Tutorials
Production AI Data Pipeline Architectures
TL;DR:
AI applications need the right data at the right time, in the right format, in the right place. But data fragmentation across structured and unstructured sources forces teams to build complex ELT glue code that consumes months of engineering time. CloudQuery solves this by moving data from many different sources to where your AI needs it, at high speed, integrated into the code you're already writing for RAG, agents, and LLM fine-tuning.
Great AI starts with great data. But getting that data to your AI is harder than it should be.
The unfortunate reality is that most AI projects get stuck in operational quicksand: data freshness issues, data fragmentation, and complex integration overhead that turns promising AI prototypes into maintenance nightmares.
Here's what we're seeing in production AI deployments:
Data Freshness Problems
RAG pipelines and AI agents need near real-time data to be effective. Static knowledge bases and stale training datasets lead to AI responses that feel outdated or miss critical context. When your customer asks about last week's product update and your AI references six-month-old documentation, trust erodes quickly.
Data Fragmentation Chaos
Relevant context is scattered across dozens of sources, like Confluence wikis, customer support tickets, product databases, analytics platforms, and cloud infrastructure APIs. Your AI needs unified access to both structured data (databases, APIs) and unstructured content (documents, conversations) to provide meaningful responses.
Engineering Time Sink
Teams end up building complex ELT glue code using orchestrators, API wrappers, and vendor tools just to get data flowing to the right place. What should be AI development becomes months of data plumbing. Engineers spend 60-70% of their time on data preparation instead of building intelligent applications.
The result? Slow iteration cycles, scaling pains, and AI that works in demos but struggles in production.
Introducing CloudQuery for AI Pipelines #
With CloudQuery, extract and load structured & unstructured data from any source to any destination: faster, smarter, and privately.
Faster
CloudQuery moves your data to your AI stack 2.5x faster than manually hitting APIs or using a managed data integration tool, without exposing it, embedded into the AI-native stack. Move structured or unstructured data wherever you need it, instantly.
Private
Data privacy in the AI world is more important than ever. CloudQuery runs on your infrastructure, meaning that sensitive data is never exposed: perfect for regulated industries or proprietary, valuable information.
Developer Experience
This model and a developer-friendly interface allow you to embed ELT into RAG, AI agents, or your own applications without sacrificing performance or functionality. CloudQuery's code-first, composable architecture makes it high-speed, lightweight, and easily embeddable into your existing orchestrator, agentic frameworks, and data stores.
How CloudQuery Works #
CloudQuery is a high-performance, privacy-focused, and flexible data movement framework built for developers. Here's the flow:
- Extract from 50+ sources: SaaS apps, cloud APIs, databases, or customized connectors.
- Load to AI-ready destinations: blob storage, feature stores, training pipelines, warehouses, and data lakes.
- Run it anywhere: On your laptop, on-prem, in your cloud, or inside CI/CD pipelines
- Control everything in code: Configs live in Git, integrate with your stack, and extend via SDKs.
AI Pipeline Integration #
CloudQuery specifically addresses AI pipeline challenges and integrates into your AI infrastructure:
Data Preparation
CloudQuery handles document parsing, chunking, deduplication, and format standardization. Your documents arrive clean, chunked, and structured, ready for embedding without additional preprocessing.
Flexible Output Formats
Support whatever your vector ingestion process needs. Output to PostgreSQL with pgvector, S3 for batch processing, feature stores like Feast, or direct integration with vector databases through our growing ecosystem.
Metadata Preservation
Keep document lineage, timestamps, source attribution, and access permissions intact. When your RAG system cites sources, those citations actually work and maintain audit trails.
Incremental Updates
Only process changed documents, reducing embedding costs by 60-80% and keeping your vector databases current without expensive complete rebuilds.
Format Standardization
Convert diverse sources into consistent schemas. JSON from APIs, CSVs from databases, and documents from content management systems all arrive in unified formats ready for your AI workflows.
Incremental Sync
Only update changed content, reducing embedding costs and compute requirements. Track document versions and changes automatically.
What You Can Do With It #
CloudQuery supports both structured and unstructured data, from any source to any destination. Here's a snapshot of what's possible:
🛒 E-commerce Recommendations #
Stream fresh product data into a vector DB for real-time recommendations.
Sync product catalogs, user behavior, and inventory levels in real-time to deliver sub-second recommendation responses. CloudQuery handles product updates, user interactions, and stock changes automatically, ensuring your recommendation engine always has the freshest data for maximum relevance.
🎯 Fine-tuning with Customer Feedback #
Feed fine-tuning jobs with clean, structured customer feedback.
Transform customer support tickets, surveys, and reviews into training-ready datasets with automated PII removal and quality filtering. CloudQuery standardizes formats across different feedback sources and scores data quality, giving you clean training data that produces better model outcomes.
📚 Fresh RAG Pipelines #
Keep RAG pipelines fresh with daily syncs from internal knowledge bases or wikis.
CloudQuery preserves document metadata, source attribution, and access permissions so your RAG citations remain accurate and trustworthy.
☁️ AI-Powered Cloud Management #
Sync cloud infrastructure data into LLM pipelines for real-time, AI-powered cloud management
Stream cloud configuration data across multi-cloud environments and feed it into AI models for anomaly detection, compliance monitoring, and security insights. CloudQuery handles the complexity of different cloud APIs while your AI focuses on identifying threats and governance violations in real-time.
Each of these patterns addresses specific infrastructure challenges while maintaining the flexibility to adapt to your existing architecture.
See it in action #
We've built a complete demo that showcases how CloudQuery transforms your data into AI-ready insights using PostgreSQL and pgvector.
The demo syncs your AWS cloud config data through CloudQuery to PostgreSQL, creates vector embeddings from resource configurations, and then runs AI-powered queries that reveal hidden patterns in your infrastructure:
- Infrastructure similarity discovery - Find resources with similar configurations across teams
- Intelligent clustering - Identify configuration patterns that could benefit from standardization
- Cross-team recommendations - Get AI-driven insights for infrastructure optimization
If you are looking to get started with the most versatile, privacy-focused data movement tool that’s built for AI pipelines, this is a great place to start. Unlike managed services that require sending your sensitive cloud infrastructure data to external platforms, everything runs on your own infrastructure. Your AWS credentials, resource configurations, and AI analysis stay entirely within your environment.
The complete demo is available in our GitHub repository with Docker Compose setup, sample data, and step-by-step instructions: CloudQuery Feed Your AI Demo
This is just one example of how CloudQuery enables AI applications that would be impossible with traditional data integration tools. The combination of privacy, performance, and flexibility makes it the ideal foundation for any AI pipeline that needs access to real-world data.
Feed Your AI #
46% of developers distrust AI tools not because the models are bad, but because the data feeding them is broken. CloudQuery changes this by delivering 2.5x faster data movement while handling parsing, chunking, and incremental updates. CloudQuery also supports:
Versatile performance: Support for both structured and unstructured data from 50+ sources to any AI destination, adapting to your architecture instead of forcing you into ours.
Privacy-focused: Everything runs on your infrastructure. Your data never leaves your environment—perfect for regulated industries and proprietary information.
Developer-first: Code-first configuration, Git integration, and SDK extensibility. Build AI applications instead of fighting data plumbing.
Your AI deserves better than duct-taped APIs and brittle integrations. Give it the clean, reliable data it needs to succeed.
We're inviting developers, engineers, and AI teams to rethink how they feed their models:
- Explore the "Feed Your AI" hub → Use cases, guides, and architecture patterns
- Run your first pipeline → Get started in minutes
- Follow along for updates → Use cases, guides, and customer stories
When you feed your AI with the right data, you're no longer fighting your LLM; you're working with it.
