ZeroGPU
Route AI inference to small specialized models — 10× faster, 50% cheaper.
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ZeroGPU sits in front of an LLM stack and routes the high-volume, low-complexity calls, classification, tagging, summarisation, signal extraction, away from frontier models and onto small and nano language models built for those tasks.
The result is roughly a 50% drop in inference cost and about 10x faster responses on workloads that do not need frontier reasoning. It exposes an OpenAI-compatible API, so switching is a one-line endpoint change, and pricing is usage-based.
It is aimed at teams running AI features at production volume who are paying frontier prices for routine calls and want to cut cost and latency without changing their code.
AI-assisted draft, human-reviewed before publishing — see how we choose & review tools.
Why we picked it
ZeroGPU made the list for a specific, unglamorous optimization: routing classification and tagging calls to small specialized models instead of a frontier one. The 50% cost drop matters more than any single feature for anyone running high-volume, low-complexity inference at scale.
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