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Akamai Launches Managed Service for API Performance

Akamai Technologies announced Managed Service for API Performance. 

Leveraging the APIContext platform, this service combines proactive testing, expert analysis, and customized optimization to help businesses keep their APIs fast, reliable, and compliant in today's complex digital environments.

The service continuously tests, monitors, and guides API environments to ensure uptime, speed, and adherence to standards — even across multicloud or hybrid deployments.

"APIs are now the heartbeat of business. Keeping them fast, resilient, and standards-aligned is a competitive advantage," said Patrick Sullivan, CTO of Security Strategy at Akamai. "With Akamai Managed Service for API Performance, users gain a partner dedicated to anticipating issues, accelerating response, and optimizing performance across the digital ecosystem."

Key features of Managed Service for API Performance include:

  • 24/7 monitoring and incident response: Around-the-clock synthetic testing and expert incident validation ensure APIs stay operational and efficient. Built for global scale, the service supports IT staff augmentation and delivers critical service assurance.
  • Tailored action plans: Executive-level reporting highlights API health metrics, performance trends, and business impact. Recommendations evolve with changing environments, refining everything from alert sensitivity to workflow design.
  • Expert guidance: Akamai analysts uncover hidden performance patterns — such as slowdowns, schema mismatches, and recurring endpoint failures — and present findings in clear, actionable reports.
  • Comprehensive performance monitoring: Identifies geographic and network-based anomalies while validating APIs against OpenAPI specs and regulatory benchmarks. Synthetic checks run continuously, triggering immediate alerts and expert-led investigations for any interruptions.
  • Cloud and infrastructure visibility: Monitors multicloud environments, DNS and SSL configurations, and the full internet delivery chain to pinpoint bottlenecks from source to user.
  • Performance and compliance baselines: Establishes benchmarks for API health and integrates seamlessly with existing performance monitoring tools, enabling better correlation and insight across observability stacks.
  • Built for regulatory frameworks: APIContext monitors both performance and conformance, ensuring APIs meet the availability, latency, and other requirements set by DORA, NIS2, MAS TRM, SEC SCI, and other financial and critical infrastructure regulations.
  • Audit-ready evidence: Every synthetic call is logged with full traceability, producing a tamper-proof record of uptime, response integrity, and standards adherence — giving compliance officers defensible proof during audits or regulator inquiries.

"With resilience requirements becoming more prominent, uptime is now a business imperative," said Mayur Upadhyaya, CEO of APIContext, the offering launch partner. "APIs sit at the heart of digital services, and their performance directly shapes customer experience and digital experience alike. Working with Akamai, we're enabling enterprises to meet these demands with confidence."

Akamai expands its API security capabilities with this new managed service to provide not just protection — but proactive performance optimization. Organizations can now offload the burden of continuous API tuning to Akamai's experts, ensuring better business outcomes and user experiences.

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 ... 

Akamai Launches Managed Service for API Performance

Akamai Technologies announced Managed Service for API Performance. 

Leveraging the APIContext platform, this service combines proactive testing, expert analysis, and customized optimization to help businesses keep their APIs fast, reliable, and compliant in today's complex digital environments.

The service continuously tests, monitors, and guides API environments to ensure uptime, speed, and adherence to standards — even across multicloud or hybrid deployments.

"APIs are now the heartbeat of business. Keeping them fast, resilient, and standards-aligned is a competitive advantage," said Patrick Sullivan, CTO of Security Strategy at Akamai. "With Akamai Managed Service for API Performance, users gain a partner dedicated to anticipating issues, accelerating response, and optimizing performance across the digital ecosystem."

Key features of Managed Service for API Performance include:

  • 24/7 monitoring and incident response: Around-the-clock synthetic testing and expert incident validation ensure APIs stay operational and efficient. Built for global scale, the service supports IT staff augmentation and delivers critical service assurance.
  • Tailored action plans: Executive-level reporting highlights API health metrics, performance trends, and business impact. Recommendations evolve with changing environments, refining everything from alert sensitivity to workflow design.
  • Expert guidance: Akamai analysts uncover hidden performance patterns — such as slowdowns, schema mismatches, and recurring endpoint failures — and present findings in clear, actionable reports.
  • Comprehensive performance monitoring: Identifies geographic and network-based anomalies while validating APIs against OpenAPI specs and regulatory benchmarks. Synthetic checks run continuously, triggering immediate alerts and expert-led investigations for any interruptions.
  • Cloud and infrastructure visibility: Monitors multicloud environments, DNS and SSL configurations, and the full internet delivery chain to pinpoint bottlenecks from source to user.
  • Performance and compliance baselines: Establishes benchmarks for API health and integrates seamlessly with existing performance monitoring tools, enabling better correlation and insight across observability stacks.
  • Built for regulatory frameworks: APIContext monitors both performance and conformance, ensuring APIs meet the availability, latency, and other requirements set by DORA, NIS2, MAS TRM, SEC SCI, and other financial and critical infrastructure regulations.
  • Audit-ready evidence: Every synthetic call is logged with full traceability, producing a tamper-proof record of uptime, response integrity, and standards adherence — giving compliance officers defensible proof during audits or regulator inquiries.

"With resilience requirements becoming more prominent, uptime is now a business imperative," said Mayur Upadhyaya, CEO of APIContext, the offering launch partner. "APIs sit at the heart of digital services, and their performance directly shapes customer experience and digital experience alike. Working with Akamai, we're enabling enterprises to meet these demands with confidence."

Akamai expands its API security capabilities with this new managed service to provide not just protection — but proactive performance optimization. Organizations can now offload the burden of continuous API tuning to Akamai's experts, ensuring better business outcomes and user experiences.

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 ...