Trace every request and token

Real-time logging, consumption metrics, and end-to-end tracing with OpenTelemetry. Know exactly who’s using what model, when, and at what cost.

Use one API for any model

OpenAI-compatible endpoint works with any provider—OpenAI, Anthropic, Azure, Gemini, and self-hosted. Switch models without changing code.

Block prompt attacks and data leaks

Enforce inline guardrails on every prompt and response. Define custom semantic rules to stop threats before they reach your models.

Centralize keys and eliminate sprawl

Stop sharing provider keys across teams. Secure them in centralized storage with enterprise IAM integration and fine-grained access control.

Set limits and stop budget surprises

Apply request and token-based rate limits to prevent budget overruns and provider throttling. Predictable costs, no surprise bills.

Sandbox shadow MCP access

MCP servers can dynamically request backend URLs - creating shadow access. Validate, sandbox, and observe every elicited URL from a single control point.

Federate agents with A2A protocol

Connect agents across boundaries with Google’s Agent-to-Agent protocol. Discover capabilities 
and collaborate securely.

Know who did what and when

Complete audit trails for every tool call and agent interaction. Essential for compliance, debugging, and incident response.

Turn REST APIs into MCP tools instantly

Drop in any OpenAPI spec and instantly expose it as MCP tools. No code changes required—just configure and go.


Apply one policy to every tool

Global rate limits, quotas, and access controls from a single control plane. Consistent governance across every MCP server.

Trace every hop and kill blind spots

Full call-chain observability from agent to tool to backend. Deep metrics, logging, and tracing for every interaction.


Isolate fine-tuned models by use case

Route specific users and workloads to specialized fine-tuned models. Right request, right model, every time.

Prioritize critical workloads

Priority scheduling by use case and model. When capacity is tight, mission-critical requests never wait in line.

Slash latency and speed up TTFT

Real-time inference metrics route requests to models 
with available capacity. Lower latency, faster time-to-
first-token.

Maximize GPU utilization

Smart routing to inference pools with llm-d integration. Isolate prefill and decode stages to extract maximum efficiency.

Route by context not just load

Context-aware routing that reads request metadata and directs traffic to the right model—including
fine-tuned variants.