Lamini

Fine-tune open-source LLMs and serve them in production.

Predibase is an enterprise platform for fine-tuning open-source models and serving them in production. It pairs a post-training stack — supervised fine-tuning plus an end-to-end reinforcement fine-tuning (RFT) flow — with an optimized inference engine, and its open-source LoRAX framework serves many fine-tuned LoRA adapters from a single GPU. Runs as managed SaaS or inside your own VPC.

Replace frontier-model spend with a fine-tuned small model.

Captures your production OpenAI / Anthropic calls, builds a dataset, fine-tunes a small open-weights model on your traffic, then serves the swap behind your existing SDK. The pitch: 10x cost reduction at parity.

Fine-tuning + inference for open-weights models. Broad coverage.

Hosted inference and fine-tuning across hundreds of open-weights models (Llama, Mistral, DeepSeek, Qwen, etc.). Strong pricing for inference-at-scale; LoRA + full fine-tuning supported.

Managed fine-tuning API with low-level control over the training loop.

Tinker is Thinking Machines Lab's training API for fine-tuning open-weight LLMs. It exposes low-level primitives — forward_backward, optim_step, sample — so researchers keep full control of data and algorithms while the service handles distributed GPU scheduling and failure recovery. LoRA-based runs cover models from small Llamas up to large mixture-of-experts like Qwen-235B and Kimi K2, and trained weights can be downloaded.