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Galileo Raises $45M Series B Funding

Galileo announced it raised $45M in Series B funding led by Scale Venture Partners, with participation from Premji Invest, bringing the company's total funding to $68M.

The surge in enterprise demand for Galileo's Evaluation Intelligence platform also attracted participation from strategic investors, including Databricks Ventures, ServiceNow Ventures, Amex Ventures, Citi Ventures, SentinelOne Ventures, as well as AI leaders like Clement Delangue, CEO of HuggingFace and Ankit Sobti, CTO of Postman. The round also includes existing investors, Battery Ventures, Walden Capital and Factory. Additionally, Andy Vitus, Partner at Scale Venture Partners, will join Galileo's board. With this investment, Galileo will scale its go-to-market strategy, expand its product development efforts, and double down on AI evaluation research to help AI developers build trustworthy AI applications.

Galileo's Evaluation Intelligence platform gives AI teams a scalable solution to evaluate, monitor, and protect their AI systems, helping ensure safe and effective performance in development and production.

"We started Galileo three years ago to solve AI's measurement problem, specifically with a focus on language models. Using humans or LLMs to judge model responses is expensive, slow, and does not scale. Yet today these are the de-facto techniques adopted across AI teams," said CEO and co-founder of Galileo Vikram Chatterji. "Our unique research-backed approach and carefully crafted UX has seen massive adoption across enterprises to unblock and grow GenAI application development. The new funding will allow us to greatly accelerate our development to meet the increasing demand."

Galileo's growth has been driven by three major trends in the AI landscape:

- First, enterprise adoption of generative AI (GenAI) is surging—Gartner projects that by 2026, over 80% of enterprises will have integrated GenAI APIs or deployed GenAI-enabled applications in production.

- Second, as AI becomes accessible to 30 million software developers—not just machine learning engineers and data scientists—many teams lack a standardized framework to evaluate the accuracy and safety of their AI solutions. A recent report found that evaluation is the second greatest challenge in deploying production AI, after serving costs, with nearly 50% of organizations relying on subjective human feedback and review.

- Third, as teams adopt advanced AI methods like RAG (Retrieval-Augmented Generation) and agentic workflows, the need for robust evaluation tools is only intensifying, driving demand for Galileo's platform to ensure reliable and effective AI deployments.

Galileo provides enterprises with an end-to-end platform that enables teams to use more accurate and trustworthy AI. Galileo developed the first Evaluation Intelligence Platform, that embeds research-backed evaluation metrics across the entire GenAI stack and workflow, giving teams the visibility and control they need to build, deploy, test, monitor, and secure their AI system.

Andrew Ferguson, VP, Databricks Ventures, said: "Evaluations have become a critical component of the AI stack, and Galileo has established itself as a leader with one of the most mature products and businesses in this space. We look forward to collaborating further to accelerate enterprise adoption of generative AI and to help companies build data intelligence."

"It's incredibly rare to find AI infrastructure companies like Galileo that can deliver value regardless of your chosen cloud or LLM," said Vedant Agrawal, Vice President of Premji Invest. "This unique positioning, coupled with the potential to build a massive franchise, is what drove our excitement."

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Galileo Raises $45M Series B Funding

Galileo announced it raised $45M in Series B funding led by Scale Venture Partners, with participation from Premji Invest, bringing the company's total funding to $68M.

The surge in enterprise demand for Galileo's Evaluation Intelligence platform also attracted participation from strategic investors, including Databricks Ventures, ServiceNow Ventures, Amex Ventures, Citi Ventures, SentinelOne Ventures, as well as AI leaders like Clement Delangue, CEO of HuggingFace and Ankit Sobti, CTO of Postman. The round also includes existing investors, Battery Ventures, Walden Capital and Factory. Additionally, Andy Vitus, Partner at Scale Venture Partners, will join Galileo's board. With this investment, Galileo will scale its go-to-market strategy, expand its product development efforts, and double down on AI evaluation research to help AI developers build trustworthy AI applications.

Galileo's Evaluation Intelligence platform gives AI teams a scalable solution to evaluate, monitor, and protect their AI systems, helping ensure safe and effective performance in development and production.

"We started Galileo three years ago to solve AI's measurement problem, specifically with a focus on language models. Using humans or LLMs to judge model responses is expensive, slow, and does not scale. Yet today these are the de-facto techniques adopted across AI teams," said CEO and co-founder of Galileo Vikram Chatterji. "Our unique research-backed approach and carefully crafted UX has seen massive adoption across enterprises to unblock and grow GenAI application development. The new funding will allow us to greatly accelerate our development to meet the increasing demand."

Galileo's growth has been driven by three major trends in the AI landscape:

- First, enterprise adoption of generative AI (GenAI) is surging—Gartner projects that by 2026, over 80% of enterprises will have integrated GenAI APIs or deployed GenAI-enabled applications in production.

- Second, as AI becomes accessible to 30 million software developers—not just machine learning engineers and data scientists—many teams lack a standardized framework to evaluate the accuracy and safety of their AI solutions. A recent report found that evaluation is the second greatest challenge in deploying production AI, after serving costs, with nearly 50% of organizations relying on subjective human feedback and review.

- Third, as teams adopt advanced AI methods like RAG (Retrieval-Augmented Generation) and agentic workflows, the need for robust evaluation tools is only intensifying, driving demand for Galileo's platform to ensure reliable and effective AI deployments.

Galileo provides enterprises with an end-to-end platform that enables teams to use more accurate and trustworthy AI. Galileo developed the first Evaluation Intelligence Platform, that embeds research-backed evaluation metrics across the entire GenAI stack and workflow, giving teams the visibility and control they need to build, deploy, test, monitor, and secure their AI system.

Andrew Ferguson, VP, Databricks Ventures, said: "Evaluations have become a critical component of the AI stack, and Galileo has established itself as a leader with one of the most mature products and businesses in this space. We look forward to collaborating further to accelerate enterprise adoption of generative AI and to help companies build data intelligence."

"It's incredibly rare to find AI infrastructure companies like Galileo that can deliver value regardless of your chosen cloud or LLM," said Vedant Agrawal, Vice President of Premji Invest. "This unique positioning, coupled with the potential to build a massive franchise, is what drove our excitement."

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Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...