Open-source alternatives to Together AI: 5 hosted model training and inference tools compared
Replacing Together AI means taking the hardware and the training stack in-house, so the trade is control over your prompts, your datasets and your model versions against running drivers, queues and upgrades yourself. Most readers should start with huggingface/trl when post-training is the goal, and with vllm-project/vllm when serving is.
What Together AI does, and why people look elsewhere
Together AI is a hosted platform for open-weight models: it serves them over an API and also runs fine-tuning jobs on rented GPUs through its own tooling.
- fine-tuning runs on the vendor's GPUs and its own version of the training stack
- the prompts and datasets sent for training are processed on the vendor's side
- the model catalogue and the serving stack are the vendor's, so a model version moves when the vendor moves it
The candidates at a glance
| Project | Licence | Maintenance | Language | GitHub stars | Install |
|---|---|---|---|---|---|
| huggingface/trl | Apache-2.0Permissive: commercial use allowed | Commits in the last dayLast push October 23, 2026 | Python | 19,591 | See analysis |
| axolotl-ai-cloud/axolotl | Apache-2.0Permissive: commercial use allowed | Commits in the last dayLast push October 23, 2026 | Python | 12,625 | See analysis |
| unslothai/unsloth | Apache-2.0Permissive: commercial use allowed | Commits in the last dayLast push October 22, 2026 | Python | 78,428 | Command documented |
| vllm-project/vllm | Apache-2.0Permissive: commercial use allowed | Commits in the last dayLast push October 23, 2026 | Python | 95,239 | Command documented |
| sgl-project/sglang | Apache-2.0Permissive: commercial use allowed | Commits in the last dayLast push October 23, 2026 | Python | 37,671 | Command documented |
Each alternative in detail
huggingface/trlTrain transformer language models with reinforcement learning. Built on top of the Transformers ecosystem, TRL supports a variety of model architectures and modalities, and can be scaled-up across various hardware setups.
- Best for
- A team already inside the Transformers ecosystem that wants one library to run supervised fine-tuning, GRPO, DPO or KTO on a standard dataset without writing a training script.
- What you give up
- It is a library, not a service. You supply the GPUs, the job queue and the checkpoint storage, and the trainers assume a model and a reward function that fit the Transformers family. Our analysis of trl says to check the memory footprint and the generation settings for the algorithm you plan to use before you commit a cluster to it.
- Moving from Together AI
- TRL's trainers read their data through load_dataset from the datasets library, so a dataset already stored in that format can be handed to a trainer directly instead of being reshaped for a hosted tuning endpoint.
axolotl-ai-cloud/axolotlGo ahead and axolotl questions
- Best for
- A team that wants to fine-tune a newly released open-weight model and would rather declare the run in a YAML file than write the training loop in Python.
- What you give up
- Axolotl gives you no endpoint to serve from and no scheduler to place jobs on, so the queue, the machines and the checkpoint copies are yours to build. Our analysis of axolotl warns that the advanced parallelism options assume multi-GPU hardware and that the fast release cycle means a config can change shape between versions.
unslothai/unslothUnsloth is a local UI for training and running Gemma 4, Qwen3.6, DeepSeek, Kimi, GLM and other models.
- Best for
- A solo developer or small team that wants a desktop app and a web interface for training and serving on one workstation, rather than a cluster and a container fleet.
- What you give up
- The performance figures inside the repository are written two different ways, so you have to measure on your own hardware before relying on them. The Windows on ARM download is pinned to an older beta while other platforms follow the latest tag, and the piped install script gives you no checksum to verify against.
- Moving from Together AI
- Unsloth serves models through an OpenAI compatible API and exports trained checkpoints to GGUF, NVFP4 and FP8, so a client written against a hosted chat endpoint can be pointed at the local server.
- First install step
- Full setup guide
curl -fsSL https://unsloth.ai/install.sh | sh
vllm-project/vllmA high-throughput and memory-efficient inference and serving engine for LLMs.
- Best for
- A team with accelerators that wants one OpenAI-compatible server in front of a specific model, and expects the installation to be the hard part of the project rather than the serving itself.
- What you give up
- There is no training path here, so a fine-tuning job still needs another tool. The build pins torch to an exact version and caps setuptools below a fixed release, and compiling from source needs cmake, ninja, setuptools-rust and a Rust toolchain.
- Moving from Together AI
- vLLM ships an OpenAI-compatible API server alongside Anthropic Messages API and gRPC support, so a client that talked to a hosted endpoint can be repointed at the local process once the model is loaded.
- First install step
- Full setup guide
uv pip install vllm
sgl-project/sglangSGLang is a high-performance serving framework for large language models and multimodal models.
- Best for
- A serving team running agentic workloads or reinforcement learning rollouts across several nodes, where multi-node throughput matters more than a simple single-box endpoint.
- What you give up
- The launch command, the KV cache choice and the RL trainer live in other repositories, so a working setup takes more than the container image. There is no Windows install path, and the published tag line moves quickly, so an unpinned image changes underneath you.
- First install step
- Full setup guide
docker pull lmsysorg/sglang:latest
TRL and Axolotl take over the training half that Together AI runs on rented GPUs
Together AI runs fine-tuning jobs on rented GPUs through its own version of the training stack, and it serves the resulting models over an API. Two of the five candidates here take over that training half. huggingface/trl is a library that wraps the Transformers trainer in one class per algorithm: SFTTrainer for supervised fine-tuning, GRPOTrainer for group relative policy optimization, DPOTrainer for paired preferences, KTOTrainer for binary feedback, and a reward trainer for scoring models. axolotl-ai-cloud/axolotl approaches the same job from the other side. Instead of a training script you describe the run in a YAML configuration file, and the repository keeps pace with new model releases by adding architectures as they appear.
The difference is who makes the decisions. With trl you write the loop, so you can change the objective, the reward function or the batching without leaving Python. With axolotl the configuration file is the contract: a run is reproducible from a file you can check into a repository, and the price is that the vocabulary of that file moves when the project changes shape. Our analysis of axolotl notes that the advanced parallelism options assume multi-GPU setups, so the configuration that matters to you may be the one your single card cannot run.
The other three candidates, unslothai/unsloth, vllm-project/vllm and sgl-project/sglang, serve models rather than train them. Nothing on this page replaces the managed console that Together AI puts around a training job, and none of the five is a hosted service of any kind.
Unsloth, vLLM and SGLang cover the serving half, each aimed at a different scale
unslothai/unsloth arrives as four separate surfaces rather than one install: a native desktop app, a web interface called Studio, a Python package and a Docker image. That breadth suits a developer who wants to fine-tune a model and then serve it from the same workstation, and the repository documents an OpenAI compatible API, export to GGUF, NVFP4 and FP8, and a launch command that wires local models into coding agents. Its own analysis notes that the base package carries no deep learning framework, so the extra that supplies the training stack is the part you have to choose deliberately.
vllm-project/vllm is the opposite shape. It is a serving engine that concentrates on throughput and memory efficiency for one model at a time, with paged attention, continuous batching, chunked prefill and prefix caching under an OpenAI-compatible server. The README reduces the install to a single command, and our analysis of vllm says to expect that install to be the hard part, because the build configuration pins torch to an exact version and caps setuptools below a fixed release.
sgl-project/sglang sits between them in ambition. It is a Python and Rust framework aimed at agentic workloads, reinforcement learning rollouts and multi-node serving, with a wide set of quantization formats and speculative decoding. It is the right shape for a team that already runs a cluster. For one developer on a laptop it is the heaviest of the three, and the pieces most teams need next, a launch line, a KV cache choice and an RL trainer, sit in other repositories rather than this one.
What self-hosting costs: build pins, floating image tags and a queue you now own
A hosted platform bundles the accelerator, the queue and the serving stack into one thing you call. Self-hosting splits it into three, and each candidate hands you a different share of the work.
The libraries are the lightest. Installing trl is a pip command, but the hardware, the accelerator image and the distributed launch configuration are yours, and the README points at Accelerate with DeepSpeed for multi-node runs and at PEFT for quantization and LoRA when memory is tight. Axolotl is uv-first and ships pinned dependency versions in the repository that you are told to check against your own environment, and the documentation assumes you have thought about disk and VRAM for whichever parallelism strategy you choose.
The serving engines cost more. Our analysis of vllm records that the version string comes from a git describe filtered to v[0-9]*, so a shallow or untagged checkout has nothing to derive and a source build needs cmake, ninja, setuptools-rust and a Rust toolchain. SGLang installs by pulling a container image, which pins nothing at all, and its 0.5.x line shipped three tags in four weeks. Our analysis of sglang says to pin the exact version or image digest you intend to run.
Unsloth adds a supply chain question rather than a build one: for unattended environments its analysis recommends the Docker image or a fixed GitHub Releases download instead of the piped install script, because that script carries no checksum. One more cost applies to all five: the weights still come from a model hub, so a fully private deployment also means mirroring the model files.
Apache-2.0 across all five, and what a company keeps by running the code itself
Every candidate on this page carries the same licence, Apache-2.0, and that shapes the compliance question in a useful way. A copyleft candidate with a network use clause, an AGPL-3.0 project for instance, obliges you to publish the changes to a modified version that you host for other people. An Apache-2.0 candidate does not. You can patch vllm-project/vllm for your own kernels, change the batching behaviour of sgl-project/sglang or adjust the reward functions in huggingface/trl and run those modified copies behind your own firewall without releasing the source.
Two conditions come with that permission. The licence grants a patent and requires you to keep the notices and the licence text with any redistribution, so a container image you hand to another team still has to carry them. And it covers the code, not the data around it. The licence of the base model, the licence of the dataset you train on and the terms of whatever mix together AI or another hosted service hands you when it produces a checkpoint are separate questions that the Apache-2.0 grant does not answer.
For a company with data residency obligations, the practical value of moving here is not the licence at all. It is that prompts, documents and training data stop leaving your network and start landing on machines you control. The licence simply makes sure that control does not turn into a new obligation to publish your stack. The harder part of the decision is the operational one on the previous section: pins, tags and a queue that now answers to you rather than to a vendor.
Choosing by scenario, and where Baseten and Cerebras readers land
A solo developer with one workstation starts with unslothai/unsloth, because a desktop app and a local API on one port remove the queue question entirely, and reaches for huggingface/trl when the task is a supervised fine-tune rather than a chat endpoint. A small team with a GPU box and a need to serve traffic picks axolotl-ai-cloud/axolotl for the training half, where a YAML file is easier to review and reuse than a script, and vllm-project/vllm for the serving half.
A company with compliance requirements should read the operational section above twice. The gain is that prompts, datasets and documents stay on hardware you own. The cost is that a toolchain upgrade becomes your change rather than the vendor's, and the pin discipline in the build files becomes a piece of internal process. All five licences are permissive enough that none of this turns into a publication duty.
A developer embedding the stack in a product should look at the OpenAI-compatible server in vllm-project/vllm first, because an existing client usually needs only a base URL change, and at sgl-project/sglang when the workload is agentic or rollout heavy. Neither gives you a training pipeline, so pairing one with trl or axolotl is the normal shape.
The same shortlist answers the Baseten and Cerebras questions, because the underlying job is identical: serve open-weight models from hardware you control. Where a candidate is the weaker choice is specific. Axolotl is wrong for a team that needs a stable API between versions. SGLang is wrong for a single box. Unsloth is wrong when every run has to be reproducible from a pinned digest. vLLM is wrong when the missing piece is training.
Bottom line
Take huggingface/trl and vllm-project/vllm together when you are replacing a training and serving stack in one move, axolotl-ai-cloud/axolotl when the run should be described in a file, and unslothai/unsloth when the whole job fits on one workstation. Before committing, check the pinned torch and setuptools versions against your own image, confirm the model you need is served by the engine you picked, and read the licence attached to the weights you plan to deploy.
Sources
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