TuneLLM
Auto Finetuning without any technical hassle.
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TuneLLM - Auto Finetuning without any technical hassle.

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TuneLLM will bring down your cost by ~10-20x. -> ₹4.4k/day only Just use it like any other API provider, pick a reference model (Fable-5/GPT-5.5 etc) and let our system handle the rest autamatically.

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TuneLLM deploys inside your infrastructure and automatically distills your expensive LLM workflows into small fine-tuned models, benchmarked against the frontier model you use today, at a fraction of the cost.

Plugged in my usual prompt stack and the cost line genuinely looked off at first, had to double-check the dashboard. Responses feel snappy too, which I wasn't expecting for that price tier.

How does the routing actually decide when to use the smaller model versus the reference one, and is there any visibility into the calls being made under the hood?

How does the routing actually work under the hood to hit that 10-20x cost reduction, like are you caching responses, picking cheaper models, or something else entirely? Would love to understand before I trust it for production.

Love that you just drop in a reference model and let the system figure out the optimization behind the scenes. Cuts out so much tuning friction.

Tried TuneLLM on a small coding task yesterday and the response felt surprisingly snappy for the price. Picking a reference model and just letting it route was the easiest setup I have seen in this space.

Swapped in TuneLLM for a side project yesterday and was honestly surprised how painless the switch was, just swapped the base URL and it worked. The cost drop is the real win though, ran a full day of testing for less than a coffee.

Curious how the cost reduction actually works under the hood - is it routing between models smartly, distilling responses, or something else entirely? And does latency take a noticeable hit compared to going direct to GPT-5.5?

Swapped in TuneLLM for a small batch of chatbot requests yesterday and the cost drop is genuinely noticeable, almost half what I usually pay. Picking a reference model and letting it handle the rest feels a little too hands-off, but the responses came back clean.