Predibase Customer Stories
Learn how leading enterprises are building innovative production-grade AI applications with fast, efficient, and customized Small Language Models (SLMs) on Predibase and our open-source projects like LoRAX.
Trusted by Leading Enterprises
Featured Customer Stories
Saved over 1 million employee hours by building agent-based tools with fine-tuned SLMs
Delivered customer support automation by serving 60+ adapters with an 80% improvement in throughput
Streamlined background checks at a 5x lower cost and 30x faster inference than OpenAI
Improved model accuracy for a major media company by over 10% by fine-tuning on Predibase
Built specialized copilots for finance and regulatory use cases with fine-tuned agents
By fine-tuning and serving Llama-3-8b on Predibase, we've improved accuracy, achieved lightning-fast inference, and reduced costs by 5x compared to GPT-4. But most importantly, we’ve been able to build a better product for our customers, leading to more transparent and efficient hiring practices.
Vlad Bukhin, Staff ML Engineer, Checkr
Our AI workload can be extremely variable, with spikes that require scaling up to double-digit A100 GPUs to maintain performance. The Predibase Inference Engine and LoRAX allow us to efficiently serve 60 adapters while consistently achieving an average response time of under two seconds. Predibase provides the reliability we need for these high-volume workloads. The thought of building and maintaining this infrastructure on our own is daunting—thankfully, with Predibase, we don’t have to.
Giuseppe Romagnuolo, VP of AI at Convirza
With Predibase, I didn’t need separate infrastructure for every fine-tuned model, and training became incredibly cost-effective—tens of dollars, not hundreds of thousands. This combined unlocked a new wave of automation use cases that were previously uneconomical.
Paul Beswick, Global CIO at Marsh McLennan
By switching from OpenAI to Predibase, we’ve been able to fine-tune and serve many specialized open-source models in real-time, saving us over $1 million annually while creating engaging experiences for our audiences. Best of all we own the models.
Andres Restrepo, Founder and CEO, Enric.ai
You can achieve better performance using fewer compute resources by fine-tuning smaller open-source LLMs with task-specific data. We’ve used this approach to innovate customer service and believe that Predibase offers the most intuitive platform for efficiently fine-tuning LLMs for specialized AI applications.
Sami Ghoche, CTO & Co-Founder