vioscaleAI
Comparison

KDB.AI vs Qdrant

On the evidence we track, Qdrant leads this comparison with a composite score of 56/100. Scores are only directly comparable because these tools share a category; the full breakdown and every source is below.

KDB.AI full profile →Qdrant full profile →
Machine formatsMarkdownJSONGraphQLMarkdown is open. JSON and GraphQL need a free key.
KDB.AI47
★Qdrant56
Score
vioscaleAI score
KDB.AI47 / 100low · 24%
Qdrant56 / 100medium · 65%
Pricing
Free tier
KDB.AI✓
Qdrant✓
Model
Price level
KDB.AIlow
Qdrantlow
Transparent
KDB.AI✗
Qdrant✓
Integrations
Count
KDB.AI5
Qdrant4
Security
Disclosure policy
KDB.AI✓
Qdrant–
Gdpr
KDB.AI–
Qdrant✓
Hipaa
KDB.AI–
Qdrant✓
Soc2
KDB.AI–
Qdrant✓
Trust center
Vulnerabilities
Adoption
Dependent repos
KDB.AI–
Qdrant0
Github stars
KDB.AI–
Qdrant35,060
Activity
Commits last 30d
KDB.AI–
Qdrant100
Release
Cadence days
KDB.AI–
Qdrant18
History
KDB.AI–
Qdrant20 items
License
Spdx
KDB.AI–
QdrantApache-2.0
Language
Primary
KDB.AI–
QdrantRust
Market
Availability
KDB.AI–
QdrantAvailable worldwide
Content
Faq
KDB.AI4 items
Qdrant7 items

Capabilities

Feature-by-feature on the axes that matter for vector databases. “-” means undocumented, not absent.

Deployment
Deployment
KDB.AIManaged
QdrantSelf-hosted
Serverless / consumption pricing
KDB.AI-
Qdrant-
Index
ANN index (HNSW / IVF / DiskANN / ScaNN / flat)
KDB.AIFlat, qFlat, IVF, IVFPQ, HNSW, qHnsw
QdrantHNSW
Distance metrics (cosine / dot / Euclidean)
KDB.AIEuclidean, Inner-Product, Cosine
Qdrantdot product
Query
Metadata filtering
KDB.AIPre-filter
Qdrant-
Hybrid search (dense + sparse fusion)
KDB.AI✓
Qdrant✓
Built-in BM25 / lexical
KDB.AI✓
Qdrant✓
Storage
Quantization
KDB.AIProduct
QdrantScalar
On-disk vs in-memory
KDB.AIDisk-backed
QdrantDisk-backed
Scale
Multi-tenancy (namespaces / collections / tenants)
KDB.AI✓
Qdrant✓
Horizontal scale
KDB.AISharded / distributed
QdrantSharded / distributed
Consistency
Consistency
KDB.AI-
Qdrant-
Licensing
Licence class
KDB.AI-
QdrantSource-available
Classification
Purpose-built vs feature-of-parent
KDB.AIDedicated DBMS
QdrantDedicated DBMS

What each one is

The product in its own terms, so the numbers below have context.

KDB.AI

KDB.AI is a specialized vector database enabling developers to build AI-powered applications by integrating vector embeddings with structured time-series data. It supports hybrid search, multimodal retrieval, and real-time analytics for generative AI and RAG applications.

Independently observed

Qdrant

Leader

A database system designed for storing and searching vectors with comprehensive filtering capabilities. It provides production-ready APIs and tools for semantic similarity matching and embedding-based applications.

Independently observed

Pricing

List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.

KDB.AI

HybridFree tier

Free tier available; enterprise pricing via contact sales

  • FreeFree
as of verify ↗

Qdrant

Leader
Usage-basedFree tier

Usage-based pricing billed monthly based on CPU, memory, and disk storage consumption. Free tier available.

as of verify ↗

Platform & deployment

Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.

Platforms
Web
KDB.AI–
Qdrant✓
Deployment
Cloud / SaaS
KDB.AI✓
Qdrant✓
Self-hosted
KDB.AI✗
Qdrant✓
On-premise
KDB.AI✓
Qdrant✓

Integrations

What each product connects to. Counts come from the vendor's own integration directory where one exists.

In common (1)
  • Langchain

KDB.AI

8 total - 7 not shared
  • Hugging Face
  • NVIDIA cuVS
  • LlamaIndex
  • Voyage AI
  • TwelveLabs
  • Google Colab
  • HuggingFace
Independently observed

Qdrant

Leader
14 total - 13 not shared
  • Slack
  • Adobe
  • Hubspot
  • Arize
  • Google DeepMind
  • Qualcomm
  • Cohere
  • AWS
  • GCP
  • Azure
  • FastEmbed
  • Google Cloud Storage
  • Azure Blob Storage
Independently observed
Still deciding?

KDB.AI or Qdrant: which one depends on you

A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.

Free to run, no account needed to start. How the evaluation works

Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.