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Cribl Raises $200M in Series C Funding

Cribl raised $200 million in new Series C funding led by Greylock and Redpoint Ventures, joined by new investor IVP, existing investors Sequoia and CRV, and with strategic investment from Citi Ventures and Crowdstrike.

This Series C funding brings Cribl’s total funding to $254 million, coming on the heels of sizable deals with large enterprise customers including FINRA, Rivian, and Cox Automotive.

David Wadhwani, Partner at Greylock, says: “Cribl is enabling customers to realize their long term observability strategies by addressing the single biggest pain point: getting control of the massive surface of data”

Data has become a double-edged sword in the enterprise. Exacerbated by the dramatic growth of remote work, security attacks, and heightened privacy and compliance requirements, companies are now collecting and storing such vast amounts of observability data that a new landscape of tech vendors have emerged to solve the myriad challenges that this “big data” created. But many data vendors address problems by locking customers into their own expensive data stacks — creating a long-term cost and complexity for customers.

Cribl is taking an open approach to the flow of data in the enterprise. In its flagship product, LogStream, Cribl has invented an entirely new, vendor-agnostic way to parse and route any type of event data that flows through corporate IT systems. In doing so, Cribl’s LogStream has not only created an observability pipeline that offers unparalleled flexibility and control across IT systems — it gives companies the freedom to choose their own analytics tools and storage destinations from a diverse range of best-of-breed data solutions without fear of vendor lock-in, complementing tools such as Splunk, Datadog, and Exabeam.

“Enterprises today are caught between the mythical ideal of a single pane of glass for all data insights, and the harsh reality that they have to install agents everywhere they want to observe data,” said Clint Sharp, co-founder and CEO of Cribl. “Cribl, our customers, and investors recognize there’s a better way — to create a unified data pipeline, with the same agents across security and operations, that allows enterprises to maximize the value of their existing investments. This isn’t a ‘better’ or ‘faster’ version of what’s in the market — it’s an entirely new, open architecture for observability.”

“Today’s IT and security teams are under siege – and no one is building software catered to them. Cribl is truly unique as they are the only vendor in the market giving those teams both the power and the choice to manage — and enrich — the onslaught of data in a totally agnostic way,” said Scott Raney, Managing Director at Redpoint Ventures. “We hear again and again that their customers are in awe when they use Cribl, which is not feedback we hear very often in this space...”

The Cribl team has a deep heritage building innovative technology at Splunk, and at Cribl the team has invented technology that is the single strategic control point for all data in their enterprise — offering unparalleled flexibility and control over observability data flowing between every system in the enterprise. Its superior purpose-built technology is 7x more efficient at processing event data while using far fewer resources than alternatives, and has helped it win relationships with new global customers such as Whole Foods and Vodafone.

“Organizations engaged in digital transformation initiatives are now faced with managing highly dynamic, but also very complex, distributed environments. It’s no surprise then that the top goal of these digital transformation initiatives is to drive operational efficiencies,” stated Bob Laliberte Sr Analyst with ESG. “These modern cloud-native environments generate more data than ever before, and organizations need solutions like Cribl LogStream to enable them to regain control and streamline the collection and distribution of the right data to the right tools, in a cost effective manner.”

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

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 ...

Cribl Raises $200M in Series C Funding

Cribl raised $200 million in new Series C funding led by Greylock and Redpoint Ventures, joined by new investor IVP, existing investors Sequoia and CRV, and with strategic investment from Citi Ventures and Crowdstrike.

This Series C funding brings Cribl’s total funding to $254 million, coming on the heels of sizable deals with large enterprise customers including FINRA, Rivian, and Cox Automotive.

David Wadhwani, Partner at Greylock, says: “Cribl is enabling customers to realize their long term observability strategies by addressing the single biggest pain point: getting control of the massive surface of data”

Data has become a double-edged sword in the enterprise. Exacerbated by the dramatic growth of remote work, security attacks, and heightened privacy and compliance requirements, companies are now collecting and storing such vast amounts of observability data that a new landscape of tech vendors have emerged to solve the myriad challenges that this “big data” created. But many data vendors address problems by locking customers into their own expensive data stacks — creating a long-term cost and complexity for customers.

Cribl is taking an open approach to the flow of data in the enterprise. In its flagship product, LogStream, Cribl has invented an entirely new, vendor-agnostic way to parse and route any type of event data that flows through corporate IT systems. In doing so, Cribl’s LogStream has not only created an observability pipeline that offers unparalleled flexibility and control across IT systems — it gives companies the freedom to choose their own analytics tools and storage destinations from a diverse range of best-of-breed data solutions without fear of vendor lock-in, complementing tools such as Splunk, Datadog, and Exabeam.

“Enterprises today are caught between the mythical ideal of a single pane of glass for all data insights, and the harsh reality that they have to install agents everywhere they want to observe data,” said Clint Sharp, co-founder and CEO of Cribl. “Cribl, our customers, and investors recognize there’s a better way — to create a unified data pipeline, with the same agents across security and operations, that allows enterprises to maximize the value of their existing investments. This isn’t a ‘better’ or ‘faster’ version of what’s in the market — it’s an entirely new, open architecture for observability.”

“Today’s IT and security teams are under siege – and no one is building software catered to them. Cribl is truly unique as they are the only vendor in the market giving those teams both the power and the choice to manage — and enrich — the onslaught of data in a totally agnostic way,” said Scott Raney, Managing Director at Redpoint Ventures. “We hear again and again that their customers are in awe when they use Cribl, which is not feedback we hear very often in this space...”

The Cribl team has a deep heritage building innovative technology at Splunk, and at Cribl the team has invented technology that is the single strategic control point for all data in their enterprise — offering unparalleled flexibility and control over observability data flowing between every system in the enterprise. Its superior purpose-built technology is 7x more efficient at processing event data while using far fewer resources than alternatives, and has helped it win relationships with new global customers such as Whole Foods and Vodafone.

“Organizations engaged in digital transformation initiatives are now faced with managing highly dynamic, but also very complex, distributed environments. It’s no surprise then that the top goal of these digital transformation initiatives is to drive operational efficiencies,” stated Bob Laliberte Sr Analyst with ESG. “These modern cloud-native environments generate more data than ever before, and organizations need solutions like Cribl LogStream to enable them to regain control and streamline the collection and distribution of the right data to the right tools, in a cost effective manner.”

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

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 ...