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Repeated data becomes recurring cost.
Search results, logs, and intermediate output often travel into subsequent turns whether they're still useful or not.
AI COST OPTIMIZATION
Minimize tokens.
Reduce AI costs.
MinToks is building a provider-independent optimization layer for AI applications and agents. Reduce wasted context, make smarter inference decisions, and improve the cost of getting the job done.
THE ECONOMICS OF AIILLUSTRATIVE FLOW · PRODUCT VISION
AI WORKLOADDECISION LAYER
InstructionsPRESERVE
Redundant tokensREVIEW
Relevant evidenceKEEP
Excess historyREVIEW
Cache & provider choicePROTECT
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MINTOKSPOLICY · QUALITY · COST
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EXECUTIONCompute and context aligned.
FEWER WASTED TOKENS✳LOWER INFERENCE COSTS✳PERFORMANCE MATTERS
THE ECONOMICS OF AI
Your AI bill has room to shrink.
At scale, wasted tokens, inefficient context, missed cache opportunities, and expensive model choices compound across every request. MinToks is designed to address the whole equation.
TOOL OUTPUT
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Not every task needs the largest model.
Model and provider selection should match the task, the data, and the cost of errors, not just a default configuration.
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Smaller isn’t always cheaper.
Compression can destroy cache reuse. A shorter prompt can cost more. The bill, latency, and task result all matter.
THE PRODUCT WE'RE BUILDING
One place to cut waste across your AI stack.
Minimize unnecessary tokens, select relevant memory, protect valuable caches, and route workloads to suitable models. Apply the right optimization, or leave the request unchanged.
YOUR APPLICATIONAgent / RAG / WorkflowAlready running
MINTOKSOptimization policyInspect · Decide · Check
YOUR CHOICEModel / ProviderDirect, gateway, or private
IN RESEARCH
Context selection
Find and retain relevant evidence instead of passing along every retrieved paragraph or tool result.
IN RESEARCH
Memory efficiency
Use the facts an agent needs now. Keep provenance and changing decisions in view.
PROTOTYPE
Model & provider routing
Evaluate models and providers against cost, latency, permitted regions, capabilities, and session continuity. The current selector runs offline only.
PLANNED
Cache-aware choices
Account for cached input, stable prefixes, and provider pricing before declaring a win.
PLANNED
Outcome checks
Compare cost and latency alongside answer quality. If a strategy makes the task worse, it shouldn't ship.
PLANNED
Portable execution
Use direct APIs, an existing gateway, or private and local inference. The long-term policy should be yours to run and move.
Works with your stack, by design.Routing could work with your existing gateway or eventually execute through MinToks directly. Neither path should lock your policies to a vendor.
THE METRIC THAT MATTERS
Put a number on wasted tokens.
Small improvements compound across millions of requests. Change the assumptions to see the effect on your own input spend, with existing cache discounts accounted for.
The target is lower cost per successful task, not merely a shorter prompt.
THE COST OF EXCESS INPUTSCENARIO MODEL
Use your own request volume and model pricing to see what reducing unnecessary input could be worth.
POTENTIAL MONTHLY INPUT COST REDUCTION$0
Baseline input spend: $0After scenario: $0
Input costs only. Assumes cached tokens cost 10% of standard input, with existing cache hits preserved. Results depend on your workload, optimization overhead, and answer quality.
GOOD QUESTIONS
Questions worth asking.
These are the things we'd ask before trusting another component with production AI traffic.
Often, yes. Stable prefixes can be much cheaper on a cache hit. But caching doesn't decide which search results, logs, or historical facts deserve to enter the next request. MinToks is designed to evaluate both, because a shorter prompt can cost more if it breaks cache reuse.
They already solve important parts of the problem. MinToks aims to coordinate context, model, and provider choices using an existing gateway, direct endpoints, or an optional future MinToks route. The policy should stay independent of the transport vendor.
The only defensible way is to test. Our roadmap calls for baseline comparisons, source provenance, task-success checks, and a safe no-change fallback. The full optimization platform is still being built. We will not claim that an optimization preserves quality until task-based evaluations support it.
That’s the intended architecture: policies that work alongside existing gateways and private inference. Production integrations are still being developed. For pilot discussions, describe your workload without sharing confidential data.
PILOTS · DEVELOPER ACCESS · INQUIRIES
Make AI cost less. Get in early.
We're inviting companies with meaningful AI workloads and developers building demanding agents or applications to help shape MinToks. Tell us what you're working on.
↗Companies
Explore an optimization pilot for your AI workloads and infrastructure.
↗Developers
Register interest in early integrations, SDK access, and technical previews.
MinToks is in development. Joining the waitlist doesn't imply immediate access or guaranteed savings.
EARLY INTERESTMINTOKS / CONTACT