Fireworks AI is the closest Together AI alternative. It also hosts open models, offers managed LoRA and full fine-tuning plus reinforcement fine-tuning, and gives new users $1 of free credit. The best pick depends on why you are looking:
You want to pay nothing for software: Unsloth or Hugging Face TRL on your own or rented GPU.
You need to tune Gemini: Google Vertex AI.
You want to write your own training loop without managing GPUs: Tinker.
Together requires a $5 credit purchase before you can make a single call. Fireworks gives $1 of free credit, and open-source tools like Unsloth cost nothing to try.
Serving costs for custom models
Fine-tuned models need a dedicated endpoint at $5.49 per H100-hour. Teams with light traffic often prefer to download the weights and serve them on cheaper hardware.
Closed models
Together only fine-tunes open models. If you need a tuned Gemini, GPT or Claude-family model, you have to use Google, Microsoft or Amazon.
Cluster reliability
SemiAnalysis downgraded Together's GPU clusters from Silver to Bronze in September 2026 after node failures and a health-check bug. Long training runs may be safer on Platinum-rated providers.
Billing and support complaints
Trustpilot reviewers report unexpected charges, trouble cancelling and slow support replies. The sample is small, but it is a pattern worth checking before committing large budgets.
Together AI alternatives at a glance
Tool
Best for
Price from
Free plan
Our score
Together AI for comparison
Teams that want managed fine-tuning of open models without losing the weights
Scores come from our category rankings (hover for which one). "n/a" means the tool is not in a ranking yet.
The best Together AI alternatives, reviewed
#1 · Best for managed SFT, DPO and reinforcement fine-tuning with fast inference
Fireworks AI
by Fireworks AI · from $0.50 per 1M training tokens (LoRA SFT, up to 16B) · free plan
8.1/10
Fireworks is Together's most direct rival: fast serverless inference for open models plus managed fine-tuning. LoRA SFT costs $0.50 per million tokens for models up to 16B, $3 for 16B to 80B and up to $10 above 300B, with DPO and full fine-tuning at roughly double. It also offers reinforcement fine-tuning. New users get $1 of free credit. On-demand H100 or H200 deployments cost $8 an hour, and fine-tuned LoRA models run on those dedicated deployments. It ranks #6 in our fine-tuning ranking.
Versus Together AI:Adds reinforcement fine-tuning and a small free credit; costs more per training token on small models.
#2 · Best for fine-tuning for free on one GPU or a free notebook
Unsloth
by Unsloth AI · from Free (open source) · free plan
8.6/10
Unsloth is an open-source library that makes fine-tuning faster and lighter: it claims 2x faster training with 70% less VRAM and no accuracy loss. It supports LoRA, QLoRA, full fine-tuning, DPO and reinforcement learning such as GRPO, and provides free Google Colab and Kaggle notebooks for models like Gemma 4, Qwen3.5 and gpt-oss. The core is Apache 2.0. You pay only for your own GPU, which can be a free notebook for small models. It ranks #2 in our fine-tuning ranking, just behind Together.
Versus Together AI:Free and runs anywhere, but you manage the GPU, data pipeline and serving yourself.
#3 · Best for mL engineers who want every post-training method
Hugging Face TRL
by Hugging Face · from Free (open source); Jobs from $0.40/hour · free plan
8.4/10
TRL is Hugging Face's open-source library for post-training, and it is the reference implementation for methods such as SFT, DPO and GRPO. It is free under Apache 2.0. If you do not have a GPU, Hugging Face Jobs runs your script on its hardware, billed per minute: a T4 costs $0.40 an hour, an A100 80GB $2.50 and an H200 $5.00, and PRO, Team or Enterprise credits can pay for Jobs. You get full control over the training loop and your weights stay on the Hub.
Versus Together AI:The widest choice of training methods for free; far more hands-on than Together.
#4 · Best for tuning Gemini, or open models inside Google Cloud
Google Vertex AI
by Google Cloud (now Gemini Enterprise Agent Platform) · from $1.50 per 1M training tokens (Gemini 2.5 Flash-Lite)
8.0/10
Vertex AI, which Google now sells as Gemini Enterprise Agent Platform, is the way to fine-tune Gemini models, something Together cannot do. Supervised tuning costs $1.50 per million training tokens for Gemini 2.5 Flash-Lite, $3 for Gemini 3.1 Flash-Lite, $5 for 2.5 Flash, $10 for Gemini 3.5 Flash and $25 for 2.5 Pro. It also tunes open models such as Gemma 3. For companies already on Google Cloud, it keeps data, billing and security in one place.
Versus Together AI:Tunes Gemini and fits Google Cloud; far higher per-token prices than Together's open models.
#5 · Best for researchers who want to write their own training loop without managing GPUs
Tinker
by Thinking Machines Lab · from $0.44 per 1M training tokens (Qwen3-8B)
7.6/10
Tinker is an API from Thinking Machines Lab for LoRA fine-tuning where you write the training loop in Python and Tinker runs it on its GPUs. That gives researchers custom losses and reinforcement-learning setups without cluster work. Prices are per million tokens for prefill, sampling and training: Qwen3-8B training costs $0.44, GPT-OSS-120B $0.737 and Qwen3.5-397B-A17B $6.60, with an 80% discount on cached prefill and checkpoint storage at $0.10 per GB-month.
Versus Together AI:Much more control over the algorithm; smaller model list and no managed serving.
by Runpod · from H100 SXM $2.69/hour (Community Cloud)
8.2/10
If you are willing to run Unsloth, Axolotl or TRL yourself, RunPod rents the GPUs for less than Together's clusters: an H100 SXM costs $2.69 an hour on Community Cloud or $3.49 on Secure Cloud, billed per second with no egress fees, and an RTX 4090 costs $0.34. Its Serverless product can then host your fine-tuned model and scale to zero between requests, which can be cheaper than a Together dedicated endpoint for light traffic. It ranks #2 in our GPU cloud ranking.
Versus Together AI:Cheaper hardware and scale-to-zero serving; you do all the training and deployment work.
Many teams mix these: they prototype with Unsloth on a free notebook, run production fine-tunes on Together or Fireworks, and serve on whichever option fits their traffic. See our full best LLM fine-tuning platforms ranking.
Switching tips from our editors
Download your fine-tuned weights or LoRA adapters from Together before you leave, so you can serve them anywhere.
Keep your training data in a standard JSONL chat format. Fireworks, Unsloth, TRL and Tinker all accept similar layouts, so the same file moves with you.
Point your code at the new provider's OpenAI-compatible endpoint by changing the base URL, key and model name, and compare outputs on a fixed test set.
Stop or delete dedicated endpoints and clusters first: they bill by the minute or hour until removed.
Use up or keep your prepaid credits in mind; Together credits do not expire, so you can keep a small balance for occasional use.
An AI model whose weights are published, so anyone can download, run and fine-tune it, such as DeepSeek, Qwen or gpt-oss.
Fine-tuning
Training an existing model further on your own examples so it gets better at a specific task or style.
LoRA
Low-rank adaptation: a cheap way to fine-tune that trains a small set of extra weights instead of changing the whole model.
Cached input
Input tokens the provider has recently processed, such as a repeated system prompt, charged at a lower rate.
Dedicated endpoint
A model deployment on GPUs reserved for you, billed by time rather than by tokens.
Preemptible GPU
A cheaper GPU that the provider can take back at short notice, suited to jobs that save progress and can restart.
Frequently asked questions
What is the best alternative to Together AI?
Fireworks AI is the closest match, with open-model inference, managed fine-tuning and $1 of free credit. For free fine-tuning, use Unsloth; to tune Gemini, use Google Vertex AI.
Is there a free alternative to Together AI for fine-tuning?
Yes. Unsloth, Hugging Face TRL, LLaMA-Factory and Axolotl are free and open source. You only pay for the GPU, and Unsloth provides free Colab notebooks for small models.
Is Fireworks AI cheaper than Together AI?
For small-model LoRA fine-tuning, no: Together charges $0.34 per million tokens against Fireworks' $0.50. Compare inference prices model by model, since they differ.
Can I fine-tune GPT or Gemini on Together AI?
No. Together fine-tunes open models only. Use Google Vertex AI for Gemini or Microsoft Foundry for OpenAI models.
What is the cheapest way to serve a fine-tuned model?
For light or bursty traffic, a scale-to-zero option such as RunPod Serverless or your own hardware usually beats a dedicated endpoint billed by the hour. For steady, heavy traffic, a dedicated endpoint can be cheaper per token.
Sources
Every fact on this page comes from public information. Vendor figures are labelled as vendor claims.