Platform / AI Monitoring

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 monitoringInference provider APIsTool contract checksMulti-step workflows
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MCP · live check · every 30s
125+global monitoring locations
24/7continuous AI infrastructure monitoring
OTELnative spans on every call
<5mto start monitoring your AI stack
End-to-End AI Infrastructure Monitoring

Every tool call. Every inference request. Verified outside-in.

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.

tools/list24ms3 tools registered
search_docs(q='refunds')412msschema passed
query_db(sql='SELECT…')188ms14 rows
send_email(to='x@y')2.1sover SLO threshold
{
  "content": [{
    "type": "text",
    "text": "Refund policy...",
    "source": "docs/refunds.md"
  }],
  "isError": false
}
Inference provider monitoring

Know which AI providers are actually performing.

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
Coverage125+ data centers · all major clouds
All regions healthy
AWSAzureGoogleIBMAkamai
Full payloads

Simulate how a real AI client calls your tools.

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
mcp-session.tsTypeScript
// 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

Match each AI provider to your product needs.

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
slo-board.yaml · 30-day window5/6 on target
serviceobjectivetargetliveburn rate
Payments API
Availability99.95%99.97%0.4×
Payments API
p95 latency250ms218ms0.6×
Accounts API
Availability99.9%99.94%0.3×
Plaid · supplier
Availability99.5%99.21%4.1×
Stripe · supplier
Availability99.95%99.96%0.5×
Auth · OAuth
Error rate0.1%0.04%0.2×
config as-codereview via PRlast sync · 12s ago
Works across multi-step journeys

Connect MCP servers, endpoints, and APIs end-to-end.

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
global reachability · mcp.acme.com
Key Features

Everything you need in production.

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.

Signal ships OTEL-native into every tool your SRE team already uses

DatadogDynatraceSplunkGrafanaNew RelicHoneycombAkamaiPagerDutySlackOpsGenie

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.

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