Eval-driven model routing
Route each request to the cheapest model that still clears the quality bar for that workflow.
For AI-heavy teams with fast-growing LLM spend
TokenShred
Cut LLM costs without degrading output quality.
We find where frontier models are overused, where prompts are too heavy, where caching is missing, and where private inference can beat per-token API pricing.
Audit focus
Routing + caching
Buyer concern
Tokenmaxxing
Infra lever
Private inference
Cost governance for AI-heavy teams
The LLM bill is not one problem. It is routing, prompts, cache misses, provider mix, and tokenmaxxing spread across teams.
TokenShred starts with usage data, then separates workflows that need frontier models from workflows that can safely move to cheaper models, cached responses, smaller context windows, or private inference.
The result is a practical savings plan that platform teams can implement and finance teams can defend.
ROI calculator
Pressure-test the savings before changing the stack.
This is a directional model. In the audit, the assumptions are replaced with real request logs, eval results, latency targets, provider rates, and GPU utilization estimates.
Savings model
Adjust the current spend and the share of usage that can be routed, cached, or moved to private inference.
What gets optimized
Cut spend where the system is wasteful. Keep quality where the work is hard.
Token and prompt reduction
Remove redundant context, shrink prompts, and set response budgets without breaking task quality.
Caching and reuse
Identify repeatable calls, stable context, retrieval patterns, and batch paths that should not hit frontier models every time.
Private inference economics
Compare API spend against hosted GPUs, private cloud, and owned hardware when volume justifies it.
Spend observability
Make shadow AI usage visible by team, workflow, model, provider, quality tier, and cost center.
Governance without slowdown
Set routing policies, quality guardrails, and cost controls that teams can actually live with.
2-week audit
A concrete path from uncontrolled spend to governed AI usage.
The audit is designed to produce implementation-ready decisions: what to route, what to cache, what to shrink, and what should remain on frontier models.
1
Measure request volume, model mix, prompt size, cacheability, latency, and quality requirements.
2
Segment workflows by risk, tolerance for smaller models, and ability to reuse prior context.
3
Run evals against candidate routing policies before changing production behavior.
4
Pilot the highest-ROI changes first: routing, caching, prompt budgets, then private inference when the math supports it.
Who you work with
Founder-led cost reduction with technical implementation behind it.
TokenShred combines Anand's growth and company-building background with Jason's hands-on systems work, so the audit can move from spreadsheet savings to production changes.
Eval-based routing instead of blanket downgrades
Token budgets and caching where quality is preserved
Private inference only when the economics are defensible
Founder and growth operator
Anand Chhatpar
Technical founder and growth engineer who has scaled products to 20M+ users, holds 8 patents, co-founded Agentplex, and led growth work behind Mystery Science before its $125M Discovery Education acquisition.
20M+ users scaled8 patents$125M exit
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Technical partner
Jason Strutz
Systems-minded engineering partner with Auth0 experience through its $6.5B acquisition by Okta, plus WorkOS experience, focused on model routing, cost-quality tradeoffs, private inference, and the implementation details that make savings durable.
$6.5B Auth0 acquisitionRouting and infraPrivate inference
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Field notes on LLM cost reduction.
Practical guides for teams comparing routing, caching, token reduction, private inference, and governance tradeoffs.
LLM cost optimizationModel routingTokenmaxxing and shadow AI spendPrompt cachingSelf-hosted inferencePrivate GPU deployment
Cost governance
What an LLM Cost Audit Should Measure
The practical usage, quality, latency, and governance signals needed before anyone can claim real savings.
Read briefModel routing
Try Model Routing Before Buying GPUs
Private inference can be powerful, but routing and caching often expose faster savings with less operational risk.
Read briefObservability
The Hidden Problem Behind Tokenmaxxing and Shadow AI Spend
The biggest LLM bill is often not one app. It is ungoverned usage spreading across teams without visibility.
Read briefFAQ
Practical answers for technical and finance buyers.
How much can a company realistically save?
It depends on traffic mix and quality requirements. Routing, caching, and prompt reduction commonly create meaningful savings before private inference is even considered. The audit produces a defensible estimate from your usage data, not a made-up benchmark.
Will cheaper models reduce output quality?
Not when routing is eval-driven. The goal is not to downgrade everything. It is to use frontier models where they matter and cheaper paths where the task does not need them.
Do you replace our existing AI stack?
Usually no. The first engagement looks for changes that fit your current providers, apps, prompts, and infra. Replacement only makes sense when the ROI is obvious.
When does self-hosting make sense?
Self-hosting starts to pencil out when volume is high, workloads are stable, latency targets are clear, and a smaller open model can satisfy quality requirements. The audit compares that path against API optimization first.
Who is this for?
AI-heavy startups, scaleups, and enterprise teams with meaningful LLM spend, uncontrolled internal usage, or a CFO asking why the AI bill keeps climbing.
Start with the bill
Request an LLM cost audit.
Share the rough spend band, the workflows driving cost, and the timeline. We will reply with the fastest path to a useful savings estimate.
No public case studies or fake benchmarks.
Quality-preserving recommendations before infra changes.
Qualified leads only: spend band, team context, and timeline.