AI Infrastructure
Once a business runs more than a couple of AI features, the plumbing starts to matter. Which model handled that request? Why did an agent fail last night? Who is spending what? The AI Infrastructure range answers those questions with gateways, routers, evaluations, monitoring and memory, all set up inside your own cloud and tool accounts.
Products in this category
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Observability & EvaluationAI Agent Monitoring Platform
Monitors agent health, failures and drift in production.
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Observability & Evaluation
AI Agent Observability
Traces every agent step, tool call and outcome for debugging.
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PlatformAI Agent Orchestration System
Coordinates multiple agents, tools and handoffs in one runtime.
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Observability & EvaluationAI Cost Audit
A review of 30 days of your AI model API spend that shows where money is wasted and gives a ranked plan…
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Observability & EvaluationAI Evaluation System
Builds test sets and automated evaluations for your AI features.
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Observability & EvaluationAI Model Evaluation Platform
Runs ongoing evaluations across models and prompts with regression alerts.
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Observability & EvaluationAI Model Performance Dashboard
Compares model quality, latency and cost across workflows.
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Observability & EvaluationAI Usage Monitoring Platform
Tracks AI usage by team, workflow and model.
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PlatformMCP Server Build
A production MCP server for one of your systems, with up to 8 tools, secure sign-in, action logging and tests in AI…
What problems does AI infrastructure solve?
Teams usually feel the pain in three places. Costs drift without anyone noticing. Prompt changes break things quietly. And when an agent misbehaves, there is no trail to follow. AI Cost Monitoring, AI Prompt Management and AI Agent Observability tackle each of these directly, so problems show up early and get fixed with evidence instead of guesswork.
- Route requests through one AI Gateway with keys, limits and logs
- Send each task to a suitable model with the AI Model Router
- Version and test prompts outside your code
- Trace every agent step and tool call
- Track usage and spend by team and workflow
How do you choose between these systems?
If many teams call many models, start with the AI Gateway, because everything else gets easier once requests flow through one place. If quality is the worry, the AI Evaluation System builds test sets for your AI features, and the AI Model Evaluation Platform keeps running evaluations across models and prompts with regression alerts.
For agents already in production, the AI Agent Monitoring Platform watches health, failures and drift, while AI Agent Logging adds structured logs to existing workflows. Building something bigger? The AI Agent Orchestration System coordinates several agents and handoffs, and the AI Agent Memory System and AI Agent Knowledge Layer give them durable memory and shared retrieval.
Why does it matter that this runs in your accounts?
Infrastructure sits close to your data and your keys, so ownership matters. These systems are set up in your own accounts, which means logs, prompts and usage records stay where your team can see and control them. Each one comes with a written Scope Card that spells out what gets built, so your engineers know exactly what to expect.
Questions buyers ask
What is the difference between usage monitoring and cost monitoring?
The AI Usage Monitoring Platform tracks how AI is used across teams, workflows and models. AI Cost Monitoring focuses on spend, with alerts when a workflow or team costs more than expected. Plenty of teams want both views. If you only need one today, pick the question you hear most often: who is using it, or what is it costing us?
Do I need the AI Model Router if I only use one model?
Maybe not yet. The router earns its place when tasks have different needs, such as quick, cheap replies next to slower, careful analysis. It sends each task to a suitable model by cost, speed and quality. If one model serves everything well today, monitoring and evaluation are usually the better first step, and routing can come later.
Will these products replace the AI features we already built?
No. They sit around your existing AI work rather than replacing it. AI Agent Logging, for example, adds structured logging to workflows you already run, and AI Prompt Management moves prompts out of your code so they can be versioned, tested and deployed separately. Your features keep working while you gain visibility and control.
Choosing between AI Infrastructure products?
Tick Compare on up to four products to see scope, price and delivery side by side. If you would rather ask, an expert can reply by email and help you pick the one that fits how you work.