Sedai has launched AI Agent Optimization, a new platform designed to autonomously optimize the cost, performance, and accuracy of enterprise AI agents and large language model (LLM) operations. The platform introduces centralized governance, intelligent model routing, and real-time observability capabilities aimed at helping organizations manage rapidly growing AI infrastructure and model usage costs.
The launch reflects increasing enterprise demand for operational control and optimization as AI adoption accelerates across large-scale business environments.
According to Sedai, enterprise spending on AI models has risen sharply, with average annual AI model expenses reaching $11.6 million compared to $4.5 million in 2024. The company also noted that some Fortune 500 organizations now exceed $100 million annually in combined AI and cloud infrastructure costs.
Despite this rapid increase in investment, many organizations still lack centralized oversight into how AI models are selected, deployed, optimized, and governed across internal engineering teams.
Sedai’s AI Agent Optimization platform operates between enterprise AI agents and LLM providers, enabling organizations to optimize routing, governance, and operational performance without major changes to existing application code or workflows.
"Most engineering teams are picking AI models based on intuition, not data," said Suresh Mathew, CEO and Founder of Sedai. "The result is that teams can't control their skyrocketing AI costs or keep pace as models continue to change. Sedai solves this at the infrastructure level, autonomously, and without requiring engineering teams to change how they work. It dramatically reduces both the cost and the headache of operating AI agents at enterprise scale."
The release includes several core enterprise AI management capabilities designed to improve operational efficiency and reliability.
Governance features include two-tier model access controls at both organization and project levels, automated fallback routing across providers, and centralized API key management.
The observability layer provides consolidated visibility into AI costs, token consumption, latency, anomaly detection, and usage attribution across projects and providers in real time.
Sedai’s smart routing engine automatically directs workloads to the most appropriate AI models based on production traffic patterns and operational requirements. The company said the platform evaluates actual enterprise queries rather than relying solely on public AI benchmark data.
For reliability, the system includes automatic retries, load balancing, and cross-provider failover capabilities to maintain service continuity.
Sedai stated that its optimization approach differs from traditional model routing systems by continuously analyzing real-world enterprise production traffic and grouping AI tasks by workload type.
The platform also trains custom AI evaluation systems using organization-specific human feedback to measure model accuracy against each company’s own standards and operational goals.
"AI agents are now table stakes for the enterprise. The real differentiator is how well you optimize them," said Mohamed Khalid, Senior Director of Engineering at GSK. "Today, model selection is still a manual process — but we're looking to Sedai to change that. By automatically routing each agent to the right foundational model for the task, we stop burning budget on over-engineered solutions for simple work, and ensure our most critical workflows always have the best tools available. This is how we stay ahead, and Sedai is the partner helping us get there."
"As an engineering executive in 2026, it's my job to make sure we're ahead of the curve when there are advances in AI," said Matt Duren, VP of Engineering at KnowBe4. "The problem is, we have agents across the entire KnowBe4 platform, with each agent having its own set of goals, and it's incredibly time-consuming for human engineers to pick the optimal model for each use case."
Sedai also emphasized that the platform addresses both AI inference optimization and underlying cloud infrastructure optimization through a unified system. In addition to AI model routing, the company optimizes cloud environments, Kubernetes clusters, and GPU infrastructure.
"If you're serious about controlling your AI costs, you need to optimize both the AI models and the underlying infrastructure, like your cloud, your Kubernetes clusters, and your GPUs," said Ethan Andyshak, SVP of Product at Sedai. "Sedai is the first solution that does all of that, autonomously. This launch extends Sedai's proven optimization engine to the AI agents running on that cloud, giving customers one platform to govern and optimize everything."
The platform is currently available in early access, with broader general availability planned later in 2026.
About Sedai
Sedai is the world's first self-driving cloud.™ Our platform optimizes your cloud resources to reduce costs, boost performance, & improve availability. All on autopilot. Under the hood, Sedai uses patented ML models to learn how your apps actually behave — from traffic patterns to dependencies to golden signals. This application intelligence lets Sedai make safe changes to achieve your SLOs. With zero IaC drift. Zero toil. And 100% confidence.
