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I spend a good amount of my time deploying Aiven services for demos and examples. Traditionally the tools I reach for are: If I’m writing a program, I may also look to the Aiven API, perhaps using curl at the command line or in a shell script, or perhaps with direct HTTP requests in a Python program. The API is how the console and the CLI tool talk to Aiven, but I generally find that too low level to be comfortable, and I always have to look up how to pass in the Aiven user token.

CloudZero now breaks out Snowflake Cortex spend on its own, so the AI usage buried in your Snowflake bill becomes a line item you can track and allocate before it ramps into a surprise.

You're building with Claude or Cursor, and you need to know what's actually happening on your Kafka cluster. Your AI assistant knows Apache Kafka in the abstract, but not your topics, your retention, or that a consumer group has been slipping since this morning. So you leave the editor and go digging through logs, a CLI, and a few dashboards, correlating by hand to answer questions like: The Aiven MCP (EA) turns each of those into a sentence you type where you already work.

Most teams can confirm their AI features are turned on. Measuring how often employees use them, which requests get resolved without agent intervention, and where AI is helping support teams work more efficiently is a different question. In InvGate Service Management, those capabilities live in AI Hub, a set of built-in AI features that includes the Virtual Service Agent, AI-assisted ticket resolution for agents, automated knowledge generation, and more.

Benchmarks keep showing that picking the right LLM is hard. The easy answer is "just use the most powerful one." That works, but it is pricey. A small, cheap, or local model can handle many simple requests just as well as a frontier model, for a fraction of the cost. That is what semantic routing is for. Use middleware that looks at an incoming request and decides which model should answer it.