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Token Cut

Cut LLM Token Costs With Jev Model Routing

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This is 1 of 149 launches in AI decision models and agent tooling — see how it stacks up on momentum and crowding →

196 other launches read as similar to this one →

Details

External ID
1268664
Source
PH
Company
—
Product
Token Cut
Website domain
producthunt.com
Launched
Oct. 4, 2026
Cohort
—
Upvotes
2
Upvotes percentile
0.76
Tags
Productivity, Artificial Intelligence, Tech

Description

Route every prompt through TypeSafe Jev and send it to the cheapest model that can handle it. Cut LLM token costs and latency — wire it up in minutes. Token Cut helps developers cut AI costs by automatically routing every request to the lowest-cost model capable of handling it. Powered by Jev, it understands each task first, then selects the right model across providers like OpenRouter, fal.ai, Replicate, and Kie.ai. One API, smarter routing, less wasted spend — without sacrificing quality.

Enrichment

Niche
AI decision models and agent tooling
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
llm model router for cost optimization
Manually corrected
False

Could you build this?

Partial While an API gateway proxying requests to different LLM providers is straightforward, reliably routing prompts dynamically based on complexity without degrading quality requires nuanced classification benchmarks and low-latency eval models.

What it would actually take: The architecture requires a high-performance reverse proxy (e.g., in Go or Rust) sitting between client code and LLM APIs. The hard part is the real-time classifier: determining whether a complex prompt can be safely handled by a cheap model like Haiku/Flash or demands Claude Opus/GPT-4 requires fine-tuned embedding classifiers or lightweight distilled routing models trained on vast prompt-eval datasets to avoid accuracy regression.

Competitors

Other products that read as similar to this one — 196 launches clear the similarity bar, closest 8 shown.

Attention rank: #66 of 197 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 337 days after the earliest competitor.

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