Operations | Monitoring | ITSM | DevOps | Cloud

Off-the-shelf models are easy to deploy, but they are rarely enough to solve complex, domain-specific challenges in production. The key to sustained AI value is not in the models themselves but in the ability to tune, evaluate, and refine those models against your organization’s real-time signals. We are excited to announce that Adaptive ML is joining Datadog to accelerate this vision by combining our deep observability data with their expertise in building specialized, high-performance AI agents.

Developer experience, commonly known as DevEx, describes how an organization’s systems, workflows, tools, and culture affect developer productivity. A positive DevEx leads to tangible organizational benefits, including faster releases, increased innovation, and reduced technical debt. Measuring DevEx enables engineering management to quantify their team’s impact and understand where to direct improvement efforts.

Coding agents like Claude Code, Cursor, and Codex CLI handle the coding parts of building an AI application well. The harder work comes after: understanding why a response went wrong, building eval sets that reflect real production behavior, and keeping up with an application that changes faster than any one-off script can. Teams spend 60–80% of their time on evaluation and error analysis, and much of that work needs to be redone every time the stack shifts.

Engineering teams that manage high-volume log sources, such as content delivery network (CDN) edges, streaming platforms, and authentication systems, often have to make a difficult retention tradeoff. Indexing every event keeps logs searchable during investigations, audits, and postmortems, but it can make long-term retention expensive.

At Datadog, our broad Kubernetes footprint amplifies the significance of a familiar autoscaling tradeoff: Overprovisioning wastes cloud spend, while underprovisioning threatens reliability. We built Datadog Kubernetes Autoscaling (DKA) to help teams rightsize their workloads by generating intelligent resource recommendations and automating multidimensional workload scaling. Across Datadog, adopting DKA has eliminated more than $3 million in annualized idle compute costs while reducing reliability risks.

Feature flag migrations have a reputation problem. Ask anybody who’s been through one before and you’ll hear the stories, usually from someone still a little frustrated about a bad cutover, with a postmortem or two to show for it. The reputation is mostly undeserved. While the risks are real, they’re well understood and easily controlled. Getting a migration right doesn’t require a big coordinated effort.

AI teams have invested heavily in evaluation frameworks, yet getting those frameworks beyond local experimentation remains challenging. Teams using open source libraries like DeepEval and Pydantic Evals gain flexibility and research-grounded metrics, but operationalizing those evaluations still requires brittle custom integration code that doesn’t scale.

As teams scale AI and agentic workloads, log volumes can grow fast. That growth can force teams into a difficult trade-off: Keep logs searchable in their existing workflows, or store them cost-effectively for longer periods. For teams that rely on logs during incident response, compliance reviews, and long-running investigations, losing either affordability or searchability can slow down troubleshooting. Datadog and ClickHouse are partnering to help remove that trade-off.

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.

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.