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Predibase

Fine-tune and serve open-source LLMs with efficient LoRA infrastructure.

Acquired — still operating

acquired by Rubrik Jun 2025

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About

Predibase is a developer platform for fine-tuning and serving open-source large language models. It pairs efficient LoRA and Turbo LoRA supervised and reinforcement (GRPO) fine-tuning with serverless and dedicated inference powered by LoRAX, its open-source serving stack that packs hundreds of LoRA adapters onto a single GPU. Inference is exposed through an OpenAI-compatible API, and the platform offers a UI, SDK, and VPC deployment options for data control.

The pitch is small, specialized models that beat generic frontier APIs on your own tasks at a fraction of the cost, without managing GPU infrastructure. In June 2025 Predibase was acquired by Rubrik, which is steering it toward secure agentic AI adoption inside large enterprises.

For teams running dozens of task-specific fine-tunes, the multi-adapter serving economics are genuinely compelling. Caveats: inference bills per second of GPU usage, which can surprise on bursty traffic; fine-tuning is token-metered by model size and technique; and the Rubrik acquisition means the roadmap now tilts enterprise, so solo developers should confirm the self-serve experience still fits.

What it can do

  • LoRA and Turbo LoRA fine-tuning with automatic optimization
  • LoRAX: serve hundreds of adapters on a single GPU
  • OpenAI-compatible inference API with serverless and dedicated options
  • Reinforcement-style tuning from feedback (GRPO)
  • VPC deployment and model-variant evaluation tooling

Setup

Sign up at predibase.com, install the SDK, and fine-tune from the UI or Python.

Prerequisites

A Predibase account; your training data in a supported format.

Use cases

fine-tuningloramodel-servingopen-source-llms

Works with

Trust score — 7/10

/10 trustMetrics-checked

Scored 7/10 (metrics tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 74; Trending score 74/100 from researcher assessment.

Sources

Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.