Tokenless is a YC-backed startup that offers a drop-in replacement for API calls to LLMs, automatically routing requests to the most cost-effective model without sacrificing quality, potentially cutting inference costs in half.
Hi HN, Rohit here from Tokenless (<a href="https://usetokenless.com/" rel="nofollow">https://usetokenless.com/</a>), which I’m building alongside co-founders Andrew and Kev. We’re building an API gateway which routes agent traffic dynamically turn-by-turn between different models to save on AI spend.<p>The cost of AI tokens is top-of-mind for many. Companies like Uber and Salesforce have been complaining about blowing their yearly AI spend faster than expected.<p>Frontier models are amazing for dev work, but are so expensive. Open-source models are cheap and rapidly improving, closing the gap with frontier models, but aren’t quite there yet.<p>Tokenless gets you the best of both worlds–routing harder turns to smarter models only when needed, which keeps costs low.<p>Before Tokenless, I was doing a PhD at Princeton. While using coding/other agents, I constantly agonized over model choice, to make sure my AI spend was going as far as possible on my academic Cursor account.<p>At the same time, I was doing LLM research, and a small technique I developed while in recovery from NeurIPS submission season seemed to hit SOTA pretty fast. I was surprised that such simple ideas could do routing well.<p>We’ve been able to develop a version of the router that matches the performance of Claude Fable 5 at half the cost. The blog post on our website explores the technical details on how we did this (<a href="https://usetokenless.com/blog/building-tokenless/" rel="nofollow">https://usetokenless.com/blog/building-tokenless/</a>).<p>Highlights:
- Our approach queries multiple models at once and uses their progress to make decisions (this technique is novel AFAIK, let us know if you know anyone else doing this).
- Switching models doesn’t destroy the cache if the routing algorithm is aware of when the cache is hot/cold.<p>To come:
- Adding Kimi K3, all other GPT efforts and more to the router<p>Go ahead and sign up on usetokenless.com and try using Tokenless with your agent, you’ll get $20 of free credit. Here’s a demo on how to use it: <a href="https://youtu.be/sjZWriclcls" rel="nofollow">https://youtu.be/sjZWriclcls</a><p>Tokenless provides frontier-level intelligence for cheaper, so we’d love some feedback on how it feels to use, any corner cases that the router routes incorrectly, and whether you find the routing problem interesting!
# Tokenless | The router that cuts your inference bill in half
Source: [https://usetokenless.com/](https://usetokenless.com/)
A drop\-in replacement for your API calls — always routed to the right model\.
## Same quality, half the cost\.
Most calls don’t need a frontier model\. Tokenless fans out your request to a group of models and watches them think\. Once a model is clearly on track, we select it and cancel the other models, and you only pay for what you need\.
We expose an OpenAI and Anthropic compatible endpoint\. Point your models at us and get started today\!
\>refactor CLI options into an enum
time →confidence, per modelthis request$0\.0000
$0\.0000
sent toFable 5$0\.0000
$0\.0000
−52%·$0\.0101never billed
## Measured, not marketed\.
Solve rate and cost per task on public agentic benchmarks, against the best published run of each frontier model\.
solve rate
Tokenless Pro
GPT\-5\.6 Sol
Claude Opus 5
Tokenless Ultra Saver
Claude Fable 5
Gemini 3\.6 Flash
avg $/task
Gemini 3\.6 Flash
Claude Opus 5
Tokenless Ultra Saver
Tokenless Pro
GPT\-5\.6 Sol
Claude Fable 5
## See whatyou’dsave\.
YOUR MONTHLY LLM SPEND
you pay$26K/mosaved$14K/mo
you pay$26K/mosaved$14K/mo
$0$40K/mo — your bill today
YOUR NEXT 12 MONTHSTREND: RAMP AI INDEX · \+11\.0%/MO
$20K/mo$40K/mo$60K/mo$80K/mo$100K/mo$120K/mocurrent trajectorywith tokenless$344KSAVED OVER THE NEXT YEAR−12 MOTODAY\+12 MO
\*Estimates use published token prices \(including cache rates\) and editable routing assumptions in the source\. Your rate depends on your traffic\. Spend history & trend: Ramp AI Index — AI spend per employee, Jun 2025–Jun 2026, matched to the cohort your spend sits in and extrapolated at its trailing 12\-month growth\.
Built by AI researchers from Google DeepMind, Princeton, and UC Berkeley\. Backed by Y Combinator\.
## Cut the bill\.Keep the quality\.
Book a demo and we’ll run the numbers on your actual traffic — or swap two lines and see for yourself\.
Echo is a system that achieves performance comparable to the Fable model at one-third the cost by efficiently allocating inference across open-weight models. It provides free credits and requires no credit card.
A practical guide on reducing AI coding expenses by 80% through smarter token management, including multi-model routing, prompt caching, and context discipline, rather than simply switching to cheaper models.
TokenPilot reduces LLM agent costs via ingestion-aware compaction and lifecycle-aware eviction, achieving 61–87% cost reduction on PinchBench and Claw-Eval with competitive scores.
This article argues that common LLM cost advice focusing on token reduction is too shallow, and that the more impactful strategy in production is to route different workflow steps to different models rather than using a single default model.