As AI workloads change the economics of software, Pump.co is making the case that cloud operations should be treated as its own AI-native category.
Pump.co is trying to move cloud cost optimization out of the narrow category where many companies have placed it. For years, savings tools have been treated like budget helpers. They find waste, recommend adjustments, and make the cloud bill less painful. CEO Spandana Nakka sees that as useful, but incomplete.
“Cloud savings is not a side problem anymore,” Nakka says. “It is connected to how companies build, scale, secure, and operate. The spend pattern reveals how the system is behaving.”
That is the category argument Pump is making now. The company began with a clear customer pain point: businesses were overspending on cloud infrastructure and did not have an easy way to reduce the waste. Pump built around that need and now says it works with about 1,500 customers, saving them roughly 20 percent on average. Company materials state that Pump supports businesses representing more than $600 million in annual cloud spend and has helped thousands of customers save millions across cloud and AI costs.
That traction gave Pump a practical place to start. Nakka now wants the company to use that position to address a larger technical reality. The cost of building software is no longer contained inside one cloud bill. It stretches across cloud commitments, AI inference, developer tools, monitoring systems, data platforms, compliance needs, and security requirements.
“That is where the market is changing,” Nakka says. “A company may think it is managing separate tools, but all of those tools shape the same operating picture. Cost, usage, security, and reliability are tied together.”
Pump’s platform now stretches across three related functions: savings automation, unified infrastructure visibility, and security posture monitoring across more than 30 compliance frameworks. The company has also been expanding into areas such as AI spend visibility and routing, where inference costs can become a serious pressure point for companies building with large language models.
The important point is not only that these products sit together. It is that they point toward a more unified way of managing infrastructure. A product team may ship a new feature. That feature may increase compute usage, model calls, storage, monitoring volume, and security exposure. Finance may see a higher bill later. Engineering may see performance demands. Security may see new risk. Leadership may only see that the system has become harder to explain.
Pump wants to make those signals easier to read while they are happening.
“Cloud operations has been too fragmented,” Nakka says. “One team sees cost, another sees security, another sees usage, and another sees performance. The future has to connect those signals instead of treating them like separate problems.”
That future is especially important as AI workloads become more common. An AI feature can behave differently from a traditional software feature. Usage may rise quickly. Model choice can affect margins. Inference can become expensive at scale. Internal AI tools can add costs outside the main product. The financial and technical picture can change before a company has built a process to track it.
For Pump, that makes AI cost management part of the same infrastructure story rather than a separate add-on. The company’s roadmap includes deeper visibility into AI usage and token-level spend, along with optimization across major providers. The larger goal is to help companies understand how AI changes the cost and operating structure of their software.
“AI spend is becoming infrastructure spend,” Nakka says. “If a company is building with models, routing requests, and paying for inference, that cannot live in a separate mental box. It has to be managed with the rest of the environment.”
Pump’s technical bet is that AI can also manage more of the work itself. The company’s long-term ambition is AGI for DevOps. The idea is that an AI system could build enough context around infrastructure behavior to support decisions that now require deep operational review.
That could include forecasting usage, identifying waste, suggesting infrastructure changes, managing commitments, monitoring security posture, and giving teams earlier warnings when the system begins to drift. The point is not to remove engineers from the process. The point is to give them better leverage.
“Engineers should not have to reconstruct the same context every time something changes,” Nakka says. “The system should preserve that context, learn from it, and make the next decision easier.”
The company’s position also reflects a broader shift in startup infrastructure. Earlier generations of software companies could treat cloud optimization as something to revisit once costs became painful. Today, cloud and AI costs can affect margins, runway, pricing, product design, and investor confidence much earlier. A feature may be technically impressive but financially fragile if the company does not understand what it costs to operate.
That is why the category definition matters. Savings remain the proof. They show that Pump can understand customer environments well enough to produce measurable results. But the larger category is cloud operations intelligence, where the same understanding can be applied to more decisions.
“Saving money is the visible outcome,” Nakka says. “The deeper value is understanding the environment well enough to make better choices before the waste appears.”
Companies already know they need cloud providers, AI tools, observability, security, and DevOps expertise. What they do not always have is a single way to understand how those pieces influence one another. Pump is betting that the next generation of cloud operations will be defined by systems that can connect those pieces automatically.
For more information, visit Pump.co.
Disclaimer: GeekWire newsroom and editorial staff were not involved in the creation of this content.
