Input 0Cached 0Output 0Reasoning 0
Inference control layer Live preview
Keep your AI up to speed without running up the bill.
TokenYeet keeps the right information moving from request to request and model to model. It protects useful caches, rebuilds working context after a cold start, and chooses the right route each time. Your complete record remains under your control, while models receive only what they need.
No raw prompt storage. No training on customer data.
TYInference control
Requestcontext + intent
Routemodel + effort
check readiness
price the path
respect cache
One decision per requestGate, model, effort, context, and output policy
Measured where possibleProvider usage stays separate from estimates
Shadow-first rolloutProve value on your workload before enforcement
Try it
Live conversationOne answer—from the route TokenYeet chose.
Waiting for turn 1No canned answer is waiting.TokenYeet will inspect the request, choose a route, and make one live call.
Cache writes 0Output allowance 1,000Turn cap 10Server key
Difference $0.00000Premium-model reference $0.00000
How to read these numbers +
Observed: token and cache counts returned by the provider for the call you just made.
Estimated: list-price arithmetic. The premium-model reference reprices the same observed token counts; it is not a second answer, a quality comparison, or an invoice.
Context: this short public chat deliberately preserves full history. Validated bulk/cold-resume compression is reserved for the harness and shadow pilots.
Shadow-first activation
Test before you buy. Audit your use today.
A pilot records what TokenYeet would gate, retain, route, or shorten alongside the path your application already uses. Enforcement comes only after the economics and answer quality are reviewed on your own workload.
- Per-turn route, context decision, usage, cost, and latency
- Observed provider data kept separate from projections
- Raw prompt storage disabled by default
- No automatic activation before workload-specific review
example decision record
request actionable
model economy
reasoning low
context full history retained
provider usage observed
price delta estimated
quality gate pending workload reviewOne layer / six controls
Control the request before the request controls the bill.
Each lever is useful on its own. The controller chooses only among policies that have been enabled and validated for the workload.
Request gate
Catch empty, dangling, or unsupported requests before they become paid model calls.
Model route
Start bounded, low-risk work economically and escalate complex or consequential requests.
Reasoning effort
Choose how much model work the request warrants instead of leaving every call at one default.
Cache-aware context
Keep short warm threads intact. Do not rewrite history when provider caching is already the cheaper path.
Portable state
For validated bulk and cold-resume workloads, carry forward facts, decisions, constraints, and open questions.
Transparent telemetry
Record the chosen path, provider token counts, cache activity, latency, and estimated list-price cost.
Open testing
Don’t take our word for it.
This site is a working test bed. Run the controller, inspect every decision, and bring your own workload. We separate observed results from estimates, publish what we learn, and invite early teams to help determine what earns production use.
30 / 30current controller tests passing
8.8%one synthetic tool-heavy cold-resume trajectory
Retestingthe position-biased comparison we withdrew
Open questionquality equivalence over long conversations
Observedlive provider token and cache counts
Estimatedmodel-price comparisons—not an A/B answer test
Where to test first
Start where AI costs the most.
Agentic coding
Avoid repeatedly sending old logs and dead-end attempts while preserving the active task and current changes.
Measure before enforcingSupport and operations
Route routine work economically and escalate sensitive or complex cases before a response is generated.
Measure before enforcingDocument workflows
Check required files and fields before legal, finance, or back-office inference begins.
Measure before enforcingAI platforms
Test model, effort, context, and output policies in shadow mode before they touch customer traffic.
Measure before enforcingWorkload pilot
Test TokenYeet on your own workload.
01
Analyze traces locally. Establish a private baseline without sending records to another model provider.
02
Observe in shadow mode. Record TokenYeet decisions beside the path the application already uses.
03
Approve one lever at a time. Activate only policies that improve cost per successful task.
Straight answers
You’ve got questions. That makes both of us!
What does the live console really do?+
It makes one real OpenAI Responses API call after TokenYeet gates the request and selects a model, reasoning effort, context path, and output policy. The answer is not canned.
Where can savings come from?+
Depending on the workload: avoided calls, lower-cost model tiers, targeted context, or compact durable state. The console separates observed usage from estimated price comparisons.
Why does the console often keep the full conversation?+
Because short warm chats are exactly where provider prompt caching can already work well. The current evidence does not justify rewriting them merely to show a smaller token number.
Is the percentage a measured savings result?+
No. It reprices the same provider-reported token counts at premium-model list rates. It is an estimated model-price difference, not a second answer, quality test, invoice, or production claim.
What is the evidence status?+
The economics and controller accounting have been reproduced. Quality equivalence and long-horizon savings remain unresolved. One synthetic tool-heavy cold-resume trajectory showed an observed 8.8% cost reduction.
What happens to prompts?+
The public demo sends prompts to OpenAI with store=false. TokenYeet does not save raw prompts. The demo is protected by per-visitor limits, a durable daily spend ceiling, and a provider timeout.