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Product leaders talk safer, faster releases and deeper analysis with Bits | This Month in Datadog

In July’s This Month in Datadog, Jeremy is joined by Datadog product leaders for in-depth conversations about how Bits enables you to confidently evaluate and release features containing AI-generated code, and use natural language to ask, understand, and act across Datadog.

Shipped: Put every AI task on the cheapest model that can actually do it

If your team builds with AI, someone is defaulting to the biggest model available (say, Fable) because it feels like the safe pick, and the safe pick is almost always the most expensive one. One over-powered choice looks harmless on its own, but multiplied across every prompt, agent, and workflow, and you get a big number on the P&L. All that, yet nobody chose which model on purpose. As we like to say, using a default is not a decision.

AI cost reduction: tactics that preserve performance

AI cost reduction means lowering what you spend to run AI (tokens, inference, and compute) without sacrificing quality. The highest-leverage tactics, prompt caching, batching, and routing easy work to smaller models, cut spend 50 to 90% by removing waste, not capability. Somewhere right now, a finance leader is opening an AI bill that has quietly tripled, with no new product to show for it. Nobody approved it. No single decision caused it.

Straight from Support: AI credits, student plans, and why your Mac fans are so loud

Every so often we sit down with someone from our support team and turn their week into a blog post. First up: Roberto Vizcarra, on four things generating tickets lately, AI credits, student plans, integrations, and Mac performance. Here’s what changed and what to do about it.

Institutional knowledge doesn't scale: Building an agentic data analyst

We’ve previously written about how deeply embedded data is in people’s day-to-day work at incident.io, and I’d have it no other way — demand for data is undoubtedly a good thing. What risks breaking at scale, however, is everything downstream of that demand: data-team capacity gets stretched thin, dashboard sprawl outpaces anyone's ability to maintain it, and stakeholders can't reach an answer without going through the data team.

Spend More Time Talking to Humans

A few months ago, I noticed something happening. I would spend all day working with LLMs—prompting them, reviewing their work, and correcting them—and when I wasn’t working on my own code, I was reviewing LLM-generated code. By the end of the day, I was exhausted. This was a very unusual thing for me: I’ve been a software developer at startups for 30 years, and while sometimes I might have gotten stressed out, I had never been exhausted by the actual act of writing code.

The Video Metric That Actually Predicts Channel Success

Corporate video marketing is no longer just a creative endeavor. It is a strict operational pipeline. Yet, most marketing teams track the wrong telemetry entirely. They obsess over raw subscriber counts. They celebrate top-level view totals. They treat their channel like a static video repository. But YouTube is a highly sophisticated machine. It runs on cold, hard behavioral data. With over 500 hours of video uploaded every minute, the algorithm does not care about vanity numbers. It cares about sustained attention.

From Factory Reset to Title Transfer: How to Offboard a Modern Car

Selling an older car once meant clearing the glovebox, finding the title and removing a few forgotten coins from the cup holder. A modern vehicle requires a different kind of handover. Its infotainment system may contain phone contacts, recent destinations and saved addresses. The car may still appear inside a manufacturer's app that can locate it, check its battery or unlock its doors. Toll accounts, charging services, digital keys and garage-door controls can remain active after the physical keys have changed hands.