AI Pipeline Observability · Building Toward GA
Your LLM API is an endpoint. Monitor it like one.
AI inference endpoints fail in ways standard uptime checks miss: token limits, context window errors, embedding service timeouts, queue backpressure. up0 monitors them exactly like it monitors a server or an API: the same 5 regions, the same incident lifecycle, the same alerting channels, the same status page. There's no separate AI dashboard to check.
Same monitor types, same pipeline
Five AI monitor types. Zero separate infrastructure.
LLM API latency and token throughput
Track p50/p95/p99 latency and token throughput per model and provider.
Integrates as: A latency breach opens an incident the same way an HTTP timeout does: same SLA clock, same escalation policy, same on-call rotation.
Vector database health
Health checks and query-latency monitoring for Pinecone, Weaviate, Qdrant, and similar vector stores.
Integrates as: A failed health check updates the same status page component as your other backend dependencies, not a separate AI status page.
Embedding service availability
Monitor embedding endpoints independently from the models that depend on them.
Integrates as: Monitored as its own endpoint, so the incident names the layer that actually failed instead of a generic "AI degraded."
Model gateway and proxy checks
Monitor the routing layer in front of your models, gateways, proxies, and load balancers, not just the models themselves.
Integrates as: Confirmed from multiple regions before alerting, exactly like a load balancer check: no special-cased AI threshold logic.
Inference queue depth alerting
Get alerted when queue depth climbs before it turns into user-facing latency or timeouts.
Integrates as: Feeds the same workflow automation engine that already syncs status pages on monitor and incident state changes.
One incident, start to finish
What happens when an AI endpoint fails
Monitor detects
Your embedding service times out. The check fails from 2+ regions and crosses your threshold: the same confirmation logic as a database or load balancer check.
Incident opens
Lifecycle starts automatically with an SLA clock. On-call is notified by email, SMS, Slack, or webhook: the same channels already configured for your infrastructure monitors.
Status page updates
Workflow automation syncs your public status page and closes the loop on recovery. No manual edit, no separate AI status page to maintain.
One platform, not two
An LLM monitor and an HTTP monitor are the same object.
Teams running AI in production often end up watching it with a tool separate from the one covering the rest of their infrastructure: a status pager for uptime, something else bolted on for model health. On up0 there's no second tool. An AI monitor and an infrastructure monitor share the same incident lifecycle, the same alerting channels, the same status page, and the same workflow automation engine. The only difference is what gets checked.
Get early access to AI pipeline observability
Join the waitlist and we'll email you when it's time to add an LLM, vector database, or embedding service monitor to the same dashboard you'd use for a server or an API.