Operations | Monitoring | ITSM | DevOps | Cloud

When we first introduced people to Aiven Runtime, before it even had that name, we kept getting asked: "Can we schedule things?" If you come from a Unix background, that tends to cause a reflexive reach for the cron tool, which provides basic scheduling at fixed intervals. But if you've spent more time with distributed software, you're more likely to think of Apache Airflow, which can do a lot more (although, yes, you can use Airflow to schedule tasks at fixed intervals if you want).

That was the question posed at the Women in Open Source+ Panel I had been asked to moderate. The panelists, from Snowflake, AWS, and NVIDIA, gave similar answers: We have our jobs today because of the community, and I don’t see that changing any time soon. I felt the same. When I joined Aiven, its direct callout to open source was a deciding factor.

TL;DR All scripts in this post live in the companion repository dkrautschick/pg-logical-migration. Every step references a numbered script you can run against your own Aiven project, and the listings/ folder has each step as a standalone snippet for copy-paste self-tests. The demo database is beerdb, a small craft-beer database, because every example database should contain at least one Doppelbock.

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.