Predictive intelligence for every LLM call.
ModelMeteriQ exists because developers deserve to know what their LLM calls will cost before they make them. Not after.
LLM costs are unpredictable.
Every developer building with LLMs faces the same problem. You pick a model, send a prompt, and hope the bill makes sense at the end of the month. Provider pricing changes without warning. Token counts vary by task. The cheapest model is rarely the best model for the job.
Existing tools track what you already spent. That is backwards. By the time you see the dashboard, the money is gone.
We built something different.
Sound familiar?
- No idea what a prompt will cost until after you run it
- Provider pricing changes break your budget assumptions
- Using one model for everything because comparing is painful
- Monthly bills with no way to trace costs to specific tasks
- Benchmarks from providers that never match real usage
Predict. Route. Prove.
ModelMeteriQ predicts what your LLM calls will cost before you make them, routes to the best model for each task, and proves the predictions were right. Every call makes the engine smarter. Every week, accuracy improves. Real data, real proof, real savings.
How ModelMeteriQ thinks.
Four systems working together to make every LLM call smarter.
Prompt Classifier
Analyzes every prompt for complexity, task type, estimated tokens, and context requirements before any model is called.
Cost Prediction Engine
Calculates expected cost across all 30+ models simultaneously. Factors in provider pricing, token estimates, and task fit scores.
Accuracy Tracker
Compares every prediction against actual usage. Scores accuracy daily. Identifies patterns where predictions drift and flags them for correction.
Weekly Recalibration
Every week, the prediction models retrain on real usage data. Provider pricing updates are incorporated automatically. Accuracy compounds over time.
Born inside AgentCLiQ.
ModelMeteriQ was built to solve our own problem. Inside AgentCLiQ, our AI business intelligence platform, we run six AI agents per customer, each making dozens of LLM calls daily across multiple providers.
We needed to know what those calls would cost before making them, and we needed to know if our predictions were right. So we built the prediction engine, the accuracy tracker, and the feedback loop.
Then we realized every developer running LLM workloads has the same problem. ModelMeteriQ is that engine, extracted and available as a standalone product.
ModelMeteriQ
- Predictive cost routing across 30+ models
- 94.7% cost accuracy with daily scoring
- Weekly recalibration from real usage data
- 7 providers: OpenAI, Anthropic, Google, DeepSeek, Mistral, Meta, xAI
- Free for all AgentCLiQ owners
What we are building next.
Q2 2026
Public launch
Standalone proxy, prediction engine, accuracy dashboard, and API access for all tiers.
Q3 2026
SDK and integrations
Python and TypeScript SDKs, webhook notifications, Slack alerts for budget thresholds.
Q4 2026
Team features
Multi-user dashboards, shared accuracy baselines, organization-wide cost analytics.
2027
Enterprise and open source
Self-hosted option, custom model support, open-source model pricing database on GitHub.