Tokenless (YC S26) – Automatic model switching to save money

Hi HN, Rohit here from Tokenless (https://usetokenless.com/), 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.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.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.Tokenless gets you the best of both worlds–routing harder turns to smarter models only when needed, which keeps costs low.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.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.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 (https://usetokenless.com/blog/building-tokenless/).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.To come: - Adding Kimi K3, all other GPT efforts and more to the routerGo 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: https://youtu.be/sjZWriclclsTokenless provi

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FL score

72

out of 100

Verdict

VALIDATE

high confidence

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API gateway that routes LLM requests between models mid-conversation to cut inference costs in half while maintaining quality.

The pain

Companies like Uber and Salesforce are burning through AI budgets faster than planned. Teams face a binary choice: use expensive frontier models and hemorrhage money, or use cheap open-source models and get worse outputs. There's no middle ground for cost-conscious AI applications.

The gap

Existing solutions force you to pick one model upfront. No tool currently routes dynamically turn-by-turn based on task difficulty, and no one is preserving KV cache across model switches. The routing logic itself is unsolved, most teams just guess or manually tune.

Build angle

The founder has legitimate technical credibility (Princeton PhD, LLM research background, SOTA routing technique). The core insight is sound: hard problems need Claude, easy problems work fine on cheaper models. The cache-aware switching removes a major performance penalty. Early traction shows 50% cost reduction at parity with Claude 3.5 Sonnet. This is a real technical moat, not just API wrapping.

Strengths

  • Solves a quantifiable, immediate problem (AI spend is bleeding money right now)
  • Technical differentiation through novel routing algorithm and cache preservation
  • Founder has research credibility and built this from real pain point
  • Already shows 2x cost improvement with quality parity, not theoretical
  • YC backing provides distribution and credibility
  • Expanding model coverage (Kimi K3, GPT variants) increases addressable market
  • Low switching costs for customers (drop-in API gateway)

Risks

  • Model landscape shifts fast, routing logic may need constant retuning as new models emerge
  • Customers may not trust quality parity claims without extensive benchmarking across their specific use cases
  • Switching models mid-conversation could break context in edge cases (latency, formatting, reasoning style differences)
  • Pricing model unclear, if Tokenless takes a cut, margins compress as models get cheaper
  • Competitive response from model providers (OpenAI, Anthropic) could bundle this feature into their own platforms
  • Adoption requires API integration; won't work for web UI users or closed systems
  • Routing decisions are a black box to users, debugging failures becomes harder

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