Altimate for Databricks

Databricks costs sprawl across multiple distinct surfaces, each with its own waste patterns and each requiring a different optimization strategy. Altimate covers every one of them, autonomously, around the clock.

Most optimization tools stop at the recommendations. Altimate doesn't. A deterministic engine analyzes your compute/perf metrics by the hour, AI/ML models identify the right action, and policy checks confirm SLA safety before anything is applied. Then the system executes autonomously, monitors the outcome, and rolls back if needed.

See the closed-loop system in action: how Altimate analyzes a job cluster, selects the right intervention, checks against your SLAs, and applies the change. No human in the loop.

Spark jobs rarely fail loudly. They just run longer and cost more than they should. Misconfigured shuffle partitions, oversized executors, tasks running in series that could parallelize: each individually small, but collectively responsible for hours of wasted compute every day. Altimate analyzes execution plans across every job in your environment and pinpoints exactly where the inefficiency lives.

All-purpose clusters carry the highest per-DBU cost in Databricks, and they're the easiest to waste. Developers spin them up, move on to something else, and forget. Default sizes get copy-pasted across teams without ever being validated against real workload requirements. The result is a persistent baseline of idle compute burning through credits in the background, invisible until the month-end bill arrives.

AI/ML workloads on Databricks span dozens of moving parts: training jobs, inference endpoints, notebooks, and model-serving infrastructure, all running simultaneously, all billing independently. AI may be the biggest disruptor your business has ever seen, but it should never disrupt your bill. Attributing that spend to a specific team, project, or outcome is nearly impossible without the right tooling. Altimate cuts through the complexity and makes the invisible visible.

You can't optimize what you can't see.

Most Databricks SQL warehouses are sized once, by whoever set them up, and never revisited. When concurrency drops, the credits keep flowing. When demand spikes, the configuration doesn't adapt. Altimate continuously monitors utilization and applies the right size in real time, so you're never paying for capacity you're not using.

Interactive clusters are a silent source of waste: developers spin them up for a notebook session, move on to something else, and forget. Altimate terminates idle clusters automatically. It doesn't stop there. Every cost-saving opportunity across your Databricks environment is surfaced, clearly defined, assigned to the right person, and tracked until it's resolved, turning platform optimization into a team discipline rather than a one-person task.

Walk through how Altimate surfaces a cost-saving opportunity, explains exactly what's wrong, and makes it easy for a data engineer to assign, act on, and close it out.

"Significant money savings on warehouses that were already optimized by us."

Aniket, VP of Data, ThredUp

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