Heulistic vs Together AI: Which Is Right for LLM Fine-Tuning

TL;DR

Together AI is a managed inference and fine-tuning platform that makes getting a fine-tuned model fast and simple. You upload a dataset, set a few parameters, and Together AI runs the job and hosts the result. Heulistic is a fine-tuning platform built around Axolotl that gives you full control over your config while handling the infrastructure underneath. This post explains what each platform does, where the ceiling is on Together AI's managed approach, and how to decide which one fits your use case before you start.

Together AI is one of the fastest ways to go from dataset to fine-tuned model. There is no infrastructure to set up, no config to write from scratch, and no environment to manage. You upload your JSONL, configure a handful of parameters through their UI or API, and Together AI runs the training job and serves the result on a serverless endpoint automatically.

For a lot of use cases, that is genuinely all you need. And for those use cases Together AI is hard to beat on speed and simplicity.

But there is a ceiling. And if your use case is anywhere near it, you will hit it and have no room to move.

What Together AI Fine-Tuning Actually Does

Together AI supports LoRA fine-tuning on a range of Llama, Mistral, and Qwen models. You provide a dataset in their expected format, set your hyperparameters through their interface, and submit the job. The platform handles everything else.

Pricing is per token processed during training. For models up to 16B parameters, LoRA fine-tuning runs at roughly $0.48 per million training tokens. For 17B to 69B models it is around $1.50 per million tokens. For 70B to 100B models it is around $2.90 per million tokens.

After training, the fine-tuned adapter is hosted automatically on a serverless endpoint. Inference costs the same per-token rate as the base model plus a small LoRA overhead. There are no dedicated instance costs unless you need guaranteed capacity.

The workflow is clean. The pricing is transparent. The time from dataset to deployed model is fast.

Where the Ceiling Is

Here is what you cannot do on Together AI's managed fine-tuning platform.

You cannot access intermediate checkpoints. Together AI runs the job and returns the final model. If you want to evaluate a checkpoint at epoch 1 versus epoch 3, or stop early because your validation loss is plateauing, that is not how Together AI works. You get the result of the full run.

You cannot customize your training config beyond their exposed parameters. If you need to adjust LoRA rank, target modules, gradient checkpointing, sequence length, or any parameter that Together AI does not surface in their interface, you cannot change it. Their defaults are reasonable for common tasks. But if your task is not common, you have no way to adjust.

You cannot guarantee your training data stays in your environment. Together AI is a shared cloud platform. Your dataset is uploaded to their infrastructure for training. For most teams this is fine. For teams working with proprietary data, regulated industries, or contracts that require data handling controls, this is a hard constraint.

You cannot run DPO, GRPO, or custom training objectives on large models. Together AI's managed fine-tuning is focused on supervised fine-tuning with LoRA. If your task requires preference optimization, reinforcement learning from human feedback, or a custom training loop, Together AI is not the right tool.

You cannot access or reproduce the training environment. There is no config file you can version-control, no environment you can reproduce, and no way to reconstruct exactly what happened if a run produces unexpected results.

For a task where none of these constraints matter, Together AI is excellent. For a task where any of them matter, you will eventually need to move off Together AI and rebuild your setup somewhere that gives you more control.

What Heulistic Does Differently

Heulistic is built around Axolotl, which means your training job is fully described in a config file that you own. Every parameter is accessible. Every setting is documented. And every run is reproducible because the config that produced it is a file you can commit to version control.

The infrastructure underneath is managed. You do not provision instances, install CUDA, or manage distributed training configuration. But the training layer above that is fully yours. You control the LoRA rank, the sequence length, the learning rate, the evaluation frequency, the checkpoint cadence, and every other parameter that determines what your model learns and how.

When a job fails, the instance terminates immediately. You update your config and resubmit. You pay only for the compute that ran. There is no instance sitting idle while you debug.

The cost estimate before job submission shows you what the run will cost before you commit to it. No surprises on the bill.

The Honest Comparison

Together AI wins on simplicity and speed for getting to a first model. If your task is well-defined, your data is ready, and the constraints above do not apply to you, Together AI is faster and requires less configuration knowledge to get started.

Heulistic wins when you need control. When your task requires config tuning beyond what Together AI exposes. When you need to evaluate intermediate checkpoints. When your data cannot leave your control. When you want a reproducible config file that describes your training run exactly.

Here is the practical decision.

Choose Together AI if: you want the fastest path to a first fine-tuned model, your task is straightforward supervised fine-tuning with LoRA, you do not need intermediate checkpoints or custom training objectives, and your data handling requirements are compatible with a shared cloud platform.

Choose Heulistic if: you need full control over your training config, you want to evaluate and compare checkpoints, your data handling requires more control over where training runs, you want reproducible runs backed by a versioned config file, or you have hit the ceiling on a managed platform and need more room to adjust.

A lot of teams start on Together AI and move to Heulistic when they hit the ceiling. That is a reasonable path. If you want to skip that step and start with full control from the beginning, Heulistic is the starting point.

You can get started with Heulistic at heulistic.com. Together AI's fine-tuning documentation is at docs.together.ai.