The generative AI era has sped everything up for most enterprises we talk to, especially development cycles (thanks to “vibe coding” and “agentic swarming”).
But even as they seek to leverage the power of new AI-assisted programming tools and coding agents like Claude Code to generate code, enterprises must contend with a looming concern — no, not safety (although that’s another one!): cloud spend.
According to Gartner, public cloud spend will rise 21.3% in 2026 and yet, according to Flexera’s last State of the Cloud report, up to 32% of enterprise cloud spend is actually just wasted resources — duplicated code, non-functional code, outdated code, needless scaffolding, inefficient processes, etc.
Today, a new firm, Adaptive6 emerged from stealth to reduce this cloud waste in realtime — automatically. The company, which also announced $44 million in total funding including a $28 million Series A led by U.S. Venture Partners (USVP), aims to treat cloud waste not as a financial discrepancy, but as a code vulnerability that must be detected and patched.
Co-founded by CEO Aviv Revach, an experienced founder, former Head of Strategy at Taboola, and a former security research team leader for the Israeli Military Intelligence Unit 8200, the idea behind the venture came directly from his experience working in cybersecurity.
“We realized this is not a financial problem; it’s an engineering problem,” Revach told VentureBeat in an exclusive video call interview conducted recently. “We drew on our background in cybersecurity, where to find vulnerabilities, you scan the cloud, identify the issues, map them back to the relevant code, find the responsible developer or engineer, and remediate—or, in some cases, shift left and prevent them altogether… it was obvious that this is exactly what we need to do.”
Adaptive6’s platform introduces a radical shift in how enterprises govern infrastructure: instead of asking finance teams to spot inefficiencies they can’t fix, it empowers engineers to resolve waste directly in their workflow.
By applying the rigor of cybersecurity—scanning, tracing, and remediation—Adaptive6 automates the cleanup of “Shadow Waste” across complex multi-cloud environments.
The shift: from billing to engineering
For years, the industry standard for managing cloud costs has been “visibility”—dashboards that tell you yesterday’s news. Revach argues that visibility without action is just noise.
“The first generation of tools are sort of trying to help on the financial side of the cloud,” Revach told VentureBeat. “They typically deal with the financial aspects of cloud cost… showing you costs going up, costs going down, forecasting, budgeting. But what they don’t really focus on is one of the biggest problems, which is the waste problem.”
According to Revach, the disconnect lies in ownership.
“Just like you have the CISO in cybersecurity trying to get everybody to be thinking about security, you now have the FinOps person trying to get everybody to be thinking about cloud cost.”
Technology: hunting “shadow waste”
The core of Adaptive6’s offering is its “Cloud Cost Governance and Optimization” (CCGO) platform. It doesn’t just look for idle servers; it hunts for what the company calls Shadow Waste—hidden inefficiencies in architecture and application workloads that traditional cost tools often miss.
The system operates without agents, using standard cloud APIs to gain read-only access to environments.
Revach explained to VentureBeat that the platform scans across AWS, GCP, and Azure, as well as PaaS layers like Databricks and Snowflake, and even deep into Kubernetes clusters.
“We have unique technology that basically allows us to match each resource in the cloud [where] we found a problem to the relevant line of code that actually created that problem,” Revach explained.
This “Cloud to Code” technology allows the system to identify the specific engineer who made the change and serve them a fix directly in their workflow (Jira, Slack, or ServiceNow).
Beyond basic resource sizing, the platform analyzes complex configurations, including those for emerging AI workloads.
Revach highlighted a specific technical nuance regarding “provisioned throughput” for Large Language Models (LLMs) on AWS.
He noted that engineers often struggle to balance commitment levels—committing too little risks performance, while committing too much wastes capital. Adaptive6’s engine analyzes these specific usage patterns to recommend the precise throughput commitment needed, a level of granularity that general finance tools lack.
Revach also provided a specific example of “Shadow Waste” involving application-level inefficiencies:
“If you’re using Python… and you’re not using the latest version—right now, version 3.12 made a major change that made it far more efficient,” he said. “Most folks, when they think about cloud cost, they don’t necessarily think of the Python version, so they only think about the size of the machine. By moving to that version, you gain the efficiency so your code just runs faster, and you reduce the cost.”
The AI paradox: both problem and solution
While Adaptive6 uses AI to generate remediation scripts and “1-Click Fixes,” Revach was careful to distinguish their deep-tech approach from generic AI coding agents. In fact, he noted that AI-generated code is often a source of waste itself.
“The code that is produced by AI is many times not that efficient because it was trained on a lot of code that other people wrote that didn’t necessarily take cloud cost optimization and governance into account,” Revach warned.
This is why Adaptive6 relies on a research team of experts rather than just generative models to identify inefficiencies. “Just like with vulnerability research, you see cyber companies getting the best of the best security researchers to find things… we are doing the exact same thing for cost inefficiencies,” Revach said.
Impact and adoption
The platform is already in use by major enterprises, including Ticketmaster, Bayer, and Norstella, with customers reporting 15–35% reductions in total cloud spend.
For global organizations, the ability to decentralized cost management is critical. “As complex as it gets with a big organization, that’s exactly our sweet spot,” Revach noted. He cited one dramatic instance of the tool’s efficacy: “We’ve had a case where one misconfiguration that basically an organization solved actually resulted in more than a million dollars of savings.”
Looking ahead
The system also includes “shift left” prevention capabilities, integrating directly into CI/CD pipelines. This allows the platform to scan code for cost inefficiencies before it ever goes live, effectively blocking expensive architectural mistakes before they are deployed—much like a security scanner blocks vulnerable code.
“We detect what’s already wasting money, prevent new inefficiencies before they deploy, and remediate at scale,” Revach said. By shifting the responsibility left to developers, Adaptive6 suggests the future of cloud cost management won’t be found in a spreadsheet, but in a pull request.
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