Predibase
Predibase is a developer platform that enables efficient fine-tuning and serving of open-source large language models (LLMs) with state-of-the-art infrastructure in the cloud or private deployments.
https://www.predibase.com/?ref=aipure
Product Information
Updated:Apr 16, 2025
Predibase Monthly Traffic Trends
Predibase achieved 78,572 visits with a 180.7% growth in traffic, driven by its Reinforcement Fine-Tuning (RFT) platform and Predibase Inference Engine. These innovations, coupled with the company's focus on efficient model adaptation and reinforcement learning, have likely attracted significant attention from developers and enterprises.
What is Predibase
Predibase is a low-code/no-code end-to-end platform built for developers to customize and deploy open-source language models. Founded by the team behind popular open-source projects Ludwig and Horovod from Apple and Uber, Predibase makes it easy for engineering teams to cost-efficiently fine-tune and serve small open-source LLMs on state-of-the-art infrastructure, without sacrificing quality. The platform is currently being used by both Fortune 500 companies and high-growth startups like Nubank, Forethought, and Checkr.
Key Features of Predibase
Predibase is a developer platform that enables efficient fine-tuning and serving of open-source Large Language Models (LLMs). It offers state-of-the-art fine-tuning techniques including quantization, low-rank adaptation, and memory-efficient distributed training. The platform features reinforcement fine-tuning capabilities, multi-LoRA serving through LoRAX, and can be deployed either in Predibase's cloud or private infrastructure. It provides enterprise-grade reliability with features like multi-region high availability, logging, and SOC 2 Type II certification.
Turbo LoRA Multi-Model Serving: Serves hundreds of fine-tuned models on a single GPU with 4x faster throughput than traditional solutions, using LoRAX-powered technology
Reinforcement Fine-Tuning (RFT): Enables model training with minimal data requirements using reward functions for continuous learning and improvement
Flexible Deployment Options: Offers both cloud-hosted and private infrastructure deployment with autoscaling capabilities and dedicated GPU resources
Enterprise-Grade Security: Provides SOC 2 Type II certification, multi-region high availability, and comprehensive logging and metrics
Use Cases of Predibase
Background Check Automation: Checkr uses Predibase to streamline background checks with fine-tuned models, achieving 5x cost reduction compared to GPT-4
Customer Service Enhancement: Convirza leverages Predibase to efficiently serve 60 adapters for handling variable customer service workloads with fast response times
Enterprise Process Automation: Marsh McLennan utilizes Predibase for cost-effective automation of various enterprise processes, saving over 1 million hours of team time
Conservation Data Analysis: WWF employs customized LLMs through Predibase to generate insights from their large corpus of conservation project reports
Pros
Cost-effective with 5x reduction in costs compared to GPT-4
High performance with 4x faster throughput using Turbo LoRA
Flexible deployment options in cloud or private infrastructure
Enterprise-grade security and reliability features
Cons
Requires technical expertise to fully utilize all features
Limited to open-source LLMs only
Private serverless deployments have 12-hour inactivity timeout
How to Use Predibase
Sign up for Predibase: Go to predibase.com and sign up for a free trial account or contact sales for enterprise options. You'll need to generate an API token from Settings > My Profile once logged in.
Install Predibase SDK: Install the Predibase Python SDK using: pip install predibase
Connect to Predibase: Initialize the Predibase client using your API token: from predibase import Predibase; pb = Predibase(api_token='YOUR_TOKEN')
Prepare your training data: Upload or connect your training dataset through the UI (Data > Connect Data) or programmatically. Predibase supports various data sources including file upload, Snowflake, Databricks, and Amazon S3. Aim for 500-1000+ diverse examples.
Configure fine-tuning: Create a fine-tuning configuration specifying the base model (e.g. Mistral, Llama), dataset, and prompt template. Advanced users can adjust parameters like learning rate and temperature.
Launch fine-tuning: Start the fine-tuning job through the UI or SDK. Monitor training progress and evaluation metrics. The best performing checkpoint will be automatically selected.
Deploy model: Deploy your fine-tuned model using: pb.deployments.create(name='my-model', config=DeploymentConfig(base_model='model-name'))
Query the model: Generate predictions using: client = pb.deployments.client('my-model'); response = client.generate('Your prompt here')
Monitor and scale: Monitor model performance, costs and scaling through the UI dashboard. Predibase automatically handles GPU scaling and load balancing.
Predibase FAQs
Predibase is a developer platform for fine-tuning and serving open-source Large Language Models (LLMs). It allows users to customize and serve open-source models that can outperform GPT-4, all within their cloud or Predibase's infrastructure.
Predibase Video
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Analytics of Predibase Website
Predibase Traffic & Rankings
107.6K
Monthly Visits
#385455
Global Rank
#11086
Category Rank
Traffic Trends: Jan 2025-Mar 2025
Predibase User Insights
00:01:19
Avg. Visit Duration
2.51
Pages Per Visit
48.96%
User Bounce Rate
Top Regions of Predibase
US: 24.56%
CA: 8.43%
IN: 7.29%
SG: 6.69%
VN: 6.39%
Others: 46.64%
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