Enterprise teams storing data in Azure Blob Storage increasingly want to use that data for AI: retrieval-augmented generation, agent workflows, semantic search. Getting there means building an ingestion pipeline, choosing an embedding model, managing infrastructure, and stitching it together. That can mean weeks of engineering work before answering a single query.

What Pinecone does

Pinecone is knowledge infrastructure that includes the leading vector database built for AI retrieval. It stores your data as vectors, enabling fast semantic search across millions of documents. Pinecone is serverless, fully managed, and runs natively on Azure.

Deploy a full ingestion pipeline

We built a deployable template that automates the entire pipeline from Azure Blob Storage to a production-ready Pinecone index. Run and the template:

• Connects to your existing Azure Blob Storage account
• Parses documents (PDF, TXT, Markdown, HTML, JSON, CSV)
• Chunks text into segments optimized for retrieval
• Embeds and indexes everything into Pinecone using an integrated embedding model

The template handles parsing, chunking, embedding, and indexing end-to-end. Point it at your data and your documents are searchable in minutes.

Query your data immediately

Once deployed, your Pinecone index is ready to use. Query it via the Pinecone SDK, the Pinecone API, or AI tools like GitHub Copilot using Pinecone's MCP server and Agent Skills. Use it as the retrieval layer in any RAG application, AI agent, or search workflow.

Get started

1. Create a free Pinecone account at app.pinecone.io — no credit card required. The free Starter tier includes 2 GB of storage, 1 million monthly reads and writes, and 5 million embedding tokens per month. Need to upgrade to Standard? Subscribe through the Microsoft Marketplace.
2. Deploy the template: then .
3. Start querying your data.

Full documentation and source code: GitHub