Flexera acquired ProsperOps, an AI-enabled FinOps automation solution for public cloud, and Chaos Genius, a provider of innovator in AI-driven cost optimization for Snowflake and Databricks.
These strategic additions augment Flexera’s capabilities to deliver the most comprehensive, intelligent, and autonomous FinOps solution on the market including cost reporting and allocation, workload optimization, and rate optimization. They also expand Flexera’s product reach into the emerging areas of FinOps for AI and FinOps for Data Clouds.
ProsperOps brings an autonomous approach to managing cloud commitments across AWS, Azure, and Google Cloud, helping enterprises move beyond passive recommendations and into active savings outcomes. ProsperOps extends Flexera’s FinOps for AI capabilities and supports finance, engineering, and procurement teams with intelligent automation that takes action without human intervention. As a Flexera company, ProsperOps will continue operating under its own brand to ensure continuity for customers and partners while integrating complementary Flexera FinOps features.
“As enterprises adopt AI across their infrastructure, the need for intelligent, automated execution has never been greater,” said Jim Ryan, CEO of Flexera. “ProsperOps strengthens our ability to deliver on that promise, helping organizations govern cloud spend with precision and scale outcomes that were previously out of reach.”
“ProsperOps was founded on the belief that many of the critical cloud cost optimization use cases, particularly rate optimization, could be delivered through AI-enabled management. As the market matures, customers are asking for more than point solutions; they want unified rate optimization, workload optimization, and cost visibility,” said Chris Cochran, CEO and Co-Founder of ProsperOps. “Together, we are uniquely positioned to deliver the comprehensive FinOps platform organizations have been asking for.”
Chaos Genius delivers agentic-based FinOps for AI that autonomously optimizes inefficient usage across Snowflake and Databricks.
“Chaos Genius brings the autonomous automation through agentic AI for Snowflake and Databricks optimization that our customers and partners need,” said Jim Ryan, CEO of Flexera. “It delivers real-time intelligence and control that puts them back in command of their cloud and AI investments.”
“Joining Flexera allows us to scale our impact globally and empower more organizations to govern data cloud costs amid exponential AI growth,” said Preeti Shrimal, CEO of Chaos Genius.
Flexera is accelerating toward a unified FinOps future as cloud costs surge and AI reshapes enterprise technology strategy. The additions of ProsperOps and Chaos Genius build on Flexera’s integration of Spot and Snow, reinforcing its position as the only provider with comprehensive capabilities across the entire FinOps Framework as defined by the FinOps Foundation.
“Organizations need more than dashboards. They need execution,” added Ryan. “With ProsperOps and Chaos Genius, Flexera delivers the AI-powered execution layer for modern FinOps.”
The Latest
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...
Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...
Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...
As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...
For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...
I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...
Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...
80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...