by Striped Giraffe Team
9. September 2025
Reading time: 12 Minutes
Key Drivers of Cloud Adoption in Pharma and Life Sciences
The adoption of cloud computing in the pharmaceutical industry means operating in one of the world’s most stringent and heavily regulated environments. However, companies that are mastering this complexity are already demonstrating that innovation and compliance can go hand in hand — provided that cloud computing is used strategically and not viewed as a panacea.
Executive Summary
- Over 80% of leading life sciences companies have already migrated critical workloads to the cloud.
- The life sciences cloud market is expected to reach USD 9 billion by 2030.
- Key drivers include exploding data volumes, on-demand high-performance computing, AI-enabled research, global collaboration, and stringent regulatory requirements.
- Cloud adoption is not only about cost and flexibility – it has become a strategic enabler for innovation, compliance, and new business models in pharma and life sciences.
- Hybrid approaches that combine cloud agility with the control of local systems are becoming increasingly popular.
Global studies show that more than 80% of top life sciences companies have already moved critical workloads into the cloud, ranging from research pipelines to enterprise resource planning systems. Analysts expect the life sciences cloud market to surpass USD 9 billion by 2030, driven by the exponential growth of data, the urgency of faster R&D, and the demand for real-time collaboration across a highly distributed ecosystem.
Unlike many other industries, pharma faces unique challenges:
- strict regulatory oversight
- intellectual property protection
- the need to handle sensitive patient data with the highest standards of confidentiality
Cloud adoption is therefore not just about cost or flexibility. It is about enabling new ways of working — from AI-driven drug discovery to federated data sharing across global consortia — while staying compliant with EMA, FDA, and GDPR requirements.
This article highlights the key drivers of cloud adoption in pharmaceuticals and life sciences—and shows why hybrid models are the most realistic option for many companies.
Exploding Data Volumes
Pharmaceutical research generates unprecedented volumes of data — from genomics and proteomics to real-world evidence and connected manufacturing equipment. Traditional on-premises infrastructures struggle not only with the scale but also with the heterogeneity of these sources.
The challenge is compounded by the rise of unstructured data such as lab notes, medical images, sensor feeds, and electronic health records. These datasets are essential for discovery and clinical insight, yet they cannot be efficiently managed with traditional relational databases or siloed storage.
Cloud advantage
Cloud-native data architectures provide the flexibility to ingest, organize, and analyze both structured and unstructured datasets at scale. Data lakes and data lakehouses enable pharma companies to centralize genomics files, imaging datasets, and real-world evidence into a single, queryable environment.
Combined with scalable compute, these models allow researchers to run advanced analytics and AI pipelines without overwhelming local infrastructure. The result is a more unified data foundation that supports faster discovery and better downstream decision-making.
Challenge
Data classification, governance, and security models must be carefully defined to meet regulatory requirements.
Example:
Regeneron Genetics Center built one of the world’s largest genomics analytics environments on AWS, processing millions of exomes and petabyte-scale datasets to power discovery of gene–disease associations at speed and scale that would be difficult on traditional on-prem systems.
High-Performance Computing for Research
Drug discovery increasingly depends on compute-heavy workloads such as protein folding, molecular modeling, and advanced simulations. Building and maintaining such infrastructure in-house is costly and inflexible.
Cloud advantage
Cloud high-performance computing (HPC) provides scalable clusters that researchers can access on demand, dramatically reducing the time and cost of computational experiments.
Boundary
For very stable, consistently high computing loads, on-premises HPC systems may still be more cost-effective.
Example:
AstraZeneca runs large-scale genomics pipelines and computational chemistry on AWS HPC, enabling rapid burst capacity for sequencing and bioinformatics workloads and materially shortening time to scientific results.
Advanced Analytics and AI
Artificial intelligence and machine learning are central to modern pharma, from predicting drug-target interactions to analyzing imaging data. These workloads require both scalable compute and advanced data governance.
Cloud advantage
Cloud providers offer managed ML platforms, automation pipelines, and AI-specific services that make these processes faster to deploy and validate.
Example:
Merck leveraged AWS SageMaker and HealthOmics for protein modeling and manufacturing analytics, reducing false positives in quality control and accelerating drug discovery pipelines.
Speed and Agility
Time is critical in the pharmaceutical industry. Whether it is launching a new drug, validating a manufacturing process, or running a clinical trial, delays translate into lost revenue and slower patient access to treatments.
Cloud advantage
Cloud infrastructure can be provisioned in hours instead of weeks, giving pharma organizations the agility to experiment, test, and deploy new solutions faster.
Boundary
Critical core systems often remain deliberately local in order to ensure regulatory security and business continuity.
Example:
Moderna architected its “digital-first biotech” on AWS, scaling R&D, manufacturing, and analytics workloads on demand — a foundation the company credits with accelerating development and global rollout of its mRNA platform.
Global Collaboration
Pharmaceutical research and development is by nature distributed. Large pharma companies work across multiple sites and time zones, often in partnership with contract research organizations, academic institutions, technology providers, and regulators. Ensuring that these diverse stakeholders can collaborate effectively is essential to shorten development timelines and improve research quality.
Cloud advantage
Cloud-based platforms enable this kind of cooperation by providing controlled environments where teams can access standardized data and tools in real time, regardless of geography or organizational boundaries.
Pragmatic approach
Hybrid models ensure that particularly sensitive data remains local and only results are shared.
Example:
Novartis partnered with Microsoft to establish an AI Innovation Lab on Azure. The initiative created a shared digital environment where internal and external teams could collaborate on analytics and machine learning use cases, streamlining data access and fostering innovation across global operations.
Secure Data Sharing
Data sharing has become a critical enabler of progress in today’s pharma. Whether in precompetitive collaborations, public–private research consortia, or multi-sponsor clinical trials, no single organization can generate the diversity and scale of data required to fuel modern science. Sharing data accelerates discovery, improves the robustness of clinical evidence, and allows companies and institutions to pool insights that would be impossible to achieve in isolation.
At the same time, sharing sensitive research data — clinical records, compound libraries, or patient information — requires careful handling to protect confidentiality, intellectual property, and compliance. The challenge lies in enabling knowledge exchange across organizations without exposing raw datasets. Federated learning is one approach that helps achieve this balance.
Cloud advantage
Federated learning itself is a machine learning paradigm, but in pharma it is typically deployed on cloud infrastructure that provides the secure orchestration, elastic compute power, and regulatory-grade compliance required for multi-party collaboration.
In federated learning, instead of pooling data into a single repository, each organization keeps information within its own environment. Algorithms are sent to the data, trained locally, and only the learned parameters are shared back to a central model hosted in the cloud.
By running training locally while using the cloud to coordinate and aggregate models, companies can unlock collective insights without ever moving raw data.
Challenge
Only cloud infrastructures that comply with regulatory requirements are suitable for this purpose.
Example:
The MELLODDY consortium, involving ten leading pharmaceutical companies, demonstrated this approach by training machine learning models across billions of confidential data points. Using a federated cloud-based setup, the partners improved predictive performance in drug discovery while ensuring that no proprietary datasets ever left the companies’ own environments.
Compliance and Auditability
Pharma operates under some of the world’s most demanding compliance regimes, including GxP, 21 CFR Part 11, and EMA guidance.
Cloud advantage
Cloud platforms support compliance with automated audit logs, traceable data flows, and infrastructure as code — reducing the manual burden of validation and increasing transparency.
Challenge
Companies remain responsible for ensuring that cloud environments are recognized and validated by regulators.
Example:
Roche Diagnostics adopted Signals Notebook, a cloud-based Electronic Laboratory Notebook (ELN), to support regulated research and diagnostics workflows. Unlike traditional on-prem systems, the cloud ELN comes with built-in GxP validation packages, automated audit trails, and compliance with 21 CFR Part 11 requirements for electronic records and signatures. This ensures that every experimental record is secure, traceable, and regulator ready. By moving this critical function into the cloud, Roche simplified global access for its scientists while maintaining the level of documentation rigor expected by the FDA and EMA.
Manufacturing and Quality Control
In pharmaceutical production, quality assurance is mission critical. Manual inspection processes are slow, resource-intensive, and prone to human error.
Cloud advantage
Cloud-enabled machine learning models can automate anomaly detection, improving both efficiency and product safety.
Pragmatic approach
Critical production systems remain local, while the cloud is used for analytics.
Example:
Novo Nordisk deployed ML pipelines on AWS to automate cartridge counting and anomaly detection, with results streamed into dashboards for operators, significantly reducing error rates in production.
ESG and Supply Chain Transparency
Investors, regulators, and patients increasingly expect visibility into environmental, social, and governance (ESG) practices. For pharma, this means tracking emissions, energy use, and supply chain performance.
Cloud advantage
Cloud platforms allow data from disparate systems to be consolidated, analyzed, and presented in real time, turning ESG reporting into a tool for both compliance and operational improvement.
Boundary
For stable, predictable workloads, on-premises may be more economical.
Example:
Envirotainer — a global leader in temperature-controlled pharma logistics — uses Microsoft Sustainability Manager to automate emissions data collection and improve transparency across more than 100 airlines and 600 pharma customers, supporting ESG reporting and greener operations.
Cost Efficiency and Flexibility
Maintaining large on-premises infrastructures ties up capital that could otherwise fund R&D.
Cloud advantage
Cloud’s pay-as-you-go model shifts investment to operational expense and allows organizations to scale spending in line with actual usage. This flexibility is especially valuable for companies with fluctuating compute demands, such as during peak trial phases.
Example:
Daiichi-Sankyo migrated its SAP ERP system to AWS, cutting operating costs by 50% while doubling system performance — freeing resources for strategic investments.
Conclusion
Cloud adoption in pharma and life sciences is not a uniform journey but a balancing act between innovation, compliance, and risk management. The industry has demonstrated that the cloud can handle the scale and sensitivity of genomic research, clinical trials, manufacturing, and ESG reporting — provided that deployments are carefully designed with security and regulatory frameworks in mind.
For CIOs and senior leaders, the lesson is clear: cloud is not simply about replacing on-prem infrastructure but about rethinking operating models. Success requires hybrid strategies that combine the scalability of cloud with the control of local systems, strong partnerships with trusted providers, and a clear roadmap for data governance.
Those who master this balance will not only reduce costs and accelerate R&D but also create new opportunities for collaboration, transparency, and patient trust.
You might also like:
- Artificial Intelligence in Pharma and Healthcare (Booklet) » Read more
- Pharma’s AI Readiness Index: Who Leads the Race? » Read more
- ESG – It is our responsibility (E-Book) » Read more
- Data sharing — Challenges and Opportunities » Read more
- Data governance – the linchpin of efficient data management » Read more
