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Aiven Managed Agents: Your agents, your data, fully managed and open-sourced.

Say you want to build an agent that resolves support tickets against your own data. Not a demo against a toy dataset, an agent that actually runs in production. The agent itself is maybe a day of work.

TL;DR Part 0 of a three-part series on building a documentation assistant with Aiven. This part gives you the vocabulary. Part 1 is the architecture. Part 2 builds the thing. Almost everything written about large language models explains how models get built. Very little explains what happens the other billion times, when a finished model sits there answering questions. That second thing is called inference, and it is where practically all of the engineering work actually lives.

TL;DR Aiven for OpenSearch is where you search logs from every service, with OpenSearch Dashboards included and a built-in integration for Aiven service logs. When several destinations need the same logs, or traffic bursts and mapping changes put logs at risk, put Aiven for Apache Kafka in front: applications write once, Kafka buffers and replays, and sink connectors route logs to OpenSearch, object storage and Aiven for ClickHouse.

At home I run fully automated coding loops on a few side projects. They use more than 100-million tokens a day. At Claude Opus 5.5 prices, that costs more than $5,300 a month. For hobby projects. The same usage on DeepSeek V4.1 Flash cost about $330 a month, one sixteenth of the price. If we want to keep scaling how much we use AI, most of those tokens have to run on open models, period.

TL;DR Metrics, logs, and traces each have a different shape, so each needs a different backend. Aiven for Metrics handles metrics and alerting, Aiven for OpenSearch handles keyword search across logs, and Aiven for ClickHouse handles analysis of high-volume, high-cardinality events. All three are upstream open source engines on the same Aiven platform, so you can start with one and add the others as your needs change.

TL;DR Most of the time we understand the tasks we're working on, but inevitably something comes up that we don't know much about, and we need just enough skill to cope. That used to mean reading paper documentation, then searching vendor sites and the web. Now it tends to mean a dialog with an LLM that has already ingested the documentation we don't have time to find and read. That's only half the problem, though.

TL;DR Have you ever set out to push an allegedly 15-minute fix that ended up taking multiple weeks, without even a guarantee that it would actually work? It’s happened to the best of us during our professional journeys, but this time, the story is about Apache Kafka.

TL;DR Stan's experience ranges from raising millions for his own startups to working across programming, marketing, and PR. Here is how that versatile founder mindset fuels his Product Director role at Aiven today. Stan has had a lot of ownership from day one, in the most literal sense. Aiven headhunted him to build a new product from the ground up. "My onboarding was thirty minutes with my boss, who gave me a handful of really good and original ideas, he says. "After that, I had to figure it out.

Let's start with an uncomfortable question that tends to surface exactly once, usually in front of an auditor, a customer's security team, or your own CISO: who can actually decrypt your data right now? For most managed databases and message queues, the honest answer is "the provider, technically, if they really wanted to." That's not a scandal. It's just how managed encryption-at-rest normally works: the provider generates the key, holds the key, rotates the key, and you trust them not to misuse it.