Monitor every layer of your AI infrastructure.

From MCP servers to inference providers, APIContext monitors the full AI stack — so you can see which providers are performing, catch schema drift before agents fail, and align every model with the right resilience and data protection boundary.

• MCP server monitoring
• Inference provider APIs
• Tool contract checks
• Multi-step workflows

125+ global monitoring locations
24/7 continuous AI infrastructure monitoring
OTEL native spans on every call
<5m to start monitoring your AI stack

End-to-End AI Infrastructure Monitoring

APIContext monitors both MCP server tool calls and inference provider APIs — validating schema, latency, availability, and contract correctness at every layer of your AI stack.

{
  "content": [{
    "type": "text",
    "text": "Refund policy...",
    "source": "docs/refunds.md"
  }],
  "isError": false
}

Inference provider monitoring

APIContext monitors inference provider APIs — OpenAI, Anthropic, Azure OpenAI, Google Gemini, and others — from the same global locations as your users. See latency, availability, and throughput side by side so you route to the provider that earns it.

Latency and availability across major inference providers

• p50, p95, and p99 per model and endpoint
• Instant alerts when a provider degrades

Coverage: 125+ data centers · all major clouds

Full payloads

APIContext runs full MCP sessions with realistic argument distributions. This is not a synthetic ping against a health check. See how your MCP servers work in production.

• Replay captured sessions from real agents
• Fuzz tool arguments within schema bounds
• Verify tools/list stability across deploys

Example Code

// simulate an AI client using your MCP server
import { mcpSession, assert } from '@apicontext/mcp';

export default mcpSession({
  server: 'https://mcp.acme.com',
  transport: 'sse',
  auth: oauth({ scope: 'mcp:read' }),
}, async (s) => {
  const tools = await s.listTools();
  assert.count(tools, 3);
  const r = await s.call('search_docs', { q: 'refunds' });
  assert.schema(r, 'ToolResult.v1');
  assert.contains(r.content[0].text, 'policy');
});

Provider alignment

Not every workload has the same availability requirement or data sensitivity. APIContext gives you the performance evidence to route high-stakes workloads to proven providers, keep sensitive prompts within compliant boundaries, and test failover paths before you need them.

• SLO verification per provider and model
• Data residency and boundary checks
• Failover readiness testing across provider pairs

Works across multi-step journeys

Connect synthetic journeys across internal and external MCP servers, HTTP endpoints, third parties, and APIs to verify end-to-end resilience.

• Monitor both internal and external MCP servers in one flow
• OAuth 2.1, PAT, mTLS, signed HMAC
• Run from global POPs or VPC collectors

Key Features

• Safety checks: Flag tools that are unexpected and resources that are out of specification.
• Latency SLOs: Separate SLOs for tools/list, individual tool calls, and inference provider endpoints — p50, p95, and p99.
• Instant alerting: Ping Slack, PagerDuty, Incident.io, ServiceNow, and more when contracts break or providers degrade.
• Per-tool uptime: Tool-level availability rather than just server-level availability.
• Provider comparison: Side-by-side latency and availability across inference providers — so you always know who's performing.
• OTEL native: Every MCP call and inference request emits OpenTelemetry spans — route signal to any compatible backend.

Start monitoring your AI infrastructure in 3 minutes.

Point APIContext at your MCP server or inference provider. We connect, enumerate, and start running synthetic sessions in under five minutes.