Predibase Series A Funding Round, Raising $12.2M led by Felicis


Predibase, a low-code declarative ML platform for developers, has announced the general availability of its platform along with new features for large language models and free trial editions. The company has also revealed an expansion of its Series A funding round, raising an additional $12.2 million led by Felicis. This brings the total funding for Predibase to $28.5 million.

Predibase aims to make proprietary declarative ML approaches, utilized by companies like Uber, Apple, and Meta, accessible to a wider audience. The platform has been deployed by Fortune 500 organizations and high-growth startups, including Paradigm and It enables developers and data scientists to easily build, iterate, and deploy advanced AI applications without the need for extensive knowledge of complex ML tools or low-level frameworks.

By leveraging Predibase’s cutting-edge large AI models, teams can define their prediction requirements, and the platform takes care of the rest. Novice users can benefit from recommended model architectures, while expert users can fine-tune various model parameters. As a result, Predibase significantly reduces the deployment time for ML-powered applications from months to days. Since its emergence from stealth mode, over 250 models have been trained on the platform.

Piero Molino, co-founder and CEO of Predibase, emphasized the company’s mission to simplify the process of building ML applications for both beginners and experts. He stated, “Our mission is to make it dead simple for novices and experts alike to build ML applications and get them into production with just a few lines of code. And now we’re extending those capabilities to support building and deploying custom LLMs.”

The general availability version of Predibase introduces new features, including privately hosted and customized large language models (LLMs). This allows organizations to deploy their own LLMs securely within their infrastructure, optimized for their specific ML tasks. Predibase also provides optimizations to accelerate LLM tuning while reducing costs by 100x. Additionally, the platform offers a Data Science Copilot, which provides developers with expert recommendations, real-time explanations, and examples to enhance model performance during the iteration process.

Dr. Volkmar Scharf-Katz, Data Science Leader at a leading U.S. financial institution, commended Predibase’s declarative ML platform for its simplicity and flexibility, stating that it delivers accurate results quickly and reduces time-to-value from months to days.

To showcase the platform’s capabilities, Predibase is launching a free two-week trial version, allowing engineering and data science teams to experience the benefits of the declarative approach to ML development. The trial version is available as a fully hosted SaaS solution in the Predibase Cloud or via VPC in the customer’s environment. LudwigGPT, a custom LLM built using Predibase, is also accessible during the trial to demonstrate the ease of building a custom large language model.

The additional funding secured in the expansion of Predibase’s Series A round, led by Felicis, brings the total round funding to $25.2 million. The company plans to utilize the funds to enhance its go-to-market efforts and add new capabilities to the platform.

Niki Pezeshki, General Partner at Felicis, expressed confidence in Predibase’s ability to simplify the deployment of machine learning, stating, “After seeing how much traction Predibase has gained in the year since launch and how their platform has been transformative for their customers’ ML projects, we believe they’ve cracked the code.”

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