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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.

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.

This Spring, the Apache Kafka community released version 4.2 with a “production-ready” Share Group feature. Also known as a “Kafka queue” people were eager to see this feature because it introduced elastic consumer scaling, individual message acknowledgments, and built-in "poison pill" handling; similar to what you'd find in traditional message brokers like RabbitMQ and ActiveMQ.

I’ve been active in technical communities like PyData and codebar across the UK for nearly a decade now and owe much of my career to meeting and learning from cool people at events. Now, as someone with a professional interest in community organising, I spend a lot of my time looking for events to sponsor or speak at. But whilst finding technical events and communities can be tricky, I think I’ve found a solution using Kafka that might be useful for you too.

You've felt it. You're deep in a flow state with Claude or Cursor, building the next great thing, and then you hit the wall. Time to leave your editor, open a browser, click through a console, copy a connection string, paste it back, and pray you didn't fumble a character. The vibe is gone. What if your AI agent could just... do it? Deploy the database. Create the Kafka topic. Ship the app. All without you ever leaving the conversation. Today, that's real.

Aiven for Valkey includes the Valkey Search module setup and ready to go. Here's what that looks like in practice: a small online shop adding real search on top of the cache it's already running. Needle & Yarn sells the yarn you crochet with (skeins) and the design patterns you crochet from. Like a lot of e-commerce backends, it already runs Valkey as a product cache, with each product stored as a Hash for hot-path performance.

In this article we’ll show how we built an image search demo in Aiven Apps. The demo uses the CLIP Large Multimodal Model (LMM) to turn a user’s text prompts into a vector that can be compared with the precomputed vectors for a corpus of images, allowing the user to find images based on their text. While in this example the LMM input (the text prompt) is coming from the user, the principle is the same as for an internally generated query.

Every time your app asks the database 'does this row exist,' it pays the cost of a database query to get back 'no'.

OpenSearch isn't just a search engine anymore. Recent releases moved it into AI infrastructure: agentic memory built in, Better Binary Quantization (BBQ) compressing vectors 32x, token-usage tracking, and a one-command Observability Stack. A stack for building practical AI applications, not just indexing. The catch is that production-sized OpenSearch clusters aren't where you want to prototype.

We recently announced the Limited Availability (LA) launch of Aiven Apps, which lets teams define, run, and scale production-ready, real-time applications using container and Compose-based workflows they already know. It provides a managed, stateless runtime that runs directly inside your data perimeter, letting you deploy applications alongside open-source data services like PostgreSQL and Apache Kafka.