Technology / Generative AI
Complete visibility and real-time unit economics turn AI spend from an expense you monitor into an investment you can actually track a return on.
Per inference
Cost broken down by request
Per model
Anthropic, OpenAI & more
Per user
Attributed automatically
100% of GenAI spend allocated to a team, model, or feature automatically.
Read-only, zero-agent
Continuous token tracking
Cost tied to outcomes
01
GPUs cost 10 to 20 times more than standard compute, and token-based billing means the cost of a single feature can swing wildly depending on how a model is prompted, cached, or scaled that week. Most teams can tell you what their GenAI bill was last month. Few can tell you which model, which feature, or which customer actually drove it. Zolix closes that gap.
02
Zolix's zero-agent allocation engine attributes GenAI spend to its actual source automatically - no manual tagging, no waiting on engineering to instrument a new model.
03
Zolix breaks GenAI spend down into the unit economics that actually explain it - cost per inference, cost per token, cost per model, and cost per user - rather than stopping at an “18% improvement” that doesn't tell you what changed or why. This shows exactly which model, feature, or customer segment is the most and least expensive part of your AI product, and how that's trending as usage scales.
04
Cost per inference only matters in context. Zolix connects unit cost metrics to the outcomes they're tied to - revenue per customer, engagement per feature, retention per cohort - so a rising GenAI bill can be evaluated against what it's actually producing, not treated as pure expense to be minimized at any cost.
05
When GenAI spend spikes - a prompt loop gone wrong, an uncapped batch job, a model serving far more traffic than expected - Zolix alerts the team responsible with hour-level detail on when the spike started, so root cause analysis takes minutes instead of a multi-day investigation after the bill arrives.
Traditional infrastructure billing is predictable. GenAI billing isn't - token-based pricing means the same feature can cost dramatically more or less depending on prompt length, caching behavior, model choice, and traffic patterns, often changing week to week without anyone changing a single line of code.
Zolix integrates directly with the providers actually driving GenAI cost - Anthropic and OpenAI among them - so token and API spend is visible at the source, not estimated from a downstream cloud bill.
See spend broken down by model - which one is carrying the most traffic, which one is the most expensive per request, and which one might be a candidate for a cheaper alternative without a meaningful quality tradeoff.
Because token consumption can swing sharply based on prompt design and caching, Zolix tracks token spend continuously rather than reporting it as a flat monthly average that hides the swings.
What teams see after connecting GenAI workloads to Zolix
100% Allocation
every dollar of GenAI cost mapped to a team, model, or feature automatically.
24 Hours
to a first cost visibility report across every connected AI workload.
Zero Agents
Zero write access - Zolix connects through read-only access, following each provider's recommended security practices.
Yes. Spend is broken down into unit economics - cost per inference, per token, per model, and per user - instead of a single blended AI line item.
Yes. Zolix connects directly to these providers, so token and API spend is visible at the source rather than estimated after the fact.
Token spend is tracked continuously, not reported as a flat monthly average, so swings driven by prompt design, caching, or traffic changes stay visible as they happen.
No. Zolix's zero-agent engine attributes spend to its source automatically, without requiring manual instrumentation from engineering.
Yes. Unit cost metrics can be tied to outcomes such as revenue per customer or engagement per feature, so GenAI spend can be evaluated against what it's actually producing.
No. Zolix connects through read-only access, following each provider's recommended security practices, with no write access required.
Eliminate wasteful GenAI spend, understand what every model actually costs, and scale AI investment with the same discipline you'd expect from any other part of the business.