Operations | Monitoring | ITSM | DevOps | Cloud

AI observability covers performance, quality, reliability, safety, and cost. Most tools handle the first four. Here's what each pillar means, which tools cover which, and why cost is the dimension enterprises keep missing.

When a PostgreSQL query runs slowly, engineers often start with EXPLAIN ANALYZE. The output is a tree of plan nodes, each one describing a step the database took to execute it. A query with several joins and a subquery can produce 20 or more nodes. But the plan gives no visual indication of which node corresponds to each clause in the SQL text. Diagnosing the problem means viewing the plan in one window and the query in another, manually tracing connections between them.

Metric exploration often begins with a simple question, but answering that question can require deep familiarity with metric names, tag structures, and query syntax. Experienced users spend time refining queries through trial and error, and newer users struggle to get started. As a result, teams face delays in troubleshooting and analysis. Valuable observability data, including metrics that are difficult to discover and query, also goes underused.

There's a conversation I keep having with our design partners at incident.io. It starts when I ask "what are you doing with AI internally?" and lands in a similar place every time. The shape of how their engineering teams work is changing fast. Not in vague "AI is transforming everything" ways, but in concrete, repeatable patterns. Different companies are building the same things. The frontier teams are six to twelve months ahead of the average, and they're describing the same future.

Claude Mythos may be wrapped in hype, but the core signal is real: AI is making vulnerability discovery much faster, which means defenders have less time than ever to patch and enforce secure configurations. The real risk isn’t just smarter models, it’s that security teams will face a flood of new findings while the window between disclosure and exploitation keeps shrinking.

“Autonomous” doesn’t mean uncontrolled. AI-driven IT in life sciences needs guardrails based on GxP, risk, and validation. Low-risk systems can automate; high-risk systems need human approval and documentation.

Today we're rolling out a major upgrade to API Keys in Bindplane. You can now create up to 25 API keys per project, give each one a description, set an expiration date, and delete keys you no longer need. Under the hood, every key is now hashed with Argon2, the modern standard for credential storage. If you've been working around the old single-key limit by sharing one key across CI jobs, scripts, and teammates, this release is for you.

Kosli recently released kosli evaluate trail, a command that evaluates selected attestations in a Kosli trail against a Rego policy file. We used it to build a complete and useful solution for tracking Snyk container vulnerabilities for cyber-dojo (an open-sourced browser based online tool for practising TDD which Kosli uses for demos). You’ll read about what we built, why we built it, how we tested it, and specifically.

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.

One of the things we love most about building an open source platform is seeing what the community creates with it, and independent developer Anton Havekes recently built something we just had to share. Anton put together Influx Dashboard, a native iOS app that connects to your InfluxDB instance and brings your time series data straight to your phone. We’re genuinely thrilled to see this kind of work come out of the community.