Together Fine-Tuning

source

closed

training-pipeline

closed

runs-on

Together-cloud-only

license

Proprietary(managed service, per-token training pricing)

base-weights

open-models(varies)

The fine-tuning service and its training pipeline are proprietary and run only on Together's cloud; there is no self-hostable implementation. Together's Terms of Service bar reverse-engineering the underlying source code and vest all IP in Together AI, granting users only a non-sublicensable right to use the service. (Together tunes open-weight base models, but the offering itself is closed.)

• https://docs.together.ai/docs/fine-tuning-overview recorded 2026-09-23

  "Together AI handles the full lifecycle: data upload, training, hosting, and inference on a dedicated endpoint." No self-hosted training pipeline or repository appears anywhere on the page; deploying a tuned model is described only as "Serve your fine-tuned model on a dedicated endpoint or download it for local use," which is about the resulting checkpoint, not the training engine.

• https://www.together.ai/terms-of-service recorded 2026-09-23

  The Terms of Service prohibit a user from any "attempt to create, or derive, or permit or assist any third party to create or derive, the source code underlying the Services," and state that the Company "hereby grants you a non-exclusive, non-sublicensable right and license to use the Company IP as permitted by this Agreement," while "Company reserves all of its intellectual property and other proprietary rights not expressly granted to you herein."

Together publishes no usage figures for its fine-tuning service, such as jobs run, customers tuning or models fine-tuned. A vendor case study describes one customer's faster iteration and improved accuracy, but a single anecdote says little about how widely the service is used, so the map leaves it unscored.

• https://docs.together.ai/docs/fine-tuning-overview recorded 2026-09-28

  Managed fine-tuning overview: "Together AI handles the full lifecycle: data upload, training, hosting, and inference" and "Serve your fine-tuned model on a dedicated endpoint or download it for local use." Searched the body for jobs run, customers, developers, users and usage counts: none found.

• https://www.together.ai/fine-tuning recorded 2026-09-28

  Product page carries one customer testimonial, "move from weekly to daily iteration, cut costs by 2–3×, and improve accuracy from 77% to 87%." Searched the body for customer counts, jobs run, models fine-tuned and developer counts: none found.

Together Fine-Tuning offers LoRA and full-parameter tuning with supervised, preference, function-calling, reasoning and vision-language methods on open-weight models, with full-parameter tuning up to 70B-class models and LoRA on much larger ones. It runs as a managed service rather than exposing a training framework, so it offers breadth of methods without the parallelism and precision controls of Megatron-LM.

• https://docs.together.ai/docs/fine-tuning-overview recorded 2026-09-28

  Managed fine-tuning overview: "Together AI handles the full lifecycle: data upload, training, hosting, and inference" and lists supervised, "Align a model with rankings over preferred and dispreferred responses using DPO.", vision-language, "Train a model to invoke tools and structured functions reliably." and "Train a reasoning model with chain-of-thought data." methods, plus "Choose how much of the model to update, and tune LoRA’s rank and target modules." Searched the body for jobs run, customers, developers and usage counts: none.

• https://docs.together.ai/docs/fine-tuning/supported-models recorded 2026-09-28

  Supported-models page has separate LoRA and full fine-tuning tables; the LoRA table lists "deepseek-ai/DeepSeek-V4-Flash" and "Qwen/Qwen3.5-397B-A17B", while the full fine-tuning table lists nothing larger than "meta-llama/Llama-3.3-70B-Instruct-Reference" and Mixtral-8x7B.

• https://www.together.ai/fine-tuning recorded 2026-09-28

  The product page states "Fine-tune 100B+ models (DeepSeek-V3, Qwen3-235B) that break other platforms, with the reliability to experiment rapidly." Reinforcement learning and quantization appear in an "Advanced model shaping capabilities" block headed "For teams pushing models beyond standard fine-tuning", which lists "Apply FP8 and NVFP4 quantization to push the limits of model efficiency" and "Leverage PyTorch-based reinforcement learning to shape model policies". Multi-GPU and extended-context training are named only in the page's structured-data description, not in its visible copy.