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.
- #57.9
Ray-powered platform for training, serving, and scaling LLMs.
Paid· Enterprise / contact salesInfrastructure (any model)Anyscale is the right answer for ML platform teams that already think in Ray. For everyone else, it's overbuilt — the friction is meaningful and the alternatives are more accessible.Best forPick Anyscale for organisations with serious in-house ML platform needs across multi-node training and serving.
Skip ifSkip it for small teams — Modal or Together do most of what you need with less commitment.