ModelOps
LLM Operations
& Cost Management
Monitor model performance across providers. Optimize costs without sacrificing quality. Automatic model selection based on task complexity.
Key Capabilities
Six capabilities. One LLM operations surface.
Performance Monitoring
Track latency, error rates, and quality scores across Anthropic, OpenAI, and Google in a single dashboard. See when a provider degrades before your users do — with alerting at custom thresholds.
Cost Allocation
Break down LLM spend by team, product, and use case. See exactly what each feature costs in tokens per request. Allocate cost centers accurately instead of paying one giant monthly API bill with no visibility.
Automatic Model Selection
Route each request to the cheapest model that meets your quality bar. Classify task complexity at inference time — use haiku for simple summaries, opus for complex reasoning. No manual routing rules to maintain.
Latency & SLA Monitoring
Set SLA targets per use case — customer-facing features at p95 < 800ms, async workflows at p95 < 5s. Alert when SLAs breach. Track p50/p95/p99 latency per model and provider over time.
Provider Failover
Automatic failover when a provider returns errors or exceeds latency thresholds. Define fallback chains — if Anthropic is degraded, route to OpenAI equivalents. Your users see zero downtime from upstream provider incidents.
Token Usage Forecasting
Project token consumption and cost 30/60/90 days out based on current usage trends. Set budget alerts before you hit them. No more surprise invoices — know what you'll spend before the month ends.
Who It's For
Built for any team
running LLMs in production.
You're using GPT-4 for everything because it's "the safest option" — at $4.20 per request, with no visibility into which calls actually need premium intelligence and which are simple extraction tasks that haiku handles at $0.40.
ModelOps gives you the instrumentation to route intelligently, the dashboards to see what's actually happening, and the forecasting to budget accurately — without building internal observability tooling from scratch.
Tool Replacement
What ModelOps replaces — and saves.
| Tool You're Replacing | Typical Cost | What ModelOps Does Instead |
|---|---|---|
| Helicone | $50–$500/mo | Multi-provider monitoring, cost allocation by team/product, and automatic model selection — native to your infrastructure without per-request fees that grow with your usage |
| LangFuse | Open source + ops burden | Managed ModelOps with provider failover and token forecasting — no Kubernetes cluster to maintain, no self-hosted observability stack to keep running at 3am during a provider incident |
| Custom monitoring dashboards | $0 + 2–4 engineer weeks | Production-grade LLM observability from day one — not a two-week internal project that's already outdated before it ships because Grafana dashboards don't know about LLM cost semantics |
| Using GPT-4 for everything | $4.20/req (median) | Automatic routing to the cheapest model that passes your quality bar — $0.40 for simple tasks, premium only when complexity justifies it. 70% cost reduction without quality regression |
Plus 70% reduction in LLM spend from intelligent routing. For a team spending $10k/mo on LLM APIs, that's $7k/mo back — without touching quality.
Before / After
An AI team's LLM operations. Without and with ModelOps.
- Team uses GPT-4 for everything at $4.20/request — nobody has measured whether simple tasks need that much intelligence, because there's no visibility to measure
- Monthly API invoice arrives with a single line total. No breakdown by feature, team, or use case. Finance asks "what is this?" and nobody has a good answer
- Provider outage discovered when users start reporting errors — no proactive alerting, no failover, manual incident response scramble
- Token budget exceeded mid-month with no warning — features degraded for the last 10 days while the team waits for the billing cycle to reset
- Custom Grafana dashboard built by one engineer, understood by none, unmaintained since the model API changed 3 months ago
- Automatic routing sends 68% of requests to claude-3.5-haiku at $0.40 — blended cost drops to $0.74/request, 70% reduction, zero quality regression
- Cost allocation dashboard shows exactly what each feature costs per request, per team. Finance gets a clean breakdown. Budget decisions are data-driven
- Google Gemini degrades at 2am — ModelOps automatically reroutes to Anthropic equivalents. Users see no impact. On-call is paged with the incident summary, not a user complaint
- Token forecasting projects $12k spend for the month by day 8 — team adjusts routing thresholds before the budget is hit, not after
- Multi-provider health dashboard with p50/p95/p99 latency per model, SLA breach alerts, and 90-day cost trends — out of the box, no configuration
ModelOps is in development.
Multi-provider monitoring. Cost optimization. Automatic model selection. Token forecasting. Join the waitlist to be notified when ModelOps ships.