The TokenShunt Method.
One job: stop your engineers' AI tools from paying frontier prices for routine work — without changing the tools, and without letting quality slip.
01 · Token map
Know where the tokens go.
Before anything changes, we classify your agents' real sessions by job and price each one. Most teams find the bill is dominated by reading, not reasoning.
- Session and usage data from Claude Code, Cursor, and Copilot
- Token use broken down by team, tool, and job type
- Cost per job at current pricing
- A baseline every later saving is measured against
- 01File readsWhole files pulled into contextWhole files pulled into context
- 02Repo scansSearch, grep, find-usages loopsSearch, grep, find-usages loops
- 03BoilerplateTests, types, config, scaffoldingTests, types, config, scaffolding
- 04EditsChanges from a clear specChanges from a clear spec
- 05DecisionsArchitecture, debugging, novel problemsArchitecture, debugging, novel problems
02 · Routing layer
Rules, not suggestions.
Telling an agent to use a cheaper model is a suggestion it can drop. The routing layer enforces the route outside the agent, where it can't be skipped.
- Jobs classified before they reach a model
- Hard limits, such as large files never reaching the frontier model
- Per-repository policies on which models may see which code
- Automatic fallback to the frontier model when a gate fails
- ≈ free
Cache & repo index
Repeated lookups, symbol and file search
- Lowest
Cheap worker models
Reading large files, repo scans, boilerplate, tests
- Moderate
Mid-size models
Edits from a clear spec, small refactors
- Highest
Frontier model
Architecture, debugging, novel problems
Spotify Engineering (September 2026) reported about 90% fewer Claude Code tokens in tests of a two-model routing setup on a Java monorepo. Spotify is not a TokenShunt customer. Your savings depend on how much of your token use is reading and boilerplate, which is why we measure your own sessions first.
03 · Worker models
Cheap models for routine work, wherever it has to run.
Reading files and writing predictable code don't need frontier intelligence. Worker models handle them at a fraction of the cost, via API or on your own hardware.
- Chosen per job on measured quality and cost on your code
- Commercial small models or open-weight models
- Self-hosted when source code must stay on your network
- Swapped as better or cheaper models ship
04 · Verification
Same output, proven on your code.
Routed and unrouted runs are compared on real tasks from your own repositories. A job stays on the cheap route only where it meets your bar.
- Test set built from your own tickets and pull requests
- Checks against your tests, builds, and linters
- Blind spot-checks by your engineers
- Per-job quality gates
Tests, builds & linters
Broken or non-compiling changes
Every routed change
Routed vs. unrouted runs
Quality gaps per job type
Before rollout · new models
Blind engineer spot-checks
What automated checks miss
Weekly sample
Cost tracking
Savings drifting back up
Continuous
05 · Audit trail
Every routed change, traceable.
Each request records the job, the rule that routed it, and the model that handled it — so any change can be traced back when someone asks how it was produced.
- Routing log exportable to your SIEM
- Model recorded for every routed change
- Documentation your risk and audit teams can review
- Retention governed by your policy
- Agent request
session 41f2 · read src/billing/* - Route decision
rule: file > 350 lines → worker - Model
worker-small · v2026.09 - Change
diff 7c1e · 3 files - Pull request
PR #4812 · tests passing - Review
approved · engineer on record
06 · Ongoing optimization
Savings that keep up with the models.
Model prices and capabilities change every few months. Routes are re-evaluated against your own tasks and moved only when the evidence supports it.
- Cost and quality monitored per route
- New models tested against your task set
- Monthly savings reported against your baseline
- Changes attributed to specific routing decisions
What makes us different
Not a model vendor. Not a generalist consultancy.
Model vendors and AI coding tools earn more when you use more tokens. Generalist firms sell frameworks. We have one job: make the routine work cheap and prove it stayed right.
Swipe to compare
| Capability | TokenShunt | Typical model vendor | Typical generalist consultancy |
|---|---|---|---|
| Cuts AI coding spend without changing engineers' tools | Included | Not their incentive | Rarely |
| Vendor-neutral model choice per job | Included | Own models | Varies |
| Hard routing rules, not prompt guidance | Included | Not typical | Not typical |
| Self-hosted worker models where code can't leave | Included | Limited | Varies |
| Quality verified on your own repositories | Included | Public benchmarks | Limited |
| Savings measured against your own baseline | Included | Not typical | Varies |
| Routing log for every change | Included | Varies | Rarely |
| Runbook your platform team can own | Included | Docs & support | Limited |
Start the conversation
See the method on your own sessions.
Tell us which coding tools your engineers use and roughly what you spend. We'll show you where the tokens go and what routing would change.
We respond within 1 business day. Mutual NDA available before any data discussion.