The biotech research and development field is poised for a major shift with the launch of Benchling Inference, a new collaboration between Benchling and Baseten. This platform delivers on-demand GPU capacity specifically designed for scientific model workloads, enabling biotech teams to train and deploy AI models without the complexities of managing intricate infrastructure.
A notable trend in the scientific community is the rapid rise in AI models. Between 2020 and 2025, the annual release of new scientific AI models surged from 28 to over 380. This sharp increase underscores a critical demand for computational resources that can support these models in research workflows. Unfortunately, the infrastructure needed to meet these demands has not kept up. Drug discovery often requires teams to process vast amounts of data quickly—up to 100,000 predictions within hours. As a result, many computational teams are grappling with significant backlogs in high-performance computing (HPC) queues and underutilized GPU reservations.
Benchling Inference aims to address these issues. Built on the Baseten Inference Stack, the platform combines a high-performance runtime that includes custom kernels and optimization techniques with a network of infrastructure spanning more than 15 cloud providers. It is designed for rapid deployment, achieving cold starts in as little as 5 to 10 seconds. This efficiency is further enhanced by a biotech-centric layer that provides pre-configured settings for scientific models and deployment options that comply with strict data residency regulations. By consolidating demand across the biotech sector, Benchling is also improving economic efficiencies for startups in the industry.
The platform offers flexibility, allowing scientists to deploy both third-party models and internal models created from proprietary experimental data within a single computing environment. For organizations with stringent data sovereignty requirements, the Baseten Inference Stack can function in a private cloud or a hybrid setup, making sure sensitive predictions remain secure. Users can access inference directly through Benchling while working in Jupyter notebooks or using software development kits (SDKs), simplifying their workflows.
Amir Highighat, CTO and Co-Founder of Baseten, highlighted the need to eliminate infrastructure bottlenecks: "Biotech has entered a new era where AI models trained on proprietary experimental data could enable breakthroughs that weren't possible before. The bottleneck has been infrastructure, and biotech research labs should not have to become GPU experts to run frontier models on their data." Highighat's comments emphasize the platform's potential to democratize access to advanced AI capabilities within the biotech sector.
Ashu Singhal, co-founder and President of Benchling, supported this view, stating, "Access to compute is becoming a strategic advantage. But we hear from computational scientists that getting inference to work in drug discovery is harder than it should be; workloads are bursty, the data is sensitive, compute costs are too high. We've been running Baseten internally for Benchling's Model Hub and learned a lot about tailoring inference for drug discovery. Now we want customers to have the same access."
The significance of Benchling Inference goes beyond mere convenience. As biotech companies increasingly turn to AI for drug discovery and development, the ability to quickly and effectively deploy AI models could greatly speed up innovation. Companies interested in utilizing Benchling Inference can sign up directly through their platform.
Benchling, a key player in the biotech R&D space, is already trusted by over 1,300 organizations, including startups and established companies like Merck, Moderna, and Sanofi. The firm focuses on unifying scientific data and automating workflows to expedite discovery and development processes in the life sciences sector. Baseten, founded in 2019 and based in San Francisco, has raised $585 million to date and is known for its broad systems software that simplifies AI application workloads.
As AI continues to play a larger role in biotech research, partnerships like that of Benchling and Baseten will be essential in shaping the future of drug discovery, making advanced computational resources more accessible to a broader range of researchers. This collaboration not only represents a significant advancement in biotech infrastructure but also sets a precedent for future innovations in the AI-driven pharmaceutical development landscape.