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API for fine-tuning open-source LLMs and running large-scale text classification, billed by training run rather than per token.
Taylor AI is a Y Combinator (S23) startup that lets engineering teams fine-tune open-source large language models, including Llama and Falcon variants, without managing their own GPU infrastructure. Its second product is a text-classification API built for high-volume, low-latency categorization instead of calling a general-purpose chat model per request. Rather than charging per token, Taylor AI bills for the training run itself, so a fine-tuned model can be deployed and queried afterward without ongoing inference fees from Taylor. We were repeatedly blocked from loading the company's own site to confirm current pricing figures or team details, so this record relies on secondary sources including Product Hunt and press coverage of the company's funding.
No public price list found; billing is per training run rather than per token, but we could not confirm a starting figure because the vendor's site returned a bot-block on every fetch attempt.
Use tool ↗Taylor AI targets teams that want a fine-tuned open-source model instead of a per-token call to a general chat model, with billing tied to training rather than usage. We could not load the vendor's own site to verify current pricing or product details firsthand, so treat this entry as directional rather than confirmed.
Watch out: We could not independently verify current pricing or whether the product is still actively maintained beyond secondary sources -- confirm directly before committing.
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