qdrant-client
qdrant-client is a Python SDK for interacting with Qdrant, a vector search engine. It supports local in-memory mode, server connections (REST/gRPC), and cloud deployments, with optional built-in embedding generation via FastEmbed or Qdrant Cloud.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | qdrant/qdrant-client |
| Owner | qdrant |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 1.3k |
| Forks | 245 |
| Open issues | 165 |
| Latest release | v1.18.0 (2026-05-11) |
| Last updated | 2026-06-26 |
| Source | https://github.com/qdrant/qdrant-client |
What qdrant-client is
Apache 2.0–licensed Python client providing type-hinted async/sync APIs for all Qdrant operations. Offers local mode (in-memory or file-backed), gRPC/REST transports, and optional Inference API integrations with ONNX-based FastEmbed or cloud models.
Get the qdrant-client source
Clone the repository and explore it locally.
git clone https://github.com/qdrant/qdrant-client.gitcd qdrant-client# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Local mode is useful for development but does not scale to production; plan migration to server/cloud when data grows.
- gRPC mode (prefer_grpc=True) significantly faster for bulk uploads; test transport choice before production deployments.
- Type hints are comprehensive but Qdrant API is feature-rich; review OpenAPI docs for advanced filtering, indexing, and optimization options.
- FastEmbed GPU support (fastembed-gpu) and CPU variant (fastembed) are mutually exclusive; plan environment strategy upfront.
- Async API available since v1.6.1; ensure event loop management if mixing sync and async calls in the same application.
When to avoid it — and what to weigh
- Requires non-Python environments — This is a Python-only client. Non-Python applications need separate SDKs or direct REST/gRPC calls to Qdrant server.
- Need embedding models other than FastEmbed/Qdrant Cloud — Out-of-the-box inference is limited to FastEmbed (ONNX-based) or Qdrant Cloud models. Custom models require external orchestration.
- Minimal dependencies critical — While claimed lightweight, FastEmbed and GPU variants introduce substantial ONNX Runtime or CUDA dependencies.
- Avoid vendor lock-in to Qdrant ecosystem — This client is tightly coupled to Qdrant APIs. Switching vector databases requires rewriting integration code.
License & commercial use
Apache License 2.0. Permissive OSI-approved license permitting commercial use, modification, and distribution under identical terms.
Apache 2.0 explicitly allows commercial use without restriction. Qdrant-client source code itself poses no licensing barrier. However, underlying Qdrant server licensing and any cloud service terms (if using Qdrant Cloud) must be reviewed separately. No indemnification or warranty provided by the license.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
API key authentication supported for cloud connections. Client does not validate Qdrant server certificates or enforce TLS by default (confirm in source). Local mode in-memory is isolated; file-backed mode inherits OS file permissions. No mention of input validation for injection attacks or payload size DoS mitigations in README; review Qdrant server security documentation and validate untrusted payloads.
Alternatives to consider
Pinecone Python client
Managed vector database with native Python SDK. Simpler ops but vendor lock-in; no local mode; pricing per-query.
Weaviate Python client
Open-source vector database with Python SDK, similar feature set. Different architecture, community, and pricing model.
Milvus Python SDK
Open-source, scalable, cloud-native vector database. More complex deployment; stronger for large-scale distributed scenarios.
Build on qdrant-client with DEV.co software developers
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qdrant-client FAQ
Can I use qdrant-client without running a Qdrant server?
What embedding models are supported?
Is async/await supported?
What are the license implications for commercial use?
Software development & web development with DEV.co
From first prototype to production, DEV.co delivers software development services around tools like qdrant-client. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across vector databases and beyond.
Ready to add vector search to your AI application?
Explore qdrant-client for semantic search, RAG, and embedding workflows. Start local, scale to production. Review deployment options with our engineering team.