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 2026-08-09

  "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 2026-08-09

  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."

No standalone usage figure exists for Together's fine-tuning service: no jobs run, no customers tuning, no models fine-tuned, no developer count. Together AI raised a $305M Series B and serves substantial inference traffic, and a vendor case study describes a customer moving to daily iteration with accuracy rising from 77% to 87%, but no hard per-service usage number is published and a single anecdote is not enough to band on. The absence is itself the finding: the vendor publishes nothing for a feature sold inside a larger platform.

• https://docs.together.ai/docs/fine-tuning-overview recorded 2026-08-13

  managed FT feature; no aggregate usage figures disclosed

• https://www.together.ai/fine-tuning recorded 2026-06-04

  FT product page; case-study traction (daily iteration, 77%->87% accuracy)

Broad managed method coverage over large open models: LoRA and full fine-tuning, DPO preference tuning, function-calling, reasoning and vision-language tuning, with demonstrated tuning of 100B+ models such as DeepSeek-V3 and Qwen3-235B, and an end-to-end path from data preparation through training to a dedicated serving endpoint. That is the widest open-model scale among the managed services here. It remains a black box with limited scale and precision control and no training benchmark behind it, so it is capped below the open-source libraries that define scale, and sits level with the OpenAI and Azure fine-tuning APIs.

• https://docs.together.ai/docs/fine-tuning-overview recorded 2026-08-09

  Lists the supported methods -- LoRA, full fine-tuning, "Preference fine-tuning Align a model with rankings over preferred and dispreferred responses using DPO," "Function calling Train a model to invoke tools and structured functions reliably," and "Reasoning fine-tuning Train a reasoning model with chain-of-thought data" -- with results served "on a dedicated endpoint."

• https://www.together.ai/fine-tuning recorded 2026-08-09

  "Fine-tune 100B+ models (DeepSeek-V3, Qwen3-235B) that break other platforms, with the reliability to experiment rapidly."

• https://docs.together.ai/docs/fine-tuning/supported-models recorded 2026-08-09

  Two separate tables, headed "LoRA fine-tuning" and "Full fine-tuning," list every fine-tunable base model; the LoRA table includes deepseek-ai/DeepSeek-V4-Flash and Qwen/Qwen3.5-397B-A17B, while the Full fine-tuning table tops out at meta-llama/Llama-3.3-70B-Instruct-Reference -- full-parameter tuning does not reach the largest MoE releases that LoRA does.