Unlocking Foundry IQ: Build Smarter Agents with Serverless Retrieval
In today's data-driven world, organizations are drowning in information. Foundry IQ addresses this challenge by consolidating knowledge from various sources—documents, emails, meetings, and operational data—into a single, accessible knowledge base. This means you can build smarter agents faster, without the burden of maintaining separate connectors or retrieval strategies for each data source.
At its core, Foundry IQ uses the Model Context Protocol (MCP) to expose knowledge to any compatible agent framework. This serverless model eliminates infrastructure friction, allowing for instant retrieval-augmented generation with high-quality results. You can expect to manage resources efficiently with Compute Units (CUs), which measure your resource consumption, including CPU, memory, and storage I/O. Be mindful of costs: compute usage is priced at $0.24 CU per hour, and indexed storage can go up to $0.29 per GB per month, depending on your region. Each index is capped at 1 GB, and you can create up to 30 indexes per service, with a maximum of five services per subscription in a region.
Currently, Foundry IQ is in public preview, specifically the Serverless Developer tier, which means you can experiment without incurring charges until billing starts in late 2026. However, keep in mind that current Compute Unit estimates may change before billing is enabled. This flexibility is a game-changer, but you need to stay updated on any changes to avoid unexpected costs.
Key takeaways
- →Leverage a unified knowledge base to simplify agent development.
- →Utilize the Model Context Protocol (MCP) for seamless knowledge access.
- →Monitor Compute Units (CUs) to manage resource consumption effectively.
- →Be aware of costs associated with compute usage and indexed storage.
- →Experiment with the Serverless Developer tier before billing begins.
Why it matters
By consolidating diverse data sources into a single knowledge base, Foundry IQ allows engineers to build intelligent agents more efficiently, ultimately enhancing productivity and decision-making in real-time operations.
When NOT to use this
The official docs don't call out specific anti-patterns here. Use your judgment based on your scale and requirements.
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