For AI-native SaaS companies

The financial control layer
for AI-native companies.

Your model routing is based on intuition. Gauge makes it proof. Know which customers are margin-negative today. Validate every routing decision with real traffic. Catch the feature that doubles your AI bill before it ships.

No spam. Early design-partner cohort only.

See Gauge in action → How it works →
⚡ Integrates with
Bifrost / Maxim AI
LiteLLM
OpenAI API
Anthropic API
RunPod
Lambda Labs
Stripe · Chargebee · Maxio
GitHub / GitLab
AWS
vLLM
Bifrost / Maxim AI
LiteLLM
OpenAI API
Anthropic API
RunPod
Lambda Labs
Stripe · Chargebee · Maxio
GitHub / GitLab
AWS
vLLM

You've done the obvious routing.
These are the problems that come next.

Most AI-native teams have already tiered their models. GPT-4o for reasoning, Haiku for summarization — that part's done. What's not done: proving those decisions hold, knowing which customers are burning margin, and catching the feature that silently doubles your AI bill before it ships.

The problem
What you have today
What's missing
The consequence
Model decisions
An engineer chose which tasks go to which model. Seemed right at the time.
Continuous quality validation · per-customer testing · drift detection
That decision is 6 months old. Nobody knows if it's still right.
Customer margin
You know your blended AI bill. $49k last month.
Per-customer AI cost · revenue mapping · margin floor enforcement
You don't know which customers cost you more than they pay you.
New features
Engineer estimates token cost, ships the feature, moves on.
Pre-merge cost projection · real traffic simulation · margin impact
AI bill doubles. Found out on invoice day. Already in production.

Four capabilities your
stack is missing by design.

01 — Stripe + Gateway

Multi-Tenant Gross Margin Control Plane

Maps each customer's billing platform to their live gateway key. Computes running AI margin per account in real time. When margin breaches a threshold, automatically downgrades that account to a cheaper SLM — no human required.

Result

Margin floors enforced automatically. No runaway account silently burns your compute budget.

gauge.dev / margin-matrix
Customer
MRR
AI Cost
Margin
Acme Corp
Enterprise · GPT-4o
$8,400
$1,230
+75.3%
···
Buildflow
Growth · Claude 3.5
$3,200
$2,890
+9.7%
···
NovaMind AI
Enterprise · GPT-4o ⚠
$5,100
$5,720
–12.2%
⚡ NovaMind breached –10% → downgrading to Llama-3.1-8B via Bifrost
Enforcing
02 — Shadow Testing · LLM-as-Judge

Quality-Verified Model Routing

Before Gauge instructs your gateway to use a different model, it shadow tests it. 5% of real requests are mirrored to the candidate model in parallel — your users see nothing. An LLM-as-judge scores both outputs for semantic similarity. Only when the score clears your quality floor (default: 0.82) does Gauge send the override instruction. Per customer. Per task type. Continuously re-evaluated.

Result

Your users never notice. Quality is proven, not assumed, before a single live request moves.

gauge.dev / routing-table
Quality validation · per customer · per task
Task
Score
Floor
Status
Contract summary
0.88
0.82
✓ routed
Clause extraction
0.91
0.82
✓ routed
Date lookup · Customer C
0.79
0.82
— held
Legal reasoning
n/a
frontier
Re-evaluated every 500 requests · quality floor configurable per customer
03 — Lambda · RunPod · AWS

Multi-Cloud GPU Crossover Engine

Converts hourly GPU lease rates (idle vs. active) into token-per-dollar metrics and overlays them against your live API spend. Outputs a single crossover date — the exact day self-hosting becomes cheaper than your current inference providers.

Result

Turns a six-figure infrastructure gut call into a deterministic decision with a date attached.

gauge.dev / crossover-engine
OpenAI API cost
2× H100 lease (fixed)
Crossover point
wk1 wk2 day 14 mo3 $3,100 H100 lease ↑ save $6,200/mo
At 12.4M tokens/day — crossover hits day 14. Month 3 savings: $6,200/mo.
Simulate →
04 — GitHub · GitLab CI

Pre-Merge CI/CD Cost Guardrails

Installs as a GitHub App. When a PR modifies a prompt, model, or context window, Gauge simulates cost impact against the last 7 days of production traffic and posts the result as a required status check — before merge.

Result

Engineers cannot ship a $1,400/month prompt change without an auditable cost record in the diff.

github.com / your-org / ai-service / pull / 341
G
gauge-bot commented on PR #341 · feat/rag-context-expansion
GAUGE COST ALERT
Context window: +350 tokens / request
Anthropic bill: +$1,450 / month (▲ 18.4%)
Tier-1 margin impact: –4.2 pp
Agentic loop risk: elevated
✓ Approve deployment
⊘ Block & review options
// Simulation based on 847,000 sampled requests (last 7d)

Most teams find out what
a feature costs on invoice day.

An engineer estimates tokens, multiplies by the pricing page, and ships. Three weeks later the OpenAI bill doubles. By then it's in production with real users and pulling it back is painful.

How it works today
1.Engineer estimates token count manually
2.Multiplies by OpenAI pricing page
3.Feature ships to production
4.Real cost lands on invoice day — 3 weeks later
Already in production. Too late to pull back.
How Gauge handles it
1.PR opens — Gauge intercepts automatically
2.Runs the diff against 7 days of real traffic
3.Projects exact monthly cost impact in dollars
4.Posts the number as a PR comment before merge
Decision made before it ships. Not after.
PR #341 · feat/rag-context-expansion
BLOCKED
G
gauge-bot commented · just now
⚠ GAUGE COST ALERT
Context window change+350 tokens / req
Projected monthly impact+$1,450 / mo
Margin impact–4.2 pp across Tier 1
Agentic loop riskElevated
Based on 847k sampled requests · last 7 days
✓ Approve anyway
⊘ Keep blocked

What happens when you add
proof to your existing routing.

A B2B SaaS company already running model tiering — GPT-4o for reasoning, cheaper models for simpler tasks. Gauge connects, validates each decision with real traffic data, maps cost to individual customers, and enforces margin floors automatically.

// based on typical task distribution at $50k/mo AI spend · not an actual customer result

AI margin
34% 71%
Monthly AI spend
$50k $17.5k
Recovered monthly
$32,500
// modeled projection · quality floor maintained · first model instruction to gateway typically ships within days of connecting

Your margin is leaking.
Most teams find out on invoice day.

10 spots. Direct line to the roadmap. No payment required.

No spam. Early design-partner cohort only.
See Gauge in action →