Feed your LLMs with proper context without sacrificing data privacy or speed
Extract from APIs, cloud platforms, SaaS apps, or internal systems.
Send data where your AI needs it: vector databases, feature stores, object storage, and more.
Fast, transparent, embedded ELT that works with your AI stack.
Keep embeddings fresh by continuously syncing enterprise knowledge into vector DBs.
Move proprietary datasets into feature stores or training pipelines.
Connect agents to the data they need, within your frameworks of choice.
Consolidate Bedrock or Vertex AI data for monitoring, compliance, and FinOps.
| Use Case | How it helps |
|---|---|
| Security | Govern your AI cloud infrastructure, or make your AWS data available as an LLM-powered copilot. |
| Healthcare | Feed AI models while staying HIPAA-compliant |
| Embedded ELT | Code-first data integration within your AI app or workflow. |
| Private LLMs | Train your own LLM and never expose data to the hyperscalers. |
OpenAI
Anthropic
AWS
GCP
Azure
Airflow
Dagster
LangChain
CrewAI
Pinecone
Chroma
PostgreSQL
CloudQuery's new MCP server lets AI agents directly query your cloud inventory using natural language. Get real dashboards, reports, and analytics from actual infrastructure data across AWS, GCP, Azure, and more.
Joe Karlsson •
Build an AI automation pipeline with n8n that monitors cloud storage buckets for missing tags, recommends appropriate values using AI analysis, and sends Slack alerts for better infrastructure management.
Michal Brutvan •
How we built a Go-based MCP server to make AI Assistants AI data-aware, bridging code and database, which ended up transforming the way we code.
Mariano Gappa, +1 •
Your data, your infrastructure, your stack: CloudQuery is ELT for building secure, lightning-fast AI pipelines entirely in your environment.
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