Same model. Better supply.
Keep the exact model and product experience. Our AI broker compares eligible supply paths and wholesale offers to find a lower-cost route.
GPT-5.4Eligible supply
AWSAzureGCP −22%OpenAIAnthropic+ More
Lower your AI cost
without changing your AI product.
Keep your APIs, agents, and harness. Give us a workload and budget; we run shadow and smoke evaluations, show what can move safely, and keep optimizing the route behind your product.
30%Inference cost optimization
24/7Supply visibility
100%Usage visibility
10MRequests
Inference · last 60s
claude-opus-4-8US222 ms
claude-sonnet-4-6UK148 ms
claude-haiku-4-5DE88 ms
gpt-5-5CA176 ms
Models from leading AI labs
For teams shipping AI products
For
AI product companies
Lower inference cost without changing your product or model integration.
- Keep the product experience
- Test before rollout
- Keep your APIs, SDKs, and harnesses
For
AI agent companies
Keep agent quality and reliability while evaluating cheaper model paths behind the product.
- Quality stays visible
- Fallback stays ready
- Cost keeps improving
For
Agent infrastructure platforms
Give customers verified lower-cost model routes without building the evaluation and optimization layer yourself.
- Usage stays measurable
- Routes stay governed
- Customer ROI stays visible
Product preview
One cost layer. Four operating views.
Evaluate cheaper paths, select the best one, govern production traffic, and see the savings you actually realized.
Why QuotaFlow
Two ways to lower AI cost. One layer to operate both.
Keep your APIs, SDKs, agents, and harnesses. QuotaFlow applies supply optimization and technical model optimization behind the product you already ship.
Same model · same quality
Same outcome. Better model mix.
Classify workloads by task and guardrail, then test more cost-efficient SOTA models for each job. Only proven candidates move forward.
Routing candidateQuality gate
ClaudeOpus 4.8→
Z.AIGLM 5.298% quality pass−31% cost
No optimization platform to build.
QuotaFlow runs the experiments, trains the routing model, and keeps approved traffic improving behind your existing gateway. You set the guardrails; we operate the loop.
Managed optimization loopYour standards
Experiment→Train router→Route live
Quality · latency · fallback stay yours
Savings calculator
See what your AI workload could save.
Choose the model family and job you run today. We show the average savings potential from better supply and better model mix—up to 50%—before you run a real evaluation.
Planning estimate only. We validate quality, success rate, latency, and fallback in shadow / smoke before production traffic moves.
Run a real evaluation →Average monthly savings potentialUp to 50%
$3.8K38% for Claude family · Customer support agent
Optimized cost 62%Current baseline
Same model · better supply14%
Same outcome · better model mix24%
Customer note
Customer testimonial
“This is the first platform I’ve seen where we didn’t need to change any code or integrate another SDK to get evaluation, testing, and real cost savings—without sacrificing quality. Every AI team should try it.”
CEOTarris AI
SLA-backedEnterprise support
No trainingOn your data
Encrypted keysServer-side vault
No data retentionEnterprise safe mode
See what your AI workload could save.
Give us a representative workload and budget. We will show the baseline, test candidate routes, and tell you what can move safely.
Start with an estimate. Move to a measured evaluation before live traffic changes.
Run a savings evaluationFAQ
Questions before you optimize.
Start with an evaluation, keep control of the guardrails, and move only the traffic that proves it can save.
QuotaFlow is a continuous AI cost-optimization layer for companies shipping AI products, agents, and agent infrastructure. It evaluates cheaper paths, routes only what passes, and measures the savings realized in production.
No replatforming. For supported gateway paths, you point existing model traffic to QuotaFlow with an endpoint or key change. Your APIs, SDKs, agents, tools, and harnesses stay in place.
You set the quality, success-rate, latency, and fallback standards. We run baseline-versus-candidate shadow and smoke evaluation; a cheaper path is eligible for live traffic only when it meets those standards.
First, we keep the same model family and find a better qualified supply path. Second, we classify the workload and test a more cost-efficient model mix for the same outcome. Both paths are measured before production changes.
QuotaFlow can operate the rollout: route a controlled share of traffic to the approved path, keep fallback ready, and increase or stop the rollout based on the guardrails you set.
The evaluation gives an estimated opportunity. Usage then reports actual tokens, spend, route and fallback mix, and realized savings after eligible traffic is live.
QuotaFlow uses the operational signals required to evaluate and operate routes. Your data is not used for training; enterprise safe mode, encrypted keys, and retention controls are available for teams with stricter requirements.
Teams with AI features in production: AI-native products, agent companies, and agent infrastructure platforms that need lower cost without making their engineers run a separate optimization stack.