Big Model Performance
Small Model Cost

Save 50% or more on your OpenAI bill. Guaranteed.

FROM THE CREATORS OFLudwig & lorax

Built by AI leaders from Uber, Google, Apple and Amazon. Developed and deployed with the world’s leading organizations.

GPT-4 quality for less than GPT-3.5 price

Cost should never hold you back. Future-proof of your AI spend today by fine-tuning small, task-specific models that rival GPT-4 for a fraction of the cost.

Fine-tune and Serve 100s of LLMs on Our Cloud or Yours

The biggest selection of models at industry-leading pricing

CodeLlama 13B Instruct

Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale...

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Phi 3 4k instruct

The Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained...

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Llama 3 8B

Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection...

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Mixtral 8x7B v01

The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts...

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See full list of supported models

Bigger Isn’t Always Better

Fine-tune smaller task-specific LLMs that outperform bloated alternatives from commercial vendors. Don’t pay for what you don’t need.

Efficient Fine-Tuning and Serving

Train and deploy task-specific open-source models in record time and under budget.

First-class fine-tuning experience

Predibase offers state-of-the-art fine-tuning techniques out of the box such as quantization, low-rank adaptation, and memory-efficient distributed training to ensure your fine-tuning jobs are fast and efficient—even on commodity GPUs.

The most cost-effective serving infra

Our unique serving infra–LoRAX–lets you cost-effectively serve many fine-tuned adapters on a single private serverless GPU deployment. We provide free shared serverless inference up to 1M tokens per day / 10M tokens per month for prototyping, evaluation, and experimental use cases.

Fine-tuning, built for enterprise

Start owning and stop renting your LLMs. Predibase is built with enterprise-grade security and is SOC-2 compliant. Enterprise and VPC customers can download and export their trained models at any time, ensuring you always retain control of your IP.

The fastest way to fine-tune and deploy any open-source LLM

Fine-tune and serve any open-source LLM. Our proven, scalable infrastructure is available through either serverless fine-tuned endpoints or within your environment’s virtual private cloud.

Try Any Open Source LLM in an Instant

Stop spending hours wrestling with complex model deployments before you’ve even started fine-tuning. Deploy and query the latest open-source pre-trained LLM—like Llama-2, Mistral and Zephyr—so you can test and evaluate the best base model for your use case. Scalable managed infrastructure in your VPC or Predibase cloud enables you to achieve this in minutes with just a few lines of code.

# Deploy an LLM from HuggingFace
pb.deployments.create(
    name="my-llama-3-70b-deployment",
    description="Deployment of Llama 3 70b in Predibase Cloud",
    config=DeploymentConfig(
        base_model="llama-3-70b",
    )
)

# Prompt the deployed LLM
client = pb.deployments.client("my-llama-3-70b-deployment")
client.generate("Write an algorithm in Java to reverse the words in a string.")

Efficiently Fine-tune Models for Your Task

No more out-of-memory errors or costly training jobs. Fine-tune any open-source LLM on the most readily available GPUs using Predibase’s optimized training system. We automatically apply optimizations such as quantization, low-rank adaptation, and memory-efficient distributed training combined with right-sized compute to ensure your jobs are successfully trained as efficiently as possible.

# Kick off the fine-tune job
adapter = pb.adapters.create(
    config=FinetuningConfig(
        base_model: "llama-3-70b",
        epochs: 3,
        learning_rate: 0.0002,
    ),
    dataset=my_dataset,
    repo="my_adapter",
    description='Fine-tune llama-3-70b with my dataset for my task.',
)

Dynamically Serve Many Fine-tuned LLMs In One Deployment

Our scalable serving infra automatically scales up and down to meet the demands of your production environment. Dynamically serve many fine-tuned LLMs together for over 100x cost reduction with our novel LoRA Exchange (LoRAX) architecture. Load and query an adapter in milliseconds.

Built on Proven Open-Source Technology

LoRAX

LoRAX (LoRA eXchange) enables users to serve thousands of fine-tuned LLMs on a single GPU, dramatically reducing the cost of serving without compromising on throughput or latency.

Ludwig

Ludwig is a declarative framework to develop, train, fine-tune, and deploy state-of-the-art deep learning and large language models. Ludwig puts AI in the hands of all engineers without requiring low-level code.