Biotech leaders are facing a new wave of cloud challenges. The "all-in" public cloud strategies of the last decade are giving way to a more balanced biotech cloud approach that blends public, private, and on-premises infrastructure. Why? AI workloads are consuming more compute and bandwidth than ever, and biotech and life science organizations need to reorganize their data for efficient access.
In addition, public cloud costs are ballooning beyond what many biotech organizations expected, and security and compliance continue to be top concerns.
The result? A growing number of biotech companies are repatriating workloads from the public cloud back to hybrid and private environments. You need to control sensitive clinical and R&D data, ensure regulatory compliance, and manage costs while still enabling cutting-edge AI-driven research and development. Let’s dive into the details of why and how you should shift your biotech cloud strategy.
Why Biotech Cloud Strategies Are Evolving
Public clouds promised limitless scalability and operational efficiency. But for biotech organizations, the reality has been more complex. Cloud costs, especially for AI and data-intensive workloads, have surged far beyond initial estimates. While cloud workloads continue to grow, the repatriation of roughly 21% of applications and data back to on-prem data centers show how organization are reshaping their cloud strategies.
At the same time, the biotech cloud landscape has become fragmented. Many organizations now juggle AWS, Azure, Google Cloud, and Nutanix—not by choice, but as a reaction to vendor pricing shifts, like Broadcom’s VMware acquisition driving up licensing costs. It’s a balancing act that’s becoming harder to manage.
Adding to this complexity is the sheer volume of data biotech generates. Genomic data, imaging files, and large R&D datasets are expensive to store in the cloud, slow to access, and costly to move. This is where hybrid cloud and on-prem strategies come into play. By keeping high-sensitivity or high-volume data on-premises or in private cloud environments, biotech organizations can reduce latency, avoid bandwidth costs, and maintain direct control over their most valuable assets.
The global life sciences cloud market grew to $25.3 billion in 2024, and is projected to surpass $105 billion by 2034. But many biotech firms are realizing that the best cloud strategy isn’t "cloud-only", it’s "cloud-smart."
The AI Factor: How Biotech Cloud Strategies Must Support AI Workloads
AI is changing everything. For biotech organizations, AI workloads, especially those involving real-time data retrieval and analysis, are stretching cloud infrastructure to its limits. Training models may benefit from the scalability of public clouds, but inference tasks, where models are applied to clinical or R&D data, demand low latency, high security, and robust compliance controls.
Microsoft’s new Retrieval-Augmented Generation (RAG) capabilities on Azure Local are a game-changer in this space. These tools allow biotech companies to ground AI models in on-prem data without moving it to the cloud. This means you can:
Ensure HIPAA and global regulatory compliance
Reduce latency for real-time decision-making
Avoid duplicating data across environments
Minimize bandwidth and cloud storage costs
The intuitive no-code/low-code interface, responsible AI controls, and multilingual, multi-modal support make it easier than ever to deploy AI securely in regulated biotech environments. For organizations navigating the biotech cloud landscape, Azure Local with RAG represents a powerful way to balance innovation with compliance.
Balancing Biotech Cloud and On-Prem: Key Considerations
So how do you strike the right balance between cloud and on-prem infrastructure in a biotech environment?
First, understand that not every workload belongs in the cloud. AI training models might require cloud-scale compute resources, but inference tasks that interact with sensitive patient or R&D data are often better suited to on-premises or hybrid environments. This reduces latency and improves control.
Second, cloud costs can escalate quickly. Without strict governance and monitoring, biotech organizations often find themselves facing unplanned expenses. Database licensing, API overuse, and data egress fees add up fast. Managing cloud and infrastructure costs is essential.
Third, the multi-cloud reality is here. Many biotech organizations are managing resources across AWS, Azure, Google Cloud, and Nutanix to mitigate vendor lock-in and control costs. However, managing this complexity requires robust governance, integration strategies, and careful workload placement.
Finally, consider the "data gravity" effect. Large datasets don’t move easily or cheaply. Keeping high-volume data close to where it’s generated and used—often on-premises—makes both technical and financial sense.
Actionable Advice for Biotech Cloud Success
Here are practical steps biotech leaders can take to optimize their cloud strategies:
Create a Biotech Cloud Workload Matrix: Categorize workloads by sensitivity, size, latency needs, and compliance requirements. Use this to determine which workloads should live in public cloud, private cloud, or on-prem environments.
Adopt FinOps Practices: Regularly audit cloud consumption, licensing, and API usage. Identify inefficiencies and hidden costs. Align your biotech cloud investments with clear business outcomes.
Leverage Microsoft Azure Local RAG: For biotech organizations using Azure, implementing RAG on Azure Local allows you to keep sensitive data on-prem while still harnessing AI capabilities. This reduces costs, improves compliance, and delivers faster results.
Pilot Edge Deployments in R&D Labs: Start small by deploying edge compute environments in labs or clinical settings. Measure performance, latency, and cost savings before scaling up.
Manage Multi-Cloud with Discipline: Multi-cloud strategies can prevent vendor lock-in and optimize performance, but they require careful coordination. Invest in governance frameworks and integration tools to manage this complexity.
Partner with Biotech Cloud Experts: Infrastructure transitions aren’t one-size-fits-all. Biotech organizations need partners who understand regulatory environments, data pipelines, and AI readiness.
Pennant Can Help You Navigate the Biotech Cloud Landscape
At Pennant, we specialize in helping biotech organizations design and implement infrastructure and hybrid cloud strategies that balance innovation, compliance, and cost. Knowing what works and what doesn’t is crucial for creating an efficient biotech cloud strategy, and our Pennant team has a playbook of best practices for the different sizes and stages of biotech organizations. Whether you’re looking to repatriate workloads, optimize your multi-cloud environment, or deploy AI securely on-prem, our team of experts can guide you every step of the way.
We’ve helped biotech and life sciences companies:
Streamline cloud and licensing costs
Design hybrid cloud infrastructures optimized for AI workloads
Implement Microsoft Azure Local RAG for secure, compliant AI deployment
If you’re ready to take control of your biotech cloud strategy, contact our Pennant team today. We’ll help you build an infrastructure that accelerates your innovation while keeping your data secure and your costs under control.
This post originally appeared on %P. Quantum Sol LLC. is affiliated with Pennant Networks, LLC.