Big Model Performance Small Model Cost
Predibase offers custom pricing plan
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Predibase helps teams put open-source AI into production. It allows engineering teams to fine-tune and serve small open-source large language models (LLMs) on advanced cloud infrastructure in a cost-effective way, without losing quality. Predibase provides cutting-edge fine-tuning techniques, such as ... Read More
Monthly plans
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Developer
Custom
Up to 1 user
Unlimited best-in-class fine-tuning with A100 GPUs
Inference: 1 private serverless deployment (no rate limits), Autoscaling and scale to 0, Serve unlimited adapters on a single GPU with LoRAX, Free shared serverless inference (with rate limits) for testing
Access to all available base models
Data connection via file uploads
2 concurrent training jobs
In-app chat, email, and Discord support
Enterprise
Custom
Everything in Developer, plus
Inference: Guaranteed instances to ensure scaling to meet increased demand, Additional replicas for burst usage, Additional private serverless deployments
Guaranteed uptime SLAs
Data connection via Snowflake, Databricks, S3, BigQuery, and more
Additional concurrent training jobs
Dedicated Slack channel, plus consulting hours with our experts
Enterprise
Custom
Everything in Enterprise SaaS, plus:
Deploy directly into your own cloud (AWS, Azure, GCP)
Use your own cloud commitments
Optimize usage with your own GPUs
Enterprise security and compliance
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Disclaimer: Pricing information for Predibase is provided by the software vendor or sourced from publicly accessible materials. Final cost negotiations and purchasing must be handled directly with the seller. For the latest information on pricing, visit website. Pricing information was last updated on .
Customer Service
Online
Location
San Francisco, CA
Predibase helps teams put open-source AI into production. It allows engineering teams to fine-tune and serve small open-source large language models (LLMs) on advanced cloud infrastructure in a cost-effective way, without losing quality. Predibase provides cutting-edge fine-tuning techniques, such as quantization, low-rank adaptation, and memory-efficient distributed training.
Disclaimer: This research has been collated from a variety of authoritative sources. We welcome your feedback at [email protected].
Researched by Rajat Gupta