An LLM feature that works in staging can behave very differently under real traffic — costs spike, latency creeps up, and a provider outage takes down a feature customers rely on. We integrate LLM APIs into your product with monitoring built in from day one, so you see exactly what's happening instead of finding out from a support ticket.
LLM features fail differently than normal API calls — cost and latency issues that traditional monitoring misses.
Spend broken down by feature and provider, tracked in real time instead of discovered monthly.
Architecture built to swap or mix providers as pricing and capabilities shift.
Know which requests are slow and why, instead of a vague complaint about the app feeling sluggish.
Retries, timeouts, and fallbacks so a provider outage doesn't take down your feature entirely.
Integrated into your current architecture rather than requiring a separate AI platform.
Rate limiting and caching designed for real production traffic, not demo-level usage.
Production reliability and visibility, not just a working API call.
Abstracted so you can route between OpenAI, Anthropic, or others without rewriting the feature.
Real-time view of how many requests are flowing through each feature and provider.
Spend attributed to the specific feature and model generating it.
Dashboards and alerts for slow responses, timeouts, and failure rates.
Reduces redundant calls and protects against runaway usage or abuse.
Automatic failover to a backup model or cached response when a provider is degraded.
Dedicated observability tools are strong options if you're already building on a specific orchestration framework and want tracing for prompt chains. We integrate monitoring directly into your product's own infrastructure and stack, which gives you cost and latency visibility tied to your actual business metrics, not just chain traces.
Yes — we typically build an abstraction layer so you can route requests by cost, latency, or capability across providers without touching the feature code each time.
We build fallback logic — retries, a secondary provider, or a cached response — so a single provider outage doesn't take the feature down entirely.
Caching, rate limits, and per-feature cost tracking are part of the integration itself, so you can see cost trends early instead of after a large bill arrives.
Tell us what you're building and what scale you expect — we'll scope an integration with monitoring built in from the start.