📖 The AI Tool Bible

OpenAI Fine-tuning

✓ Editorially verified

Fine-tune GPT-4o-mini and friends on your own data.

Paid· Training $25/1M tokens; inference at standard ratesFine-tuningGPT-4o-mini / GPT-3.58.4 / 10

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Best for

Pick OpenAI FT when you're already on OpenAI's API and want the simplest path to a custom model.

Skip if

Skip it if you need weights export, multi-cloud portability, or aggressive cost control.

OpenAI's fine-tuning offering is the easiest way to fine-tune a closed frontier model. Upload a JSONL of example conversations, kick off a training job, get a custom model that bakes your style, format, or domain knowledge into the weights. Vision fine-tuning is now supported, broadening the use cases beyond pure text.

The ergonomics are excellent. The fine-tuning UI walks you through dataset prep; the API supports automation; the resulting custom model is hosted on the same infrastructure as the base model with the same SLAs.

The trade-offs are well-known: pricier than open-model FT, no weights export (you're locked into the OpenAI hosting), and only certain base models are FT-eligible (GPT-4o-mini and GPT-3.5 are the common picks). For teams already on OpenAI's API, the integration is seamless.

Editor's take

OpenAI'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.

— The AI Tool Bible editorial team

Pros

✅ Easiest fine-tuning UX
✅ Vision FT now supported
✅ Works inside the OpenAI ecosystem
✅ Same infra/SLA as base models

Cons

⚠️ Pricier than open-model FT
⚠️ No weights export

Use cases

styleformatdomain knowledge

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Together AI

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Fine-tuning · Infrastructure (any model you can host)

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One-API platform for running and fine-tuning open-source models.

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Llama

Fine-tuning · Llama 4 (Maverick, Scout), Llama 3.3/3.2/3.1

Meta's open-weight LLM family covering 1B mobile models up to 405B frontier and natively multimodal 10M-context Llama 4 variants.

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RunPod

Fine-tuning · Bring-your-own (any open-weight or custom model)

On-demand GPU cloud and serverless inference platform built specifically for AI workloads.

Paid· Pay-per-second GPU rental; H100 from ~$1.89/hr, consumer GPUs from ~$0.20/hrllm-fine-tuninggpu-rental

vLLM

Fine-tuning · Multi-model (open-weight LLMs: Llama, Qwen, DeepSeek, Mistral, Gemma, Phi, etc.)

Open-source high-throughput inference engine for serving LLMs with PagedAttention and continuous batching.

Free· Free and open-source (Apache 2.0); self-hosted infrastructure costs applyllm-servingself-hosted-inference