LLM Observability & Monitoring
Monitor LLM costs, latency, accuracy and hallucinations in production. Dashboards, alerts and evaluation pipelines.
What is LLM Observability & Monitoring?
Putting an LLM into production is the easy part. Knowing it’s working correctly — that’s the hard part. Most teams have no visibility into token costs, response quality, hallucination rates or latency trends until something breaks.
We build observability systems specifically for LLM workloads: real-time cost tracking, accuracy evaluation pipelines, hallucination detection, latency monitoring and alerting. Built on open-source tools so you own your data.
From a single dashboard you’ll see exactly how your AI is performing — and get alerts before small issues become production incidents.
Why build with us
Cost Visibility
Know exactly what every LLM call costs, broken down by model, endpoint, user and feature. No more surprise API bills.
Real-Time Alerts
Get alerted within minutes when latency spikes, error rates increase or quality scores drop.
Quality Monitoring
Automated evaluation of LLM outputs against quality criteria. Catch hallucinations and regressions before users do.
Trend Analysis
Track quality, cost and latency trends over time. See the impact of prompt changes, model upgrades and traffic shifts.
What we build with LLM Observability & Monitoring
Cost Dashboard
Real-time cost tracking per model, endpoint, user and feature with budget alerts and forecasting.
Latency Monitoring
P50/P95/P99 latency tracking with automatic regression detection and alerting.
Hallucination Detection
Automated output validation against source documents with confidence scoring and human review queue.
Prompt Versioning
Track every prompt change, A/B test versions and rollback with one click if quality drops.
How we deliver
Instrument
Week 1Add observability SDK to your LLM endpoints with zero code changes.
Dashboard
Week 2Build real-time dashboards for cost, latency, quality and usage metrics.
Alerting
Week 3Configure alerts for cost thresholds, latency spikes and quality regressions.
Evaluation
Week 4Build automated evaluation pipeline with golden datasets and CI integration.
How you can work with us
Fixed-Price Project
Defined scope, fixed timeline, guaranteed deliverables. Best for MVPs and well-scoped features.
- Full scope defined upfront
- Milestone-based payments
- 8-16 week delivery
- 30-day warranty
Dedicated Developer
A senior developer assigned to your team full-time. Minimum 1 month engagement.
- 160 hours/month
- Daily standups
- Weekly demos
- Flexible scaling
Team Augmentation
A full team of developers, designers and architects embedded in your organization.
- Cross-functional team
- Quarterly engagement
- Dedicated PM
- Architecture oversight
Our LLM Observability & Monitoring technology stack
Observability
Dashboard
LLMs
Frequently asked questions
Traditional APM tools (Datadog, New Relic) don’t understand LLM semantics — token costs, prompt versions, completion quality, hallucination rates. LLM observability tracks what actually matters for AI systems.
Token usage and costs per model/endpoint, latency percentiles, error rates, completion quality scores, hallucination detection, prompt version performance, and user satisfaction signals.
Yes. We integrate with LangFuse, Helicone, or build custom dashboards on Grafana/Prometheus. We can instrument OpenAI, Anthropic, or self-hosted models.
For a single LLM endpoint, basic observability takes 1-2 weeks. Full evaluation pipelines with custom metrics and alerting typically take 3-4 weeks.