Programming Languages(languages) | Python, JavaScript, Go, Rust | — |
| 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(winner) | 120-300 minutes |
| Production Deployments Reported(count) | 500+ | — |
| Initial Setup Time(minutes) | 2 minutes | — |
| Maximum Vector Capacity(vectors) | 10M (single machine limit)(winner) | <1 billion (practical limit) |
| RBAC & Enterprise Security(yes/no) | No | — |
| Supported Vector Dimensions(dimensions) | Unlimited | — |
| Maximum Supported Vector Dimensions(dimensions) | 2048(winner) | 2000+ |
| Relational Data Integration | No (requires external database) | Native (single database) |
| Maximum Vector Dimensions(dimensions) | Unlimited | 2,000 |
| SQL Query Support | No (metadata filters only) | Yes (full SQL support) |
Show 1 more attributeSupported Indexing Algorithms(count) HNSW, IVFFlat, Exact — | ||
| LangChain Integration Native Support | Yes, official integration | — |
| Embedding Auto-Generation | Yes (Hugging Face, OpenAI, etc.) | No (external preprocessing required) |
| Primary Indexing Algorithm(algorithm type) | Flat, approximate nearest neighbor | — |
| Time to Production (First Query)(minutes) | 7 minutes | — |
| Advanced Filtering Support | Basic metadata filters only | — |
| Multi-Tenancy | Not supported | — |
| Native Multi-tenancy Support | No, application-level only | — |
| Open Source License | Apache 2.0 (Fully Open) | PostgreSQL License (permissive) |
| Enterprise Support SLA | Community-driven, no SLA | — |
| Index Type Options(count) | 2 (SQLite, DuckDB) | — |
| GPU Acceleration Support | No | — |
| Setup Time to First Query(minutes) | 2 minutes(winner) | 45 minutes |
| Deployment Model | PostgreSQL extension module | — |
| Integrated LLM Providers(count) | None (requires external integration) | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $150 | — |
| Free Tier Capacity(hits per month) | Unlimited (self-hosted) | — |
| Query Type Flexibility | Full SQL + vector operators | — |
| Operational Complexity (1-10 scale)(complexity score) | Very Low (2/10) | — |
| Native RESTful API | No (SQL-only via PostgreSQL client) | — |
CChroma
Ppgvector
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
—
Show 1 more attribute
API Query Language Support(count)
1 (SQL only)
—
Max Practical Vector Capacity(billion vectors)
0.1-1B (managed)
—
Maximum Vector Storage(Vectors)
~10M (single instance practical limit)
—
Maximum Vectors at Scale(millions)
Limited to hardware (~1B)
—
Maximum Practical Dataset Size(petabytes)
~10 million
—
Maximum Vectors Per Instance(vectors)
~10M
—
Show 7 more attributes
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
—
Maximum Recommended Vector Count(millions)
~10M vectors
—
Maximum Vector Capacity (single instance)(millions of vectors)
10 million
—
Maximum Scalability (distributed nodes)(nodes)
1-3 (read replicas)
—
Query Latency (1M vectors, CPU)(milliseconds)
50-200ms
—
GPU Acceleration
Not available
—
Query Latency (p99)(milliseconds)
50-200ms
100-500ms
Query Latency (1M vectors)(ms)
10-50 ms
—
Query Latency (1M vectors, single query)(milliseconds)
150-300ms
—
Show 17 more attributes
Minimum Deployment Size(megabytes)
50
—
Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds)
~50ms
~120ms
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
30-50ms
Query Latency (1M vectors, p99)(milliseconds)
~350ms
—
Query Latency at 1M vectors(milliseconds)
50-150ms
—
p50 Query Latency (Global)(milliseconds)
250ms (cloud-hosted)
—
Average Latency (1M vectors)(milliseconds)
75ms
55ms
Indexing Methods Supported(count)
2 methods (IVFFlat, HNSW)
—
Average Query Latency (1M vectors, 384-dim)(milliseconds)
120ms
—
Average Query Latency (P99)(milliseconds)
100-300ms
—
Vector Indexing Algorithm Options(count)
HNSW, IVFFlat
—
Scalability Limit (Single Node)(million vectors)
10-50 before latency issues
—
Hosting Flexibility
Managed cloud + local/open-source
—
Deployment Options
Embedded, Python, Serverless (SaaS beta)
—
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 6 more attributes
Monthly Base Cost (starter tier)(USD)
$0 (open-source)
—
Managed Cloud Cost (1M queries/month)(USD)
$50-150
$20-80 (AWS RDS)
Storage Cost (1M vectors, 1536-dim)(USD per month)
$0
—
Cost for 1M Monthly Read Operations(USD)
$0 (self-hosted only)
—
Production Starter Cost(USD/month)
$0 (infra only)
—
Cost for 1M Vectors/Month(USD)
$10-50
—
Uptime SLA(percent)
No SLA (community support)
Self-managed (varies)
Uptime Guarantee(%)
No SLA
—
ACID Compliance
No
Yes (full support)
Uptime SLA Guarantee(percent)
User dependent (no SLA)
—
Setup Time (Local Development)(Minutes)
2-5 (pip install + Python)
—
Setup Time (Minutes)(minutes)
15-30
—
Learning Curve (Hours)(hours)
2-4
—
Setup Time (local environment)(minutes)
2-3 minutes
—
Metadata Filter Complexity(operators supported)
Basic ($where)
—
Embedded Tokenizer Support
Yes (6+ models included)
—
Metadata Filtering Support
Native (full SQL-like support)
Limited (SQL WHERE clauses only)
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)
No (external only)
Supported Index Types(count)
Heuristic Search Algorithm (HNSW)
2 (HNSW, IVFFlat)
Hybrid Search Support (BM25 + Vector)
No
—
Multi-Tenancy Support
Not supported
Requires schema/RLS workarounds
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
—
Filtering Query Support(complexity level)
Basic metadata matching
—
Built-in Embedding Model Support
OpenAI, Cohere, Hugging Face, Ollama (6+ providers)
—
Metadata Filtering Complexity(feature count)
Basic equality/contains
—
Vector Dimensionality Support(maximum dimensions)
Up to 2,000 dimensions
—
SQL Relational Query Integration(native support)
Yes (unified via SQL)
—
Setup Time to Production(minutes)
0.1 days (2-4 hours)
1-4 hours
Installation Complexity(shell commands)
5-10 minutes (Python package)
Integrated (no new deployment)
Supported Deployment Modes
In-process, SQLite, HTTP API
—
Minimum Setup Infrastructure
Python 3.7+; runs on laptop or serverless
—
Open Source Availability
Yes (Apache 2.0)
Yes (PostgreSQL License)
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
—
Setup Time(minutes)
5 minutes
30-120 minutes
Deployment Complexity(complexity score (1-10))
2/10
7/10
Setup to Production Time(hours)
2-4
—
Infrastructure Required
PostgreSQL instance (AWS RDS, self-hosted, etc.)
—
Production Observability
Basic logging
—
SQL Filtering Capability
JSON metadata filters (limited)
Full SQL WHERE clauses (unlimited)
Native SQL Support
Limited (metadata filtering only)
Full SQL with vector operators
GitHub Stars (as of 2026)(stars)
12,000+ stars
~10,500
GitHub Stars(stars)
12,500+
~10,800
GitHub Stars (2026)(stars)
12,000+
9,500+
GitHub Community Stars(stars)
4,200+ stars
—
Time to First Query(minutes)
1-2 minutes
45-120 minutes
Memory Footprint (at rest, 1M vectors)(MB)
~800MB
—
Number of Supported Languages(languages)
Python + JavaScript
—
Kubernetes-Native Deployment
Not recommended; in-process only
—
Complex Metadata Filtering Support
Basic equality/contains only
—
Supported Programming Languages(languages)
Python, JavaScript, Go, Rust
—
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
120-300 minutes
Production Deployments Reported(count)
500+
—
Initial Setup Time(minutes)
2 minutes
—
Maximum Vector Capacity(vectors)
10M (single machine limit)
<1 billion (practical limit)
RBAC & Enterprise Security(yes/no)
No
—
Supported Vector Dimensions(dimensions)
Unlimited
—
Maximum Supported Vector Dimensions(dimensions)
2048
2000+
Relational Data Integration
No (requires external database)
Native (single database)
Maximum Vector Dimensions(dimensions)
Unlimited
2,000
SQL Query Support
No (metadata filters only)
Yes (full SQL support)
Show 1 more attribute
Supported Indexing Algorithms(count)
HNSW, IVFFlat, Exact
—
LangChain Integration Native Support
Yes, official integration
—
Embedding Auto-Generation
Yes (Hugging Face, OpenAI, etc.)
No (external preprocessing required)
Primary Indexing Algorithm(algorithm type)
Flat, approximate nearest neighbor
—
Time to Production (First Query)(minutes)
7 minutes
—
Advanced Filtering Support
Basic metadata filters only
—
Multi-Tenancy
Not supported
—
Native Multi-tenancy Support
No, application-level only
—
Open Source License
Apache 2.0 (Fully Open)
PostgreSQL License (permissive)
Enterprise Support SLA
Community-driven, no SLA
—
Index Type Options(count)
2 (SQLite, DuckDB)
—
GPU Acceleration Support
No
—
Setup Time to First Query(minutes)
2 minutes
45 minutes
Deployment Model
PostgreSQL extension module
—
Integrated LLM Providers(count)
None (requires external integration)
—
Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD)
$150
—
Free Tier Capacity(hits per month)
Unlimited (self-hosted)
—
Query Type Flexibility
Full SQL + vector operators
—
Operational Complexity (1-10 scale)(complexity score)
Very Low (2/10)
—
Native RESTful API
No (SQL-only via PostgreSQL client)
—
Pros & Cons
10 pros·6 cons across both
C
P
C
Chroma
+5-3
Pros
- Fastest setup time with pip install in under 2 minutes
- Supports unlimited vector dimensions for flexibility
- Built-in metadata filtering without SQL
- Excellent for RAG (Retrieval-Augmented Generation) pipelines
- 12,000+ GitHub stars indicating strong community adoption
Cons
- No SQL support limits complex multi-table queries
- Less mature than PostgreSQL for production-scale deployments
- Cannot combine vector results with traditional business logic queries
P
pgvector
+5-3
Pros
- Full SQL integration for complex hybrid queries combining vectors and structured data
- 30-80ms latency for 1M vector searches outperforms Chroma
- ACID compliance and transaction support for data integrity
- Leverages existing PostgreSQL infrastructure and expertise
- Supports 2,000+ vector dimensions sufficient for most LLM embeddings
Cons
- Requires PostgreSQL installation and PostgreSQL expertise to maintain
- 2,000 dimension limit restricts some advanced embedding models
- Higher operational complexity than standalone solutions
Frequently Asked Questions
5 questions
Yes, some architectures use Chroma as a cache layer for fast vector retrieval and pgvector as the authoritative store. However, this adds complexity. Most applications choose one based on whether relational data integration is required.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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