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Sazabi Raises $8M in Seed Funding

Sazabi announced an $8 million seed financing led by J2 Ventures, Village Global, and Y Combinator, with participation from Orange Collective and over 60 angel investors from leading AI companies, including Vercel, Cursor, LangChain, OpenAI, Anthropic, GitHub, Replit, Browserbase, and others. 

Sazabi will use the funding to expand its engineering team, accelerate product development, and deepen its integrations across modern cloud and developer platforms.

Sazabi is building observability for the AI era: a world where teams ship faster, production systems change continuously, and traditional dashboards, brittle instrumentation, noisy alerts, and manual incident response can no longer keep pace. Instead of asking engineers to configure complex telemetry stacks and dig through dashboards during incidents, Sazabi uses AI agents to understand a team's logs, infrastructure, and codebase before proactively detecting, investigating, and resolving production issues.

"AI has changed how software gets written. Now it is changing how software gets operated," said Sherwood Callaway, founder and CEO of Sazabi. "The first half of software engineering has been transformed by tools like Cursor, Claude Code, and Codex. But the second half — monitoring, debugging, incident response, and reliability — is still stuck in the pre-AI era. Sazabi is rebuilding observability from first principles for a world where agents are part of every engineering team."

The company's approach is built around a controversial but powerful idea: logs are all you need. Instead of splitting observability workflows across logs, metrics, and traces, Sazabi treats log data as the single source of truth for understanding production systems. Sazabi uses AI to reconstruct the views engineers need from log data on its own, reducing instrumentation complexity while preserving the power of a traditional observability platform.

Sazabi was founded by Sherwood Callaway, a two-time Y Combinator founder and software engineer with more than a decade of experience building infrastructure and observability systems at high-growth companies including Brex and Crunchbase. The Sazabi team includes early members of the Brex infrastructure engineering team as well as past founders in the observability space.

"Sherwood is the kind of founder I back without hesitation," said Hunter Walk, Founding Partner at Homebrew who previously backed AI code review platform Graphite. "A technical, second-time founder with clear product vision and deep subject-matter expertise. Sazabi reminds me of Graphite in the early days."

The Latest

For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

Sazabi Raises $8M in Seed Funding

Sazabi announced an $8 million seed financing led by J2 Ventures, Village Global, and Y Combinator, with participation from Orange Collective and over 60 angel investors from leading AI companies, including Vercel, Cursor, LangChain, OpenAI, Anthropic, GitHub, Replit, Browserbase, and others. 

Sazabi will use the funding to expand its engineering team, accelerate product development, and deepen its integrations across modern cloud and developer platforms.

Sazabi is building observability for the AI era: a world where teams ship faster, production systems change continuously, and traditional dashboards, brittle instrumentation, noisy alerts, and manual incident response can no longer keep pace. Instead of asking engineers to configure complex telemetry stacks and dig through dashboards during incidents, Sazabi uses AI agents to understand a team's logs, infrastructure, and codebase before proactively detecting, investigating, and resolving production issues.

"AI has changed how software gets written. Now it is changing how software gets operated," said Sherwood Callaway, founder and CEO of Sazabi. "The first half of software engineering has been transformed by tools like Cursor, Claude Code, and Codex. But the second half — monitoring, debugging, incident response, and reliability — is still stuck in the pre-AI era. Sazabi is rebuilding observability from first principles for a world where agents are part of every engineering team."

The company's approach is built around a controversial but powerful idea: logs are all you need. Instead of splitting observability workflows across logs, metrics, and traces, Sazabi treats log data as the single source of truth for understanding production systems. Sazabi uses AI to reconstruct the views engineers need from log data on its own, reducing instrumentation complexity while preserving the power of a traditional observability platform.

Sazabi was founded by Sherwood Callaway, a two-time Y Combinator founder and software engineer with more than a decade of experience building infrastructure and observability systems at high-growth companies including Brex and Crunchbase. The Sazabi team includes early members of the Brex infrastructure engineering team as well as past founders in the observability space.

"Sherwood is the kind of founder I back without hesitation," said Hunter Walk, Founding Partner at Homebrew who previously backed AI code review platform Graphite. "A technical, second-time founder with clear product vision and deep subject-matter expertise. Sazabi reminds me of Graphite in the early days."

The Latest

For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...