Qdrant Deployment Options Agent Skill
Guides Qdrant deployment selection. Use when someone asks 'how to deploy Qdrant', 'Docker vs Cloud', 'local mode', 'embedded Qdrant', 'Qdrant EDGE', 'which deployment option', 'self-hosted vs cloud', or 'need lowest latency deployment'. Also use when choosing between deployment types for a new project.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/qdrant/skills --skill qdrant-deployment-optionsThe instruction itself
6 sections, as written by the authorWhich Qdrant Deployment Do I Need?
Start with what you need: managed ops or full control? Network latency acceptable or not? Production or prototyping? The answer narrows to one of four options.
Getting Started or Prototyping
Use when: building a prototype, running tests, CI/CD pipelines, or learning Qdrant.
- Use local mode (Python only): zero-dependency, in-memory or disk-persisted, no server needed Local mode
- Local mode data format is NOT compatible with server. Do not use for production or benchmarking.
- For a real server locally, use Docker Quick start
Going to Production (Self-Hosted)
Use when: you need full control over infrastructure, data residency, or custom configuration.
- Docker is the default deployment. Full Qdrant Open Source feature set, minimal setup. Quick start
- You own operations: upgrades, backups, scaling, monitoring
- Must set up distributed mode manually for multi-node clusters Distributed deployment
- Consider Hybrid Cloud if you want Qdrant Cloud management on your infrastructure Hybrid Cloud
Going to Production (Zero-Ops)
Use when: you want managed infrastructure with zero-downtime updates, automatic backups, and resharding without operating clusters yourself.
- Qdrant Cloud handles upgrades, scaling, backups, and monitoring Qdrant Cloud
- Supports multi-version upgrades automatically
- Provides features not available in self-hosted:
/sys_metrics, managed resharding, pre-configured alerts
Need Lowest Possible Latency
Use when: network round-trip to a server is unacceptable. Edge devices, in-process search, or latency-critical applications.
- Qdrant EDGE: in-process bindings to Qdrant shard-level functions, no network overhead Qdrant EDGE
- Same data format as server. Can sync with server via shard snapshots.
- Single-node feature set only. No distributed mode.
- Chose EDGE and want to build on it? See the
qdrant-edgeskill (BM25, snapshot sync, app-side fusion).
What NOT to Do
- Use local mode for production or benchmarking (not optimized, incompatible data format)
- Self-host without monitoring and backup strategy (you will lose data or miss outages)
- Choose EDGE when you need distributed search (single-node only)
- Pick Hybrid Cloud unless you have data residency requirements (unnecessary Kubernetes complexity when Qdrant Cloud works)
Other skills for the same job
different authors, same section of the catalogue
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
How to use it
Take qdrant/qdrant-deployment-options from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.