Gauge is the AI spend intelligence platform that tells you which models to run, whether to build or buy, and when market conditions mean it's time to rebalance — continuously, not just at kickoff.
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Energy companies don't set their fuel mix once and ignore the market for two years. Most engineering teams do exactly that with their AI stack — and it's costing them.
GPT-4o dropped in price multiple times in a single year. DeepSeek disrupted the cost curve overnight. Every time the market moves and you don't, you're leaving money on the table.
Comparing providers means juggling a dozen pricing pages, different billing modes, and token rate variations. Nobody has time to do this math — so nobody does.
When a CFO asks "why are our AI costs up 40%?" or "should we hire instead?", you have no authoritative answer. Gauge gives you one you can defend.
GPT-4o for everything is costing teams 10× more than necessary. Most tasks don't need frontier capability — but without ongoing intelligence, teams never rebalance.
Token usage grows non-linearly as products scale. What was a manageable API bill at launch becomes a six-figure line item before anyone noticed the trajectory.
A decision that made sense at project kickoff may not hold six months later. Without continuous tracking, teams are always operating on outdated assumptions.
See how changing your token volume, growth rate, and team size shifts the breakeven point — and how Gauge continuously recalculates your optimal position.
Gauge continuously ranks every major model by cost-efficiency for your specific workloads. As new models enter the market, your optimal allocation updates automatically — so you're always positioned correctly.
The reason teams never switch models isn't cost — it's fear of quality regression. Gauge eliminates that fear by running your actual prompts through competing models and producing a Quality Equivalence Report you can show any stakeholder.
Three steps from "we're thinking of switching" to "here's the evidence that we can."
Upload 20–100 examples from your actual production workload — not synthetic benchmarks. The report is only as meaningful as the prompts you test. Gauge keeps them private and never uses them for training.
Your prompts are sent simultaneously to your current model and up to four alternatives. Outputs are evaluated across consistency, format adherence, factual accuracy, and task-specific quality criteria you define.
A shareable report shows quality scores side-by-side, highlights where outputs differ and whether the difference matters, and gives a clear cost-quality tradeoff so you can make — and defend — the switch decision.
When a new model enters the market, Gauge re-evaluates against your prompt history and alerts you if it changes your optimal position — without you having to do anything.
"We can't just switch, we don't know what it'll do to quality" is the sentence that keeps teams paying 10× more than necessary. The Quality Equivalence Report replaces that anxiety with a number. Either the quality holds — and you switch and save — or it doesn't — and you have evidence for why you're staying.
Sign up and run a full build vs. buy projection — with breakeven timeline, year-one cost exposure, and a clear recommendation. One free project included.
| Month | API / mo | Cum. buy | Cum. build | Delta |
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Once you're live, Gauge connects to your provider billing APIs and tracks your actual spend against the broader market — continuously surfacing when a rebalance would save you money, before you'd have noticed yourself.
Pricing is built around the reality that Quality Equivalence Reports cost us real API fees to run. We'd rather be transparent about that than bury it in a flat rate that forces us to cut corners.
If Gauge surfaces one rebalancing opportunity at your usage volume, it pays for itself within hours. Most teams reduce AI spend by 30–60% within 90 days of connecting their billing data.
Describe your workload and volume. Gauge ranks every major model by cost-efficiency for your task — so you enter the market in the right position, not the most expensive one by default.
Model the full build vs. buy decision with a breakeven timeline, year-one cost projection, and a clear recommendation you can present to finance before a dollar is spent.
Before switching models, run your real prompts through both. Gauge produces a Quality Equivalence Report showing exactly where outputs match and where they differ — so the decision is data-driven, not gut-feel.
Connect your billing APIs and Gauge monitors your position continuously — alerting you when a new model, price drop, or usage shift means it's time to rebalance your stack.
Stop defending gut-feel calls. Gauge gives you a continuously updated, data-backed position on AI spend you can walk into any board meeting with confidence.
Know the right model and the true cost before you write a line of code. Gauge removes the guesswork from scoping AI workloads.
Gauge translates task requirements into ranked recommendations with cost and quality scores — so you can make smart AI choices without needing to understand the underlying infrastructure.
At Series A–C, AI infrastructure spend is growing faster than headcount. Gauge gives you the same visibility into your AI cost position that you have over your cloud bill.
We're onboarding a small group of engineering leaders and PMs first. Join the waitlist and help shape the platform.
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