Best AI fine-tuning platforms in 2026
Fine-tuning has gone from "deep ML team only" to "a few hours of JSONL away" — but the choice between closed-model FT (OpenAI), open-model FT (Together, Modal), and memory-tuning matters more than ever.
Last updated · ranked by our editorial 0–10 score, weighted by capability, cost-to-value, UX, and maturity. How we rate →
- #18.6Together AIFeatured
Fine-tune & serve open-weight models (Llama, Mistral, DeepSeek).
Paid· Pay-per-token; fine-tuning per-tokenLlama / Mistral / Qwen / DeepSeek and othersTogether is the cleanest commercial answer to "I want open-weight models with closed-weight ergonomics." The catalogue width and the FT + serve integration make it the default for serious open-model production.Best forPick Together when you want open-weight FT + serving in one platform with sensible per-token pricing.
Skip ifSkip it if you need the polish of OpenAI's developer experience or single-vendor support across closed + open.
- #28.7
Serverless GPUs and infra for training & serving ML.
Freemium· $30/mo free credits; pay-as-you-go GPU ratesInfrastructure (any model you can host)Modal is the platform that made serverless GPU access feel like a normal Python decorator. For ML teams that don't want a dedicated ops function, it's transformative.Best forPick Modal when you need serverless GPUs for ML workloads and you want to write Python rather than Kubernetes manifests.
Skip ifSkip it for latency-sensitive serving of large models without warm pools.
- #38.5
One-API platform for running and fine-tuning open-source models.
Paid· Pay-per-second of GPU timeThousands of community + first-party modelsReplicate is the platform that made open-model API access feel as easy as calling OpenAI. The community-model catalogue is unique, and the fine-tuning flow for popular base models is genuinely the simplest available.Best forPick Replicate to experiment across many models or to fine-tune popular open models with minimal setup.
Skip ifSkip it for high-volume production inference where dedicated deployments or self-hosting are cheaper.
- #48.4
Fine-tune GPT-4o-mini and friends on your own data.
Paid· Training $25/1M tokens; inference at standard ratesGPT-4o-mini / GPT-3.5OpenAI's fine-tuning is the path of least resistance for teams already on the platform. The vision FT addition broadened the use cases; the lock-in remains the same trade-off it's always been.Best forPick OpenAI FT when you're already on OpenAI's API and want the simplest path to a custom model.
Skip ifSkip it if you need weights export, multi-cloud portability, or aggressive cost control.
- #58.3
Meta's open-weight LLM family covering 1B mobile models up to 405B frontier and natively multimodal 10M-context Llama 4 variants.
Freemium· Weights free under Llama Community License; partner API inference ~$0.19-$0.49 per 1M tokensLlama 4 (Maverick, Scout), Llama 3.3/3.2/3.1Llama is the gravitational centre of the open-weight ecosystem - every serious local-LLM tool, every fine-tuning recipe, every cheap inference provider points back here. Llama 4's 10M context and multimodality finally close most of the gap with closed frontier models, and if you're building anything where data sovereignty or per-token cost matters, this is the default.Best forPick Llama if you need open weights you can fine-tune, quantise, and self-host with a license that survives commercial deployment at scale.
Skip ifSkip it if you want a turnkey hosted chatbot with first-class tool use and you don't care about owning the weights.