Chroma vs Qdrant
type: Article
description: 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.
language: en-US
date published: 2026-07-09T11:28:02.560Z
date created: 2026-07-09T11:28:02.560Z
date modified: 2026-07-09T11:28:02.611Z
content reference time: 2026-07-09T11:28:02.611Z
author: A Versus B
publisher: A Versus B
about: Chroma
about: Qdrant
abstract: 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.
text: Maximum Vectors Supported. Queries Per Second (QPS) at Scale. Setup Complexity. Production Deployment Model. Hybrid Search Support. Memory Efficiency per Vector. Built-in Access Control
conclusion text: Choose 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.
additional property: Startup Time to First Query
  type: PropertyValue
  value: Chroma: 5 minutes | Qdrant: N/A
additional property: Max Practical Vector Capacity
  type: PropertyValue
  value: Chroma: 0.1-1B (managed) | Qdrant: N/A
additional property: Query Latency (1M vectors, CPU)
  type: PropertyValue
  value: Chroma: 50-200ms | Qdrant: N/A
additional property: GPU Acceleration
  type: PropertyValue
  value: Chroma: Not available | Qdrant: N/A
additional property: Hosting Flexibility
  type: PropertyValue
  value: Chroma: Managed cloud + local/open-source | Qdrant: N/A
additional property: Learning Curve (hours for LLM RAG)
  type: PropertyValue
  value: Chroma: 0.5-2 hours | Qdrant: N/A
additional property: Production Users at Scale
  type: PropertyValue
  value: Chroma: 500+ | Qdrant: N/A
additional property: Monthly Starting Cost
  type: PropertyValue
  value: Chroma: $0 (free, open-source) | Qdrant: N/A
additional property: Maximum Vector Storage
  type: PropertyValue
  value: Chroma: ~10M (single instance practical limit) | Qdrant: N/A
additional property: Maximum Vector Dimensions
  type: PropertyValue
  value: Chroma: Unlimited (backend dependent) | Qdrant: Unlimited (100K+ tested)
additional property: Query Latency (p99)
  type: PropertyValue
  value: Chroma: 50-200ms | Qdrant: 20-40ms (self-hosted)
  value reference: Qdrant
additional property: Uptime SLA
  type: PropertyValue
  value: Chroma: No SLA (community support) | Qdrant: Self-hosted (varies), Managed 99.5%
additional property: Setup Time (Local Development)
  type: PropertyValue
  value: Chroma: 2-5 (pip install + Python) | Qdrant: N/A
additional property: Cost at 10M Vectors/Month
  type: PropertyValue
  value: Chroma: $0 (self-hosted only) | Qdrant: N/A
additional property: Starting Cost (Annual)
  type: PropertyValue
  value: Chroma: $0 (free) | Qdrant: N/A
additional property: Maximum Vectors at Scale
  type: PropertyValue
  value: Chroma: Limited to hardware (~1B) | Qdrant: N/A
additional property: Uptime Guarantee
  type: PropertyValue
  value: Chroma: No SLA | Qdrant: N/A
additional property: Documentation Quality Score
  type: PropertyValue
  value: Chroma: 8/10 | Qdrant: N/A
additional property: Metadata Filter Complexity
  type: PropertyValue
  value: Chroma: Basic ($where) | Qdrant: N/A
additional property: Setup Time to Production
  type: PropertyValue
  value: Chroma: 0.1 days (2-4 hours) | Qdrant: N/A
additional property: Query Latency (1M vectors)
  type: PropertyValue
  value: Chroma: 10-50 ms | Qdrant: N/A
additional property: Embedded Tokenizer Support
  type: PropertyValue
  value: Chroma: Yes (6+ models included) | Qdrant: N/A
additional property: Metadata Filtering Support
  type: PropertyValue
  value: Chroma: Native (full SQL-like support) | Qdrant: N/A
additional property: GPU Support
  type: PropertyValue
  value: Chroma: Experimental/Limited | Qdrant: N/A
additional property: Memory Usage (10M vectors)
  type: PropertyValue
  value: Chroma: 3-5 GB | Qdrant: N/A
additional property: Query Latency (1M vectors, single query)
  type: PropertyValue
  value: Chroma: 150-300ms | Qdrant: 10-50ms
  value reference: Qdrant
additional property: Maximum Practical Dataset Size
  type: PropertyValue
  value: Chroma: ~10 million | Qdrant: Billions+
  value reference: Qdrant
additional property: Data Connectors
  type: PropertyValue
  value: Chroma: 0 (manual) | Qdrant: N/A
additional property: Setup Time
  type: PropertyValue
  value: Chroma: 5 minutes | Qdrant: 15-30 minutes
  value reference: Chroma
additional property: LLM Provider Support
  type: PropertyValue
  value: Chroma: External (0 native) | Qdrant: N/A
additional property: Minimum Deployment Size
  type: PropertyValue
  value: Chroma: 50 | Qdrant: N/A
additional property: Retrieval Strategy Types
  type: PropertyValue
  value: Chroma: 1 (similarity search) | Qdrant: N/A
additional property: Storage Backends
  type: PropertyValue
  value: Chroma: 3 (in-memory, SQLite, cloud) | Qdrant: N/A
additional property: Production Observability
  type: PropertyValue
  value: Chroma: Basic logging | Qdrant: N/A
additional property: Query Latency (1M vectors, 768-dim, 10th percentile)
  type: PropertyValue
  value: Chroma: ~50ms | Qdrant: N/A
additional property: Built-in Embedding Generation
  type: PropertyValue
  value: Chroma: Yes (OpenAI, HuggingFace, Ollama) | Qdrant: N/A
additional property: Installation Complexity
  type: PropertyValue
  value: Chroma: 5-10 minutes (Python package) | Qdrant: N/A
additional property: SQL Filtering Capability
  type: PropertyValue
  value: Chroma: JSON metadata filters (limited) | Qdrant: N/A
additional property: GitHub Stars (as of 2026)
  type: PropertyValue
  value: Chroma: 12,000+ stars | Qdrant: N/A
additional property: Supported Index Types
  type: PropertyValue
  value: Chroma: Heuristic Search Algorithm (HNSW) | Qdrant: N/A
additional property: Time to First Query
  type: PropertyValue
  value: Chroma: 1-2 minutes | Qdrant: 20 minutes
  value reference: Chroma
additional property: Memory Footprint (at rest, 1M vectors)
  type: PropertyValue
  value: Chroma: ~800MB | Qdrant: ~200MB
  value reference: Qdrant
additional property: Number of Supported Languages
  type: PropertyValue
  value: Chroma: Python + JavaScript | Qdrant: Python, JavaScript, Go, Java, Rust, C++, .NET
  value reference: Qdrant
additional property: Kubernetes-Native Deployment
  type: PropertyValue
  value: Chroma: Not recommended; in-process only | Qdrant: Yes; Helm charts, StatefulSet support
additional property: Complex Metadata Filtering Support
  type: PropertyValue
  value: Chroma: Basic equality/contains only | Qdrant: Nested fields, range, AND/OR/NOT, geo-spatial
additional property: Maximum Vectors Per Instance
  type: PropertyValue
  value: Chroma: ~10M | Qdrant: N/A
additional property: Average Query Latency
  type: PropertyValue
  value: Chroma: 10-50ms | Qdrant: N/A
additional property: Setup Time to First Query
  type: PropertyValue
  value: Chroma: 2-5 (pip install) | Qdrant: N/A
additional property: Hybrid Search Support (BM25 + Vector)
  type: PropertyValue
  value: Chroma: No | Qdrant: N/A
interactivity type: active
significant link: https://www.aversusb.net/entity/chroma
significant link: https://www.aversusb.net/alternatives/chroma
significant link: https://www.aversusb.net/entity/qdrant
significant link: https://www.aversusb.net/alternatives/qdrant
significant link: https://www.aversusb.net/api/answer/chroma-vs-qdrant))
significant link: https://www.aversusb.net/api/v1/schema/chroma-vs-qdrant))
related link: https://www.aversusb.net/compare/chroma-vs-qdrant
related link: https://www.aversusb.net/compare/chroma-vs-qdrant)
related link: https://www.aversusb.net/compare/pinecone-vs-qdrant
related link: https://www.aversusb.net/compare/pinecone-vs-chroma
related link: https://www.aversusb.net/compare/chroma-vs-pinecone
related link: https://www.aversusb.net/compare/chroma-vs-faiss
related link: https://www.aversusb.net/compare/chroma-vs-llamaindex
related link: https://www.aversusb.net/compare/chroma-vs-pgvector
teaches: How to choose between Chroma and Qdrant
genre: Software

primaryImage
type: ImageObject
width: 1200
height: 630
caption: Chroma vs Qdrant — Side-by-side comparison on A Versus B

A Versus B
type: Organization

Chroma
type: SoftwareApplication
language: en-US
description: Open-source and managed vector database designed for LLM applications with simple Python API
subject of: Chroma vs Qdrant

Qdrant
type: SoftwareApplication
language: en-US
description: Enterprise-grade vector database designed for production scale with 1B+ vector capacity and advanced retrieval features
subject of: Chroma vs Qdrant

Chroma vs Qdrant — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Chroma vs Qdrant. Contains 141 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
distribution: JSON-LD knowledge graph
  type: DataDownload
  encoding format: application/json+ld
distribution: Raw comparison JSON
  type: DataDownload
  encoding format: application/json
distribution: Structured FAQ pairs JSON
  type: DataDownload
  encoding format: application/json
distribution: AI Answer API (citation-ready)
  type: DataDownload
  encoding format: application/json
distribution: Related comparisons JSON
  type: DataDownload
  encoding format: application/json
variable measured: Startup Time to First Query
  type: PropertyValue
  value reference: Developer Experience
variable measured: Max Practical Vector Capacity
  type: PropertyValue
  value reference: Scalability
variable measured: Query Latency (1M vectors, CPU)
  type: PropertyValue
  value reference: Performance
variable measured: GPU Acceleration
  type: PropertyValue
  value reference: Performance
variable measured: Hosting Flexibility
  type: PropertyValue
  value reference: Infrastructure
variable measured: Learning Curve (hours for LLM RAG)
  type: PropertyValue
  value reference: Developer Experience
variable measured: Production Users at Scale
  type: PropertyValue
  value reference: Market Adoption
variable measured: Monthly Starting Cost
  type: PropertyValue
  value reference: Pricing
variable measured: Maximum Vector Storage
  type: PropertyValue
  value reference: Scalability
variable measured: Maximum Vector Dimensions
  type: PropertyValue
  value reference: Scalability
variable measured: Query Latency (p99)
  type: PropertyValue
  value reference: Performance
variable measured: Uptime SLA
  type: PropertyValue
  value reference: Reliability
variable measured: Setup Time (Local Development)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Cost at 10M Vectors/Month
  type: PropertyValue
  value reference: Pricing
variable measured: Starting Cost (Annual)
  type: PropertyValue
  value reference: Pricing
variable measured: Maximum Vectors at Scale
  type: PropertyValue
  value reference: Scalability
variable measured: Uptime Guarantee
  type: PropertyValue
  value reference: Reliability
variable measured: Documentation Quality Score
  type: PropertyValue
  value reference: Developer Experience
variable measured: Metadata Filter Complexity
  type: PropertyValue
  value reference: Features
variable measured: Setup Time to Production
  type: PropertyValue
  value reference: Deployment
variable measured: Query Latency (1M vectors)
  type: PropertyValue
  value reference: Performance
variable measured: Embedded Tokenizer Support
  type: PropertyValue
  value reference: Features
variable measured: Metadata Filtering Support
  type: PropertyValue
  value reference: Features
variable measured: GPU Support
  type: PropertyValue
  value reference: Hardware
variable measured: Memory Usage (10M vectors)
  type: PropertyValue
  value reference: Resource Usage
variable measured: Query Latency (1M vectors, single query)
  type: PropertyValue
  value reference: Performance
variable measured: Maximum Practical Dataset Size
  type: PropertyValue
  value reference: Scalability
variable measured: Data Connectors
  type: PropertyValue
  value reference: Integration
variable measured: Setup Time
  type: PropertyValue
  value reference: Ease of Use
variable measured: LLM Provider Support
  type: PropertyValue
  value reference: Integration
variable measured: Minimum Deployment Size
  type: PropertyValue
  value reference: Performance
variable measured: Retrieval Strategy Types
  type: PropertyValue
  value reference: Features
variable measured: Storage Backends
  type: PropertyValue
  value reference: Features
variable measured: Production Observability
  type: PropertyValue
  value reference: DevOps Features
variable measured: Query Latency (1M vectors, 768-dim, 10th percentile)
  type: PropertyValue
  value reference: Performance
variable measured: Built-in Embedding Generation
  type: PropertyValue
  value reference: Features
variable measured: Installation Complexity
  type: PropertyValue
  value reference: Deployment
variable measured: SQL Filtering Capability
  type: PropertyValue
  value reference: Querying
variable measured: GitHub Stars (as of 2026)
  type: PropertyValue
  value reference: Community & Adoption
variable measured: Supported Index Types
  type: PropertyValue
  value reference: Features
variable measured: Time to First Query
  type: PropertyValue
  value reference: Setup Speed
variable measured: Memory Footprint (at rest, 1M vectors)
  type: PropertyValue
  value reference: Resource Efficiency
variable measured: Number of Supported Languages
  type: PropertyValue
  value reference: Compatibility
variable measured: Kubernetes-Native Deployment
  type: PropertyValue
  value reference: Enterprise
variable measured: Complex Metadata Filtering Support
  type: PropertyValue
  value reference: Functionality
variable measured: Maximum Vectors Per Instance
  type: PropertyValue
  value reference: Scalability
variable measured: Average Query Latency
  type: PropertyValue
  value reference: Performance
variable measured: Setup Time to First Query
  type: PropertyValue
  value reference: Ease of Use
variable measured: Hybrid Search Support (BM25 + Vector)
  type: PropertyValue
  value reference: Features
variable measured: Multi-Tenancy Support
  type: PropertyValue
  value reference: Features
variable measured: Minimum Memory for 1M Vectors
  type: PropertyValue
  value reference: Resource Requirements
variable measured: Supported Deployment Modes
  type: PropertyValue
  value reference: Deployment
variable measured: Setup Time (first query)
  type: PropertyValue
  value reference: Developer Experience
variable measured: Max Recommended Vector Count
  type: PropertyValue
  value reference: Scalability
variable measured: Query Filtering Support
  type: PropertyValue
  value reference: Features
variable measured: Multi-Modal Search
  type: PropertyValue
  value reference: Features
variable measured: Kubernetes Support
  type: PropertyValue
  value reference: Container Orchestration
variable measured: Minimum Setup Infrastructure
  type: PropertyValue
  value reference: Deployment
variable measured: LangChain Integration Maturity
  type: PropertyValue
  value reference: Ecosystem
variable measured: Maximum Vector Scale
  type: PropertyValue
  value reference: Performance
variable measured: Minimum Setup Time
variable measured: Hybrid Search (Vector + Keyword)
variable measured: Multi-modal Support
variable measured: Enterprise Features (RBAC/Multi-tenancy)
variable measured: GitHub Stars
variable measured: Deployment Options
variable measured: Setup Time (minutes)
variable measured: Supported Data Sources
variable measured: Query Latency (P95)
is based on: https://www.aversusb.net/compare/chroma-vs-qdrant))

Chroma vs Qdrant — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Chroma vs Qdrant.
has defined term: Developer Experience
has defined term: Scalability
has defined term: Performance
has defined term: Infrastructure
has defined term: Market Adoption
has defined term: Pricing
has defined term: Reliability
has defined term: Ease of Use
has defined term: Features
has defined term: Deployment
has defined term: Hardware
has defined term: Resource Usage
has defined term: Integration
has defined term: DevOps Features
has defined term: Querying
has defined term: Community & Adoption
has defined term: Setup Speed
has defined term: Resource Efficiency
has defined term: Compatibility
has defined term: Enterprise
has defined term: Functionality
has defined term: Resource Requirements
has defined term: Container Orchestration
has defined term: Ecosystem
has defined term: User Experience
has defined term: Community
has defined term: Adoption
has defined term: Operational Efficiency
has defined term: Performance & Scale
has defined term: Security
has defined term: Capability
has defined term: Integrations
has defined term: Feature
has defined term: Licensing
has defined term: Technology
has defined term: Setup & Deployment
has defined term: Query Capabilities
has defined term: Enterprise Features
has defined term: Support
has defined term: Flexibility
has defined term: Hardware Optimization
has defined term: Cost & Licensing
has defined term: Cost & Business
has defined term: Performance & Scalability
has defined term: AI Features
has defined term: Capabilities
has defined term: Efficiency

Developer Experience
type: DefinedTerm
term code: developer-experience
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Performance
type: DefinedTerm
term code: performance
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Infrastructure
type: DefinedTerm
term code: infrastructure
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Market Adoption
type: DefinedTerm
term code: market-adoption
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Pricing
type: DefinedTerm
term code: pricing
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Reliability
type: DefinedTerm
term code: reliability
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Ease of Use
type: DefinedTerm
term code: ease-of-use
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Hardware
type: DefinedTerm
term code: hardware
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Resource Usage
type: DefinedTerm
term code: resource-usage
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

DevOps Features
type: DefinedTerm
term code: devops-features
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Querying
type: DefinedTerm
term code: querying
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Community & Adoption
type: DefinedTerm
term code: community-&-adoption
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Setup Speed
type: DefinedTerm
term code: setup-speed
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Resource Efficiency
type: DefinedTerm
term code: resource-efficiency
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Enterprise
type: DefinedTerm
term code: enterprise
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Functionality
type: DefinedTerm
term code: functionality
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Resource Requirements
type: DefinedTerm
term code: resource-requirements
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Container Orchestration
type: DefinedTerm
term code: container-orchestration
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

User Experience
type: DefinedTerm
term code: user-experience
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Adoption
type: DefinedTerm
term code: adoption
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Operational Efficiency
type: DefinedTerm
term code: operational-efficiency
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Performance & Scale
type: DefinedTerm
term code: performance-&-scale
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Security
type: DefinedTerm
term code: security
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Capability
type: DefinedTerm
term code: capability
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Integrations
type: DefinedTerm
term code: integrations
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Feature
type: DefinedTerm
term code: feature
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Licensing
type: DefinedTerm
term code: licensing
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Technology
type: DefinedTerm
term code: technology
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Setup & Deployment
type: DefinedTerm
term code: setup-&-deployment
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Query Capabilities
type: DefinedTerm
term code: query-capabilities
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Enterprise Features
type: DefinedTerm
term code: enterprise-features
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Support
type: DefinedTerm
term code: support
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Flexibility
type: DefinedTerm
term code: flexibility
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Hardware Optimization
type: DefinedTerm
term code: hardware-optimization
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Cost & Licensing
type: DefinedTerm
term code: cost-&-licensing
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Cost & Business
type: DefinedTerm
term code: cost-&-business
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Performance & Scalability
type: DefinedTerm
term code: performance-&-scalability
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

AI Features
type: DefinedTerm
term code: ai-features
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Capabilities
type: DefinedTerm
term code: capabilities
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

Efficiency
type: DefinedTerm
term code: efficiency
in defined term set: Chroma vs Qdrant — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T11:28:02.611Z
author: A Versus B
publisher: A Versus B
is part of: chroma-vs-qdrant))
main entity: Which vector database should I use for a startup MVP?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Can Chroma handle enterprise-scale datasets?
  type: Question
  accepted answer: Answer
    text: Chroma can manage 10-50M vectors but degrades significantly beyond that, hitting latency bottlenecks and QPS limits (500-2,000 max). Enterprise applications with 100M+ vectors should use Qdrant, which is specifically architected for scale with 10,000+ QPS capacity and sub-100ms latency.
    language: en-US
main entity: Does Chroma support hybrid search (keyword + semantic)?
  type: Question
  accepted answer: Answer
    text: Chroma doesn't natively support hybrid search. You must integrate it with external tools like Elasticsearch or BM25 implementations. Qdrant includes native hybrid search with built-in BM25 ranking, making it production-ready for applications needing both semantic and keyword matching.
    language: en-US
main entity: What are the deployment differences?
  type: Question
  accepted answer: Answer
    text: Chroma supports in-process mode (single Python process), serverless (AWS Lambda, Vercel), and managed cloud (Chroma Cloud). Qdrant requires Docker/Kubernetes for self-hosting or uses their managed cloud. For simplicity, Chroma wins; for control and enterprise integrations, Qdrant is more flexible.
    language: en-US
main entity: Which has better open-source support?
  type: Question
  accepted answer: Answer
    text: Chroma uses the MIT license (fully open), while Qdrant uses BUSL-1.1 (business source license), transitioning to open after 4 years. Chroma is more permissive for commercial use without restrictions. Both have active GitHub communities, but Chroma has broader adoption in startups.
    language: en-US

How to Choose Between Chroma and Qdrant
type: HowTo
description: 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.
language: en-US
total time: PT3M
step: Consider: Maximum Vectors Supported
step: Consider: Queries Per Second (QPS) at Scale
step: Consider: Setup Complexity
step: Consider: Production Deployment Model
step: Consider: Hybrid Search Support
step: Consider: Memory Efficiency per Vector
step: Consider: Built-in Access Control
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose 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.

Consider: Maximum Vectors Supported
type: HowToStep
position: 1
text: Maximum Vectors Supported: Chroma has ~10-50M vectors, while Qdrant has 1B+ vectors.

Consider: Queries Per Second (QPS) at Scale
type: HowToStep
position: 2
text: Queries Per Second (QPS) at Scale: Chroma has 500-2,000 QPS, while Qdrant has 10,000+ QPS.

Consider: Setup Complexity
type: HowToStep
position: 3
text: Setup Complexity: Chroma has 5 minutes (Python pip install), while Qdrant has 15-30 minutes (Docker/Kubernetes).

Consider: Production Deployment Model
type: HowToStep
position: 4
text: Production Deployment Model: Chroma has In-process, serverless, or managed cloud, while Qdrant has Self-hosted, cloud, or managed SaaS.

Consider: Hybrid Search Support
type: HowToStep
position: 5
text: Hybrid Search Support: Chroma has Keyword search via integrations, while Qdrant has Native hybrid search with BM25 ranking.

Consider: Memory Efficiency per Vector
type: HowToStep
position: 6
text: Memory Efficiency per Vector: Chroma has ~100-200 bytes overhead, while Qdrant has ~50-100 bytes overhead.

Consider: Built-in Access Control
type: HowToStep
position: 7
text: Built-in Access Control: Chroma has API key only, while Qdrant has RBAC, API keys, OAuth2 support.

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Chroma vs Qdrant
review body: 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.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Chroma vs Qdrant

Multi-Tenancy Support
type: PropertyValue
value: Chroma: Not supported | Qdrant: N/A

Minimum Memory for 1M Vectors
type: PropertyValue
value: Chroma: 1-2GB | Qdrant: N/A

Supported Deployment Modes
type: PropertyValue
value: Chroma: In-process, SQLite, HTTP API | Qdrant: N/A

Setup Time (first query)
type: PropertyValue
value: Chroma: 2-5 | Qdrant: N/A

Max Recommended Vector Count
type: PropertyValue
value: Chroma: 1-10M (single node) | Qdrant: N/A

Query Filtering Support
type: PropertyValue
value: Chroma: Basic metadata filters | Qdrant: N/A

Multi-Modal Search
type: PropertyValue
value: Chroma: Text embeddings only | Qdrant: N/A

Kubernetes Support
type: PropertyValue
value: Chroma: Not native; runs as Python process | Qdrant: N/A

Minimum Setup Infrastructure
type: PropertyValue
value: Chroma: Python 3.7+; runs on laptop or serverless | Qdrant: N/A

LangChain Integration Maturity
type: PropertyValue
value: Chroma: Official, first-class integration | Qdrant: N/A

Maximum Vector Scale
type: PropertyValue
value: Chroma: 10-50 million | Qdrant: N/A

Minimum Setup Time
type: PropertyValue
value: Chroma: 2-5 minutes | Qdrant: N/A

Hybrid Search (Vector + Keyword)
type: PropertyValue
value: Chroma: No | Qdrant: N/A

Multi-modal Support
type: PropertyValue
value: Chroma: Text only | Qdrant: N/A

Enterprise Features (RBAC/Multi-tenancy)
type: PropertyValue
value: Chroma: No | Qdrant: N/A

GitHub Stars
type: PropertyValue
value: Chroma: 12,500+ | Qdrant: 28,000+ stars
value reference: Qdrant

Deployment Options
type: PropertyValue
value: Chroma: Embedded, Python, Serverless (SaaS beta) | Qdrant: Self-hosted + managed cloud

Setup Time (minutes)
type: PropertyValue
value: Chroma: 15-30 | Qdrant: N/A

Supported Data Sources
type: PropertyValue
value: Chroma: 12 embedding models | Qdrant: N/A

Query Latency (P95)
type: PropertyValue
value: Chroma: 45-120 | Qdrant: N/A

Maximum Embeddings
type: PropertyValue
value: Chroma: 50M (in-memory) | Qdrant: N/A

GitHub Stars (2026)
type: PropertyValue
value: Chroma: 12,500 | Qdrant: N/A

LLM Integration
type: PropertyValue
value: Chroma: Manual (requires wrapper code) | Qdrant: N/A

Learning Curve (Hours)
type: PropertyValue
value: Chroma: 2-4 | Qdrant: N/A

Production Deployments Reported
type: PropertyValue
value: Chroma: 500+ | Qdrant: N/A

Initial Setup Time
type: PropertyValue
value: Chroma: 2 minutes | Qdrant: N/A

Minimum Monthly Cost
type: PropertyValue
value: Chroma: $0 (open-source) | Qdrant: N/A

Production Plan Cost
type: PropertyValue
value: Chroma: $0 (self-hosted infrastructure only) | Qdrant: N/A

Maximum Vector Capacity
type: PropertyValue
value: Chroma: 10M (single machine limit) | Qdrant: 1B+
value reference: Qdrant

Query Latency (p99) at 100M Vectors
type: PropertyValue
value: Chroma: Not tested (infeasible) | Qdrant: N/A

REST API Support
type: PropertyValue
value: Chroma: No (client libraries only) | Qdrant: N/A

RBAC & Enterprise Security
type: PropertyValue
value: Chroma: No | Qdrant: N/A

Maximum Vectors Per Index
type: PropertyValue
value: Chroma: ~10 million | Qdrant: N/A

Query Latency (p50, local/optimal)
type: PropertyValue
value: Chroma: 5-20ms | Qdrant: N/A

Monthly Base Cost (starter tier)
type: PropertyValue
value: Chroma: $0 (open-source) | Qdrant: N/A

Supported Vector Dimensions
type: PropertyValue
value: Chroma: Unlimited | Qdrant: N/A

LangChain Integration Native Support
type: PropertyValue
value: Chroma: Yes, official integration | Qdrant: N/A

Single-Vector Search Latency (1M vectors)
type: PropertyValue
value: Chroma: 15-25ms | Qdrant: N/A

Maximum Supported Vector Dimensions
type: PropertyValue
value: Chroma: 2048 | Qdrant: N/A

Native SQL Support
type: PropertyValue
value: Chroma: Limited (metadata filtering only) | Qdrant: N/A

Embedding Auto-Generation
type: PropertyValue
value: Chroma: Yes (Hugging Face, OpenAI, etc.) | Qdrant: N/A

Open Source Availability
type: PropertyValue
value: Chroma: Yes (Apache 2.0) | Qdrant: N/A

Managed Cloud Cost (1M queries/month)
type: PropertyValue
value: Chroma: $50-150 | Qdrant: N/A

Relational Data Integration
type: PropertyValue
value: Chroma: No (requires external database) | Qdrant: N/A

Query Latency (1M vectors, p99)
type: PropertyValue
value: Chroma: ~350ms | Qdrant: ~75ms
value reference: Qdrant

Maximum Recommended Vectors
type: PropertyValue
value: Chroma: 50-100M | Qdrant: Unlimited (billions with clustering)
value reference: Qdrant

Setup Time (local environment)
type: PropertyValue
value: Chroma: 2-3 minutes | Qdrant: 15-20 minutes (with Docker)
value reference: Chroma

Supported Embedding Dimensions
type: PropertyValue
value: Chroma: Up to 2048 | Qdrant: Up to 65536
value reference: Qdrant

Filtering Query Support
type: PropertyValue
value: Chroma: Basic metadata matching | Qdrant: Complex nested, geo, range, and boolean queries

Primary Indexing Algorithm
type: PropertyValue
value: Chroma: Flat, approximate nearest neighbor | Qdrant: HNSW, IVF-Flat, Product Quantization

Language/SDK Support
type: PropertyValue
value: Chroma: Python, JavaScript, Go | Qdrant: Python, JavaScript, TypeScript, Go, Rust, Java, .NET
value reference: Qdrant

Time to Production (First Query)
type: PropertyValue
value: Chroma: 7 minutes | Qdrant: N/A

Maximum Recommended Vector Count
type: PropertyValue
value: Chroma: ~10M vectors | Qdrant: N/A

Minimum RAM Requirement (Single Node)
type: PropertyValue
value: Chroma: 64 MB | Qdrant: N/A

Advanced Filtering Support
type: PropertyValue
value: Chroma: Basic metadata filters only | Qdrant: N/A

Multi-Tenancy
type: PropertyValue
value: Chroma: Not supported | Qdrant: N/A

Open Source License
type: PropertyValue
value: Chroma: Apache 2.0 (Fully Open) | Qdrant: AGPL-3.0 (with commercial license)

Enterprise Support SLA
type: PropertyValue
value: Chroma: Community-driven, no SLA | Qdrant: N/A

Setup Time (minutes to first working example)
type: PropertyValue
value: Chroma: 3 minutes | Qdrant: N/A

Maximum Vector Capacity (single instance)
type: PropertyValue
value: Chroma: 10 million | Qdrant: N/A

Query Latency at 1M vectors
type: PropertyValue
value: Chroma: 50-150ms | Qdrant: N/A

Memory per Million Vectors
type: PropertyValue
value: Chroma: 1.5-2.0 GB | Qdrant: N/A

Built-in Embedding Model Support
type: PropertyValue
value: Chroma: OpenAI, Cohere, Hugging Face, Ollama (6+ providers) | Qdrant: N/A

Index Type Options
type: PropertyValue
value: Chroma: 2 (SQLite, DuckDB) | Qdrant: N/A

GPU Acceleration Support
type: PropertyValue
value: Chroma: No | Qdrant: N/A

p50 Query Latency (Global)
type: PropertyValue
value: Chroma: 250ms (cloud-hosted) | Qdrant: N/A

Storage Cost (1M vectors, 1536-dim)
type: PropertyValue
value: Chroma: $0 | Qdrant: N/A

Metadata Filtering Complexity
type: PropertyValue
value: Chroma: Basic equality/contains | Qdrant: Advanced boolean/range queries

Supported Programming Languages
type: PropertyValue
value: Chroma: Python, JavaScript, Go, Rust | Qdrant: N/A

Pricing Model
type: PropertyValue
value: Chroma: N/A | Qdrant: Self-hosted free or managed from $25/mo

Estimated Monthly Cost at 100GB
type: PropertyValue
value: Chroma: N/A | Qdrant: $25-100 (managed cloud)

Vector Dimension Limit
type: PropertyValue
value: Chroma: N/A | Qdrant: 65,536

GitHub Stars/Community Size
type: PropertyValue
value: Chroma: N/A | Qdrant: 18,000+ stars

Self-Hosting Available
type: PropertyValue
value: Chroma: N/A | Qdrant: Yes (open-source)

SLA Uptime Guarantee
type: PropertyValue
value: Chroma: N/A | Qdrant: Varies by self-hosted setup

Query Latency (95th percentile)
type: PropertyValue
value: Chroma: N/A | Qdrant: 10-50 ms

Memory per 1M Vectors
type: PropertyValue
value: Chroma: N/A | Qdrant: 2-4 GB

Startup Time (empty instance)
type: PropertyValue
value: Chroma: N/A | Qdrant: 2-5 seconds

Built-in LLM Integrations
type: PropertyValue
value: Chroma: N/A | Qdrant: 0 (custom only)

Managed Cloud Base Price (monthly)
type: PropertyValue
value: Chroma: N/A | Qdrant: $10/month

Multi-modal Support (native)
type: PropertyValue
value: Chroma: N/A | Qdrant: 1 (vectors only)

Throughput (vectors/second insert)
type: PropertyValue
value: Chroma: N/A | Qdrant: 50,000-100,000

Monthly Cost (1M vectors, 768 dims)
type: PropertyValue
value: Chroma: N/A | Qdrant: $0 (self-hosted) or $25 (managed)

Time to Production
type: PropertyValue
value: Chroma: N/A | Qdrant: 30-120 minutes

Hybrid Search Support
type: PropertyValue
value: Chroma: N/A | Qdrant: Yes (dense + sparse)

API Compatibility
type: PropertyValue
value: Chroma: N/A | Qdrant: OpenAI API compatible + REST

Query Throughput (QPS)
type: PropertyValue
value: Chroma: N/A | Qdrant: 10,000+ QPS

Memory Overhead per Vector
type: PropertyValue
value: Chroma: N/A | Qdrant: 50-100 bytes

License Model
type: PropertyValue
value: Chroma: N/A | Qdrant: BUSL-1.1 + Cloud/Enterprise

Native Hybrid Search
type: PropertyValue
value: Chroma: N/A | Qdrant: Yes (BM25 included)

Multi-tenant RBAC Support
type: PropertyValue
value: Chroma: N/A | Qdrant: Full RBAC + OAuth2

Latency at 100M Vectors
type: PropertyValue
value: Chroma: N/A | Qdrant: 50-150ms

SpeakableSpecification
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Chroma — Wikipedia
type: CreativeWork

Qdrant — Wikipedia
type: CreativeWork

Compare Chroma vs Qdrant
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/chroma-vs-qdrant))
object: Chroma
object: Qdrant

Minimum Setup Time
type: PropertyValue
value reference: User Experience

Hybrid Search (Vector + Keyword)
type: PropertyValue
value reference: Features

Multi-modal Support
type: PropertyValue
value reference: Features

Enterprise Features (RBAC/Multi-tenancy)
type: PropertyValue
value reference: Features

GitHub Stars
type: PropertyValue
value reference: Community

Deployment Options
type: PropertyValue
value reference: Infrastructure

Setup Time (minutes)
type: PropertyValue
value reference: Ease of Use

Supported Data Sources
type: PropertyValue
value reference: Integration

Query Latency (P95)
type: PropertyValue
value reference: Performance

Maximum Embeddings
type: PropertyValue
value reference: Scalability

GitHub Stars (2026)
type: PropertyValue
value reference: Community

LLM Integration
type: PropertyValue
value reference: Features

Learning Curve (Hours)
type: PropertyValue
value reference: Ease of Use

Production Deployments Reported
type: PropertyValue
value reference: Adoption

Initial Setup Time
type: PropertyValue
value reference: Operational Efficiency

Minimum Monthly Cost
type: PropertyValue
value reference: Pricing

Production Plan Cost
type: PropertyValue
value reference: Pricing

Maximum Vector Capacity
type: PropertyValue
value reference: Performance & Scale

Query Latency (p99) at 100M Vectors
type: PropertyValue
value reference: Performance

REST API Support
type: PropertyValue
value reference: Integration

RBAC & Enterprise Security
type: PropertyValue
value reference: Security

Maximum Vectors Per Index
type: PropertyValue
value reference: Scalability

Query Latency (p50, local/optimal)
type: PropertyValue
value reference: Performance

Monthly Base Cost (starter tier)
type: PropertyValue
value reference: Pricing

Supported Vector Dimensions
type: PropertyValue
value reference: Capability

LangChain Integration Native Support
type: PropertyValue
value reference: Integrations

Single-Vector Search Latency (1M vectors)
type: PropertyValue
value reference: Performance

Maximum Supported Vector Dimensions
type: PropertyValue
value reference: Capability

Native SQL Support
type: PropertyValue
value reference: Querying

Embedding Auto-Generation
type: PropertyValue
value reference: Feature

Open Source Availability
type: PropertyValue
value reference: Licensing

Managed Cloud Cost (1M queries/month)
type: PropertyValue
value reference: Pricing

Relational Data Integration
type: PropertyValue
value reference: Capability

Query Latency (1M vectors, p99)
type: PropertyValue
value reference: Performance

Maximum Recommended Vectors
type: PropertyValue
value reference: Scalability

Setup Time (local environment)
type: PropertyValue
value reference: Ease of Use

Supported Embedding Dimensions
type: PropertyValue
value reference: Features

Filtering Query Support
type: PropertyValue
value reference: Features

Primary Indexing Algorithm
type: PropertyValue
value reference: Technology

Language/SDK Support
type: PropertyValue
value reference: Integration

Time to Production (First Query)
type: PropertyValue
value reference: Setup & Deployment

Maximum Recommended Vector Count
type: PropertyValue
value reference: Scalability

Minimum RAM Requirement (Single Node)
type: PropertyValue
value reference: Infrastructure

Advanced Filtering Support
type: PropertyValue
value reference: Query Capabilities

Multi-Tenancy
type: PropertyValue
value reference: Enterprise Features

Open Source License
type: PropertyValue
value reference: Licensing

Enterprise Support SLA
type: PropertyValue
value reference: Support

Setup Time (minutes to first working example)
type: PropertyValue
value reference: Developer Experience

Maximum Vector Capacity (single instance)
type: PropertyValue
value reference: Scalability

Query Latency at 1M vectors
type: PropertyValue
value reference: Performance

Memory per Million Vectors
type: PropertyValue
value reference: Resource Usage

Built-in Embedding Model Support
type: PropertyValue
value reference: Features

Index Type Options
type: PropertyValue
value reference: Flexibility

GPU Acceleration Support
type: PropertyValue
value reference: Hardware Optimization

p50 Query Latency (Global)
type: PropertyValue
value reference: Performance

Storage Cost (1M vectors, 1536-dim)
type: PropertyValue
value reference: Pricing

Metadata Filtering Complexity
type: PropertyValue
value reference: Features

Supported Programming Languages
type: PropertyValue
value reference: Functionality

Pricing Model
type: PropertyValue
value reference: Cost & Licensing

Estimated Monthly Cost at 100GB
type: PropertyValue
value reference: Cost & Business

Vector Dimension Limit
type: PropertyValue
value reference: Performance & Scalability

GitHub Stars/Community Size
type: PropertyValue
value reference: Community & Adoption

Self-Hosting Available
type: PropertyValue
value reference: Deployment

SLA Uptime Guarantee
type: PropertyValue
value reference: Reliability

Query Latency (95th percentile)
type: PropertyValue
value reference: Performance

Memory per 1M Vectors
type: PropertyValue
value reference: Resource Efficiency

Startup Time (empty instance)
type: PropertyValue
value reference: Deployment

Built-in LLM Integrations
type: PropertyValue
value reference: AI Features

Managed Cloud Base Price (monthly)
type: PropertyValue
value reference: Pricing

Multi-modal Support (native)
type: PropertyValue
value reference: Capabilities

Throughput (vectors/second insert)
type: PropertyValue
value reference: Performance

Monthly Cost (1M vectors, 768 dims)
type: PropertyValue
value reference: Pricing

Time to Production
type: PropertyValue
value reference: Deployment

Hybrid Search Support
type: PropertyValue
value reference: Features

API Compatibility
type: PropertyValue
value reference: Integration

Query Throughput (QPS)
type: PropertyValue
value reference: Performance & Scale

Memory Overhead per Vector
type: PropertyValue
value reference: Efficiency

License Model
type: PropertyValue
value reference: Licensing

Native Hybrid Search
type: PropertyValue
value reference: Features

Multi-tenant RBAC Support
type: PropertyValue
value reference: Security

Latency at 100M Vectors
type: PropertyValue
value reference: Performance & Scale