Managed fine-tuning platform
Together AI alternatives
What are the alternatives to Together AI?
The realistic field is 5 other managed fine-tuning platforms plus the adjacent categories below. Which one fits depends on why you are leaving — cost, portability, evaluation discipline and operational envelope pull in different directions, and no single alternative wins on all four.
Why teams leave
- Token-based training costs became hard to forecast as datasets grew
- A steady-traffic endpoint costs more per request on token pricing than on reserved capacity
- You want a blocking evaluation gate rather than a dashboard
Before you move: what Together AI is good at
Model breadth. If the specific base model you need is unusual, Together is more likely to have it than anyone else here, and their token-priced inference means you never think about a GPU at all.
Stay if
- You need a base model outside the mainstream open-weight families
- You would rather pay per token than reason about GPU classes
- You want fine-tuning and a large general model API from one vendor
What a migration actually involves
- 01
Export your training data — the conversation formats are interchangeable and convert without loss.
- 02
Pick a GPU class rather than a model tier. The catalogue page for your base model states the minimum for each objective.
- 03
Point your client at the new base URL. Both endpoints implement OpenAI chat completions, so no other client change is needed.
Direct alternatives
Same category, so the closest substitutes.
Fireworks AI
Inference, fine-tuning and evaluation behind one API, with fine-tuned adapters served at the same per-token rate as the base model.
Hugging Face
The model hub itself, plus AutoTrain for training and Inference Endpoints for serving. The centre of gravity of the open-weight world.
Predibase
A fine-tuning platform built around serving many LoRA adapters from a single GPU. Acquired — predibase.com now redirects to Rubrik.
OpenPipe
Capture production traffic from a large model, then train a small one to replace it on that exact distribution.
Onrup
Cost authorised before compute is leased, a blocking evaluation gate before deploy, and weights you can always download or publish. Head to head with Together AI.
Adjacent options
Different category, but frequently the right answer depending on why you are leaving.
Modal
Serverless GPU platformServerless compute for arbitrary Python, billed by the second. Not a fine-tuning product — a substrate you build one on.
Baseten
Serverless GPU platformModel deployment and serving with strong operational tooling, compliance posture and cold-start engineering.
Replicate
Serverless GPU platformA catalogue of community-published models behind one API, billed per second of compute, with fine-tuning on a subset.
OpenAI fine-tuning
Closed model APIFine-tuning of OpenAI’s own closed models, served only from OpenAI. The default first stop, and the thing most teams are trying to leave.
Frequently asked questions
Why do teams leave Together AI?
Token-based training costs became hard to forecast as datasets grew; A steady-traffic endpoint costs more per request on token pricing than on reserved capacity; You want a blocking evaluation gate rather than a dashboard.
What do I lose by moving away from Together AI?
Model breadth. If the specific base model you need is unusual, Together is more likely to have it than anyone else here, and their token-priced inference means you never think about a GPU at all.
Can I export my model from Together AI?
Fine-tuned model weights can be downloaded.
Is Onrup cheaper than Together AI?
On directly comparable dedicated H100 capacity, yes — Together publishes $5.49 per GPU-hour and our serving rate for the same class is $2.99. On per-token fine-tuning there is no like-for-like comparison, because we bill training by GPU-second rather than by token, and which is cheaper depends on your dataset size and sequence length.
Last verified 6 August 2026.
Start with the free tier
A magic link creates your account, your tenant and your first API key. No card until you ask for compute.