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Netlify Releases Observability, AI Gateway and Prerendering Extensions

Netlify announced that Observability, AI Gateway, and Prerender extensions are now generally available to make it easier to understand how applications behave after deployment and reduce the operational work required to run them. 

They help developers ship with more confidence and less guesswork. Together, they mark the next step in Netlify's AI-native platform, bringing more of the development process onto one system so developers stay in flow from code to production.

Netlify offers Observability, AI Gateway, and Prerender extensions so developers stay in flow from code to production.

In October, Netlify introduced Agent Runners to help developers apply code updates through natural language instructions. That release built on earlier work with Why Did It Fail, which explains build failures in plain terms. Those efforts focused on creation and build. The new features extend that momentum into deploy and run, strengthening Netlify's Agent Experience (AX) vision: a workflow where developers and AI agents work in the same environment and move through the same steps. With more of the workflow in one place, developers can move from writing code to running applications without losing context. Netlifysupports the shift towards AI-supported development that spans the entire development workflow, not just the start of it.

"We're seeing developers move faster with AI when they write code, but deploy and run haven't kept up. We're changing that," said Matt Biilmann, CEO and Co-founder of Netlify. "When the work of writing code, understanding builds, and running applications lives on the same platform, developers don't have to stitch their workflow across different tools. It's a cleaner experience, and it's the direction modern development is moving."

  • Observability gives developers immediate insight into how an application behaves after each deploy. It surfaces request and function activity so it's easier to spot changes in latency, error patterns, or usage tied to recent updates. Developers can see what changed and why directly in the Netlify dashboard without maintaining a separate monitoring system. For enterprise teams, Observability provides the visibility needed to run AI-powered applications as dependable production workloads. This becomes even more important as teams begin relying on agents to generate and modify more of their code.
  • AI Gateway lets any agent or developer add AI features to applications that work from the first prompt, without creating or managing accounts, credentials, or environment variables. Like other Netlify primitives, it's ready to use the moment you deploy. AI Gateway securely centralizes credentials, simplifies billing, and tracks usage across providers including Anthropic, OpenAI, and Google Gemini, making it easier to manage and scale AI features in production. Teams can experiment with different models faster and get clearer visibility into AI usage across their applications.
  • Prerendering extensions fix this automatically by serving a fully rendered HTML version to crawlers while preserving the dynamic experience for users. Search engines and AI agents get a complete view of each page, and developers avoid maintaining separate rendering paths. 

These new capabilities strengthen Netlify's AI-native direction by supporting more of the workflow that developers and agents rely on together.

Availability Observability and AI Gateway are generally available now. Prerender extensions are available to all Netlify plans.

The Latest

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 ...

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 ... 

Netlify Releases Observability, AI Gateway and Prerendering Extensions

Netlify announced that Observability, AI Gateway, and Prerender extensions are now generally available to make it easier to understand how applications behave after deployment and reduce the operational work required to run them. 

They help developers ship with more confidence and less guesswork. Together, they mark the next step in Netlify's AI-native platform, bringing more of the development process onto one system so developers stay in flow from code to production.

Netlify offers Observability, AI Gateway, and Prerender extensions so developers stay in flow from code to production.

In October, Netlify introduced Agent Runners to help developers apply code updates through natural language instructions. That release built on earlier work with Why Did It Fail, which explains build failures in plain terms. Those efforts focused on creation and build. The new features extend that momentum into deploy and run, strengthening Netlify's Agent Experience (AX) vision: a workflow where developers and AI agents work in the same environment and move through the same steps. With more of the workflow in one place, developers can move from writing code to running applications without losing context. Netlifysupports the shift towards AI-supported development that spans the entire development workflow, not just the start of it.

"We're seeing developers move faster with AI when they write code, but deploy and run haven't kept up. We're changing that," said Matt Biilmann, CEO and Co-founder of Netlify. "When the work of writing code, understanding builds, and running applications lives on the same platform, developers don't have to stitch their workflow across different tools. It's a cleaner experience, and it's the direction modern development is moving."

  • Observability gives developers immediate insight into how an application behaves after each deploy. It surfaces request and function activity so it's easier to spot changes in latency, error patterns, or usage tied to recent updates. Developers can see what changed and why directly in the Netlify dashboard without maintaining a separate monitoring system. For enterprise teams, Observability provides the visibility needed to run AI-powered applications as dependable production workloads. This becomes even more important as teams begin relying on agents to generate and modify more of their code.
  • AI Gateway lets any agent or developer add AI features to applications that work from the first prompt, without creating or managing accounts, credentials, or environment variables. Like other Netlify primitives, it's ready to use the moment you deploy. AI Gateway securely centralizes credentials, simplifies billing, and tracks usage across providers including Anthropic, OpenAI, and Google Gemini, making it easier to manage and scale AI features in production. Teams can experiment with different models faster and get clearer visibility into AI usage across their applications.
  • Prerendering extensions fix this automatically by serving a fully rendered HTML version to crawlers while preserving the dynamic experience for users. Search engines and AI agents get a complete view of each page, and developers avoid maintaining separate rendering paths. 

These new capabilities strengthen Netlify's AI-native direction by supporting more of the workflow that developers and agents rely on together.

Availability Observability and AI Gateway are generally available now. Prerender extensions are available to all Netlify plans.

The Latest

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 ...

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 ...