Most organizations lack visibility into AI inference and token spending across teams, models, and workflows — leading to runaway costs and missed optimization.


Token-omics serves as a single source of truth for token consumption — giving every stakeholder the clarity they need to act.
Identify and eliminate wasteful token usage across teams from day one.
Define per-team, per-model, and per-workflow budget policies with automated enforcement.
Route workloads to the most cost-effective model without sacrificing quality.
Granular analytics across every dimension: team, model, prompt type, and time.
Centralize compliance, audit trails, and access controls for AI usage.
Forecast AI costs as models and usage evolve with confidence.
As AI adoption spreads across engineering, product, data, and research — token spend fragments into dozens of invisible streams with no central tracking.
Across engineering, product, data, and research with no central tracking or visibility into usage patterns.
Redundant calls, unused models, inefficient prompts — invisible until Token-omics exposes them.
Most teams break even in under one quarter
Sustained reduction after initial optimization
Forecast AI spend with confidence. Real-time actuals vs. budget, chargebacks, and trend projections.
See per-service token usage. Catch regressions before they hit the budget. Optimize prompts with data.
Full audit trail. Model usage logs, access records, and policy enforcement history on demand.
Understand which features drive token cost. Ship AI features with cost-awareness built in.
Point your AI applications through token-omics instead of directly to model providers.
Analyze intent, complexity, token requirements, and required capabilities before selecting a model.
Enforce organizational policies for models, budgets, data, rate limits, and compliance.
Automatically select the most cost-efficient model capable of handling the request.
Log every request, token, model, cost, latency, and routing decision.
Understand where AI spend is going, identify waste, forecast costs, and continuously optimize.
Schedule a demo and see Token-omics running against your actual AI workloads within 24 hours.
token-omics doesn't just track AI spending. It helps organizations decide which models can be used, how requests should be handled, and how much each workflow should cost.
Enforce model access policies, budget limits, token caps, and usage rules before requests reach AI providers.
Match each request to an appropriate model based on complexity, capability, cost, and latency requirements.