Pump attacks cloud cost from the purchasing side rather than the engineering side. Instead of asking you to re-architect workloads, it aggregates demand across roughly 2,500+ teams and buys reserved capacity in bulk, then resells that capacity at pooled rates. Because the commitment risk sits with Pump, teams can get the benefit of long-term reserved pricing without being locked into multi-year contracts themselves, and if usage drops, that problem stays with Pump rather than with the customer.
The platform covers more than raw compute commitments. Model routing sends each AI request to the cheapest model that still clears a quality bar you set yourself, with fallback routing when a provider goes down, so inference costs fall without degrading output. Alongside that, Pump provides a spend visibility layer: unified budgets that span both cloud and AI bills, hourly anomaly detection that traces unusual spend to a resource and an owner, and tag allocation that attributes costs down to the team and even the token, suggesting owners for untagged spend.
Onboarding is designed to be light. You connect cloud accounts with read-only access, get a report of potential savings, and join the pool without migrating any infrastructure. Pump states the process takes about two minutes to connect and around 15 minutes to receive a full savings plan, and enterprise options exist for teams that want managed savings while keeping their own accounts or API keys.
