Operations | Monitoring | ITSM | DevOps | Cloud

The AI Bottleneck: Why Your Modern Models Are Choking on Legacy and Streaming Data Architecture

Enterprise AI struggles not from inadequate models, but from fragmented data architecture. Critical business data remains trapped in legacy systems or lost in streaming complexity. Success requires bridging the gap between modern intelligence layers and underlying systems of record.

AI Cost Savings Unlocking Hidden Engineering Value

Bain says AI cost savings aren't arriving. But the value isn't missing, it's invisible. Most engineering teams can see token spend. They can see AI usage. What they can't see is whether any of it shipped, and whether it moved the needle on delivery. That's the measurement gap. And until it closes, AI ROI will keep looking worse than it should.

A Practical Guide to Deploying LMM-Powered Apps with CLIP and pgvector

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.

Get reliable answers to business questions with Bits Data Analysis

Teams are wiring AI coding agents straight to their warehouse over MCP and asking things like “What was our revenue by channel in Q2?” The agent finds a revenue table, runs a query, and returns a number in seconds, with no waiting on the data team. While the answer initially looks right, the problem is that the number is often wrong.

Autonomously monitor for impactful degradations with Bits Detection

Monitoring is built around the system a team understands at a point in time. Engineers add endpoints, move dependencies, and change user flows every day. Over time, that creates coverage drift as monitors keep reflecting the system as it used to behave, while changing paths introduce failure modes that teams didn’t yet know to watch for. Bits Detection automatically creates, tunes, and maintains monitors for your services.

Coding Agents Write the Code. Who Verifies It Works? We Built the Answer.

Coding agents are good at reading a spec and producing code. But producing code is one step in a longer process. The real loop is Spec -> Code -> Deploy -> Test -> Verify -> Ship. Agents stop at step two. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

Where AI automation actually earns its place in IT operations

The promise attached to AI in operations has outrun the evidence. The pitch, repeated across keynote stages and vendor decks, is that AI will run your operations: detect, decide, remediate, and close the loop while the on-call engineer sleeps. It is a tidy story. It is also not the one that holds up at three in the morning when a cascading failure is halfway through your fleet.