[Revised January 14, 2026]

Google Cloud in Pharma: Transforming Drug Discovery, Trials, and Operations

Cloud computing is rapidly reshaping the pharmaceutical industry. Traditionally cautious about new IT paradigms, pharma companies are now accelerating cloud adoption to drive innovation and efficiency. The pharmaceutical cloud computing market has experienced substantial growth, reaching an estimated $18–21 billion in 2024 and projected to exceed $55 billion by 2033, growing at a compound annual growth rate of 13–15% (^[1]). The global pharmaceutical industry's investment in AI specifically is estimated to reach $2.51 billion in 2026 and grow to $16.49 billion by 2034 (^[2]). Over 92% of large enterprises now operate in multi-cloud environments, and pharma is no exception. This represents dramatic acceleration from the ~$4.8 billion market size in 2022. Historical concerns around regulatory compliance, data security, and validation costs (often 25–40% of software implementation budgets) made pharma a "late majority" adopter (^[3]). But the tide has definitively turned – especially in the U.S. – as pharma giants partner with cloud providers to modernize R&D, streamline clinical trials, and improve manufacturing and analytics. This article explores how Google Cloud Platform (GCP), in particular, is being leveraged by U.S.-based pharmaceutical leaders for data-driven drug discovery, smarter trials, and compliant operations. We'll dive into which pharma companies use GCP (and for what), highlight key Google Cloud services (BigQuery, Vertex AI, Gemini, Apigee, etc.) powering these use cases, and compare GCP's role vs. AWS and Azure in this industry.

2024–2025 Updates: Major Developments in Google Cloud and Pharma

Since this article was originally published, several major developments have further cemented Google Cloud's position in the pharmaceutical industry:

AlphaFold 3 and the Nobel Prize

In May 2024, Google DeepMind and Isomorphic Labs launched AlphaFold 3 (AF3), a revolutionary AI system capable of predicting the 3D structures and interactions of proteins, nucleic acids, small molecules, ions, and other biomolecules with remarkable precision. Unlike previous versions, AlphaFold 3 can model "co-folding" – predicting how proteins, DNA, RNA, and drug ligands interact simultaneously. The system shows at least 50% better accuracy than existing methods for protein-molecule interactions, with accuracy doubling for specific cases like protein-ligand binding (^[4]).

In October 2024, Demis Hassabis and John Jumper were co-awarded the Nobel Prize in Chemistry for their work on AlphaFold, alongside David Baker for computational protein design. As of November 2025, AlphaFold is being used by over 3 million researchers from over 190 countries, tackling problems such as antimicrobial resistance, crop resilience, and heart disease.

Isomorphic Labs: First AI-Designed Drugs Enter Clinical Trials

Isomorphic Labs, Alphabet's AI drug discovery spinoff from Google DeepMind, is set to begin its first human clinical trials for AI-designed cancer drugs in 2025. Speaking at the World Economic Forum in Davos, Demis Hassabis announced the company aims to "drastically shorten the drug discovery timeline, which traditionally spans a decade or more, to just weeks or months." In April 2025, Isomorphic Labs raised $600 million in its first external funding round led by Thrive Capital, following multi-billion dollar partnerships with Eli Lilly and Novartis announced in 2024 (^[5]).

Recursion Pharmaceuticals Partnership Expansion

In October 2024, Recursion and Google Cloud announced an expanded collaboration leveraging Google Cloud's technologies to support Recursion's drug discovery platform. This strategic partnership includes exploring Gemini generative AI capabilities to support RecursionOS, driving improved search and access with BigQuery, and helping scale compute resources. Recursion will also explore making some of its AI models available on Google Cloud. The two companies have collaborated for over six years, with this application of AI already accelerating Recursion's drug discovery – increasing the speed and lowering the cost of IND studies (^[6]).

Med-Gemini and MedGemma: Healthcare-Specific AI

Google introduced Med-Gemini, a family of multimodal medical models fine-tuned for the medical domain. Med-Gemini achieves 91.1% accuracy on the MedQA (USMLE-style) benchmark, surpassing previous best results by 4.6%. At Google I/O 2025, Google launched MedGemma, an open-source AI model specifically designed for healthcare, available in 4B multimodal, 27B text-only, and 27B multimodal versions. MedGemma supports medical imaging applications including 3D CT and MRI interpretation, whole-slide histopathology imaging, and anatomical localization (^[7]).

BrightInsight Generative AI Expansion

In May 2024, BrightInsight expanded its partnership with Google Cloud to deploy Gemini and MedLM generative AI models on the Vertex AI platform. BrightInsight has embedded a Gemini-powered chatbot within its Disease Management Solution to provide patients improved access to approved content and first-line support for app usage questions. The company is also leveraging MedLM to summarize, interact with, and probe patient data from companion apps, making patient data more actionable for clinicians (^[8]).

Google Cloud Next '25 and Vertex AI Search for Healthcare

At Google Cloud Next '25 (April 2025), Google positioned Gemini 2.5 Pro for technical tasks like healthcare document analysis and launched the Agent Development Kit (ADK) for creating AI agents. In March 2025, Google Cloud announced new generative AI capabilities in Vertex AI Search for healthcare, including Visual Q&A that can search tables, charts, and diagrams in medical documents, with Gemini 2.0 available as one of the models. According to a Google Cloud survey, 44% of healthcare and life sciences executives said their organizations were actively using AI agents, with 34% reporting they use 10 or more agents (^[9]).

AWS and Google Cloud Multicloud Partnership

In December 2025, AWS and Google Cloud launched a joint multicloud collaboration, allowing customers an easier way to connect the cloud platforms through AWS Interconnect and Google Cloud's Cross-Cloud Interconnect. This partnership helps businesses operate more open cloud environments and lets users shift from manually establishing cloud connections to deploying a cloud-native experience. This is particularly significant for pharma companies running multi-cloud strategies (^[10]).

Accenture Intient: Continued Growth

Accenture has continued to expand its INTIENT platform on Google Cloud, completing over 1,000 generative AI use cases by mid-2024 with leading biopharma companies as clients move from experimentation to scaled solutions. The platform manages data and workflows "from molecule to market" for pharma, combining Accenture's industry expertise with Google Cloud's generative AI solutions (^[11]).

---

Big Pharma's Shift to Google Cloud

Large U.S. pharmaceutical companies are increasingly teaming with Google Cloud as part of their digital transformation. Accenture's "Intient" platform – an open architecture life sciences suite now exclusively on GCP – counts top-25 pharmas like GlaxoSmithKline (GSK), Pfizer, and Bayer among its clients (^[12]). The goal of Intient on Google Cloud is to modernize drug discovery, development, and delivery by combining Accenture's industry expertise with Google's data analytics and AI capabilities (^[13]). Early results show drastic productivity gains, with certain research tasks reduced "from weeks to minutes" through cloud-based data analytics and AI (^[14]). This collaboration underscores how pharma giants are turning to GCP for end-to-end data integration across research, clinical development, and commercialization.

Sanofi has also been expanding its AI partnerships. While maintaining its Google Cloud infrastructure established in 2019, Sanofi announced in May 2024 a new collaboration with Formation Bio and OpenAI to build AI-powered software to accelerate drug development. Sanofi described this as "the next significant step in our journey to becoming a pharmaceutical company substantially powered by AI" (^[15]). The company continues to leverage Google Cloud for demand forecasting and supply chain optimization.

Similarly, Moderna – the Boston-based mRNA pioneer – embraced a multi-cloud data strategy leveraging Google Cloud for critical analytics. Moderna's Vice President of Data Science notes that they choose "best-of-breed" tools for each job, and GCP was selected to integrate data and enable self-service analytics across the company (^[16]) (^[17]). Notably, Moderna used Google Cloud and Looker to unify internal and external data for a holistic view of its clinical trials, improving scientists' access to insights in real-time (^[18]) (^[19]). By centralizing trial data on Google Cloud, Moderna reduced manual data wrangling, increased collaboration, and ensured high-quality trial monitoring (^[20]) (^[21]). These cases illustrate a common theme: large pharmas are leveraging GCP's cloud data platforms to break down silos and make faster decisions – whether in R&D, clinical operations, or business processes.

New partnerships continue to emerge. Ginkgo Bioworks is partnering with Google Cloud to leverage large language models (LLMs) for biotech applications, using the Vertex AI platform and Ginkgo's biological data (over 2 billion unique protein sequences and vast functional assay data) to build domain-specific AI models. Superluminal Medicines is collaborating with Google on AI-powered dynamic protein modeling for drug discovery.

Data Analytics and Integration with BigQuery & Looker

One of Google Cloud's strongest appeals in pharma is its powerful data analytics ecosystem, including the BigQuery data warehouse and tools like Looker for business intelligence. Pharma companies deal with massive datasets: clinical trial results, real-world patient outcomes, genomics, supply chain logs, etc. GCP's serverless analytics (BigQuery, Dataflow, etc.) can handle these volumes with ease and compliance. The Moderna case is a prime example – they integrated diverse trial data sources into BigQuery and used Looker for interactive analysis, enabling researchers to get a "more complete view" of trial progress and patient subpopulations in seconds rather than days (^[18]). By connecting previously siloed data (e.g. different trial sites, lab results, CRO data) on a central platform, Moderna improved data consistency and eliminated conflicting Excel reports that had plagued their analysts (^[17]) (^[22]). Scientists now spend more time on science instead of manual data cleanup, and trial monitors can make real-time decisions to ensure quality and safety in ongoing studies (^[23]). This showcases how BigQuery's scalability and Looker's user-friendly analytics can dramatically enhance clinical data management for pharma.

Beyond trials, pharma IT teams are also using BigQuery for commercial and real-world data analysis. For instance, companies can aggregate prescription data, insurance claims, and electronic health records to derive insights on treatment effectiveness or market performance – all while ensuring patient data is de-identified and secure. Google's Cloud Healthcare API (with support for HL7 FHIR and HIPAA compliance) can ingest clinical data into BigQuery, making it easier to integrate healthcare datasets for pharma research. Vertex AI, Google's managed machine learning platform, often works hand-in-hand with BigQuery by training models on these large datasets (e.g. predicting patient outcomes or optimizing trial design). In one case, BioCorteX – a techbio startup – built a knowledge graph of drug-bacteria interactions on BigQuery to predict "Will this drug work?" in specific microbiome contexts (^[24]) (^[25]). They rely on GCP's scalable storage and query engine to update this graph daily with new findings, accelerating insight generation for clinical decision support (^[25]). While BioCorteX is a smaller player, the same BigQuery-driven approach is being adopted by larger pharmas to power real-world evidence (RWE) platforms and more data-driven medicine development.

Accelerating Drug Discovery with AI and HPC

Perhaps the most exciting area for GCP in pharma is AI-powered drug discovery. Google Cloud's prowess in machine learning (with Vertex AI, Gemini, and TPU hardware accelerators) is helping pharma researchers drastically speed up the discovery of new therapeutics. A flagship example is Bayer's collaboration with Google Cloud to run quantum chemistry simulations on TPUs. Announced in January 2023, this partnership applies Google's Tensor Processing Units – custom ML chips in GCP – to scale up Bayer's quantum chemistry calculations for early drug discovery (^[26]). By running advanced simulations of molecular interactions (like density functional theory calculations of protein-ligand binding), Bayer aims to achieve fully in silico modeling of candidate drugs with high accuracy (^[27]). In essence, GCP's high-performance computing allows Bayer's scientists to explore chemical space much faster than traditional methods. As Bayer's Chief Information & Digital Officer noted, TPU-powered quantum chemistry could be a "disruptive technology" that helps identify novel drug candidates quicker (^[28]). Google Cloud's CEO likewise highlighted that accelerating drug discovery is one of the most important applications of AI and HPC in healthcare, and bringing together Bayer's R&D expertise with Google's infrastructure can get medicines to patients faster (^[29]).

(^[30]) Example of an AI-driven drug discovery pipeline using Google Cloud services (source: Recursion Pharmaceuticals). Data from high-throughput experiments is stored in Google Cloud Storage and processed through containerized pipelines on GKE (Google Kubernetes Engine) with TensorFlow. Google's TPUs are used to train deep learning models on massive image datasets, with results stored and analyzed in BigQuery and Cloud SQL. This scalable GCP pipeline enabled Recursion to discover new drug candidates in a fraction of the usual time (^[31]) (^[32]).

Another trailblazer is Recursion Pharmaceuticals, a U.S.-based biotech that has built its entire drug discovery platform on Google Cloud. Recursion is creating an "AI-enabled map of human biology" to find treatments for rare diseases, combining automated lab experiments with cloud-scale analysis (^[33]). They generate terabytes of cellular microscopy images and use deep learning to detect how different molecules affect cells. With Google Cloud, Recursion can burst to trillions of computations per second when needed (^[31]). They leverage GKE (Google Kubernetes Engine) both in the cloud and on-premises to seamlessly integrate their lab systems with cloud pipelines (^[34]). Crucially, Recursion uses Google's TPUs to accelerate image analysis and model training, cutting processes that once took hours down to minutes (^[35]). This has led to tangible R&D wins: in under two years, Recursion built hundreds of disease models and even advanced a new drug candidate into a Phase I trial – one of the first AI-discovered drugs to reach human testing (^[32]). Their ambitious goal is to discover 100 new clinical candidates in the company's first 10 years, vastly outpacing traditional pharma pipelines (^[36]). Recursion's cloud-native approach demonstrates how GCP's AI tools (TensorFlow, Vertex AI) and scalable compute can shrink drug discovery timelines by an order of magnitude. In 2025, Recursion has multiple clinical programs underway, including Phase 1/2 trials for REC-1245 (a potential first-in-class RBM39 degrader for solid tumors and lymphoma) and Phase 2 trials for REC-3964 (a C. difficile toxin B inhibitor).

Google Cloud is directly enhancing these AI capabilities with specialized solutions for pharma. Building on the 2023 launch of the Target and Lead Identification Suite (for protein structure and function prediction, built on DeepMind's AlphaFold) and the Multiomics Suite (for genomics and multi-omics data integration), Google has significantly expanded its offerings. AlphaFold 3, released in 2024, represents a major leap forward – capable of predicting not just protein structures but also how proteins interact with DNA, RNA, ligands, and other molecules. Early adopters including Pfizer, Cerevel, and Colossal Biosciences continue using GCP's high-performance computing and AI to predict protein 3D structures in a short time. During the COVID-19 pandemic, GCP also showcased its strength in collaborative HPC: Google provided 16 million hours of free GPU time to Schrödinger (a computational chemistry company), which teamed up with Takeda, Novartis, and Gilead to virtually screen billions of molecules against coronavirus targets (^[37]) (^[38]). That amounted to 1,826 years of computing in a matter of months for antiviral drug discovery (^[38]). These examples underscore how GCP's advanced AI and HPC infrastructure is accelerating drug discovery – from crunching molecular simulations to powering cutting-edge generative AI models – for pharma organizations that embrace the cloud.

Streamlining Clinical Trials and Healthcare Integration

Clinical development is another area where pharma companies are using Google Cloud to gain an edge. We saw how Moderna tapped GCP to integrate clinical trial data and enable real-time analytics. In addition, Google Cloud offers tools to improve patient recruitment, engagement, and data capture in trials. For instance, GCP's global network and analytics can help expand trial site coverage and monitor data centrally, lowering enrollment cycle times and improving cross-site collaboration (^[39]) (^[40]). Google Workspace and Apigee API management can be used to build secure investigator portals or patient apps that feed data directly into a trial database. Compliance with stringent regulations (HIPAA, 21 CFR Part 11, GDPR, etc.) is built into Google's healthcare offerings – the Cloud Healthcare API enables secure ingestion of medical records in formats like FHIR and DICOM, while maintaining audit logs and granular access controls (^[41]) (^[42]). This is critical for trials that might integrate electronic health record data or remote patient monitoring data.

AI agents are emerging as a significant trend in healthcare and life sciences. According to a Google Cloud survey in October 2025, 44% of healthcare and life sciences executives said their organizations were actively using AI agents, with 34% reporting they use 10 or more agents. For example, Hackensack Meridian Health built multiple AI agents using Google's generative AI technology, including a tool that can recap patients' medical records for doctors. Basalt Health launched advanced AI agents to support medical assistants in preparing patient charts and identifying care gaps.

Pharma companies are also leveraging GCP to integrate real-world data and pharmacovigilance. For example, a pharmacovigilance team could use BigQuery to aggregate adverse event reports and real-world evidence from various sources, then apply Vertex AI to detect safety signals (patterns of side effects) faster than manual review. The scale of Google's analytics means millions of data points (e.g. medical claims, patient registry data) can be queried in seconds, helping safety scientists identify risks or efficacy trends sooner. In the realm of precision medicine trials, GCP's ability to combine genomic data (via the Multiomics Suite) with clinical outcomes can help researchers identify genetic biomarkers that predict drug response.

Moreover, Google's cloud-based collaboration tools are improving how pharma works with external partners (CROs, research institutes, regulators). Through secure APIs and data sharing platforms, companies can grant controlled access to data sets or AI models without moving everything on-premises. For example, the Broad Institute's Terra platform (co-developed with Verily and Microsoft, but initially on GCP) allows researchers worldwide – including pharma R&D teams – to co-analyze large biomedical datasets in a compliant cloud environment. This kind of cloud-enabled collaboration was invaluable during COVID-19 vaccine development and is becoming the norm for large-scale trials and research consortiums.

Enhancing Manufacturing, Supply Chain, and Compliance

In addition to R&D and trials, pharma IT leaders are moving manufacturing and supply chain operations onto Google Cloud to gain more agility. Manufacturing execution systems (MES) and IoT sensors in production lines can stream data to GCP in real time for analysis. With tools like Cloud Pub/Sub and BigQuery, companies can perform predictive analytics on factory data – for example, predicting equipment maintenance needs or analyzing production yield metrics across sites. Sanofi's use of AI on GCP to forecast sales and optimize supply chain is one concrete example. By training demand forecasting models on sales, geographic, and logistics data, Sanofi improves the accuracy of production planning and inventory management. These cloud-based forecasts consider real-time information and constraints (like shipping or manufacturing lead times) to adjust plans dynamically. In a regulated industry, even small efficiency gains in the supply chain can translate to significant cost savings and better drug availability for patients.

Regulatory compliance and data security are paramount in all these operations – and GCP has made strides to assure pharma companies and regulators that cloud systems can be trusted. Google Cloud provides detailed guidance on building GxP-compliant environments on its platform ([PDF] Using Google Cloud in GxP Systems). For instance, GCP services can be qualified/validated for use in GMP manufacturing or GLP lab systems, with documentation to support FDA and EMA requirements (^[41]) (^[42]). Specialized partners like USDM Life Sciences work with Google to offer continuous compliance solutions for GxP workloads on GCP (^[42]). This means pharma companies can run validated applications (e.g. for batch record management or lab data capture) in the cloud and maintain compliance via proper change control and qualification processes. Many firms are now convinced that the cloud can be a GxP-compliant solution as long as best practices are followed (^[43]). In fact, those pharmas that have transitioned to cloud report benefits like standardized processes across sites and faster innovation cycles, while meeting quality requirements.

Security is another area where Google Cloud is actively supporting pharma IT. The biopharma sector has been a major target for cyber threats (from IP theft to ransomware), especially during COVID. Pfizer, for example, faced a surge of attacks during its vaccine development. In response, Pfizer turned to Google Cloud's Security Operations (Chronicle) platform to bolster its defenses. According to Google, Pfizer now leverages Cloud Security Operations to "stitch together all of its security technologies and protect all of its cloud assets and environments." (^[44]). By aggregating and analyzing security telemetry at Google's scale, Chronicle helps Pfizer detect threats across multi-cloud and on-prem systems in seconds. It provides an AI-powered SIEM (Security Information and Event Management) that can handle the massive data volumes of a global pharma and pinpoint anomalies that human analysts might miss. This move highlights that pharma companies not only trust Google for analytics, but also for mission-critical security and compliance tooling.

API Management and Digital Health Innovation

Another key Google Cloud service seeing uptake in pharma is Apigee API Management. Pharma organizations are increasingly building digital products – from patient support mobile apps to connected devices (e.g. smart inhalers, insulin pumps) – that need to exchange data securely with cloud systems. Apigee provides a secure, scalable layer to manage all these APIs. A prime example is BrightInsight, a U.S.-based platform that many pharma and medtech companies use to host their regulated digital health apps. BrightInsight's platform, built on GCP, uses Apigee to handle over 20 million API calls per day from FDA-regulated medical devices, combination products, and software apps (^[45]) (^[46]). Apigee simplifies the complex mesh of APIs and microservices, providing enterprise-grade security (OAuth2, encryption) and an average response time under 500ms for each API call (^[47]) (^[48]). This allows, for example, a connected pill bottle or glucose sensor to transmit data to the cloud, where it's stored (Cloud Storage/BigQuery) and analyzed, and then an insight is sent via API to a patient's mobile app or doctor's dashboard. By offloading API management to Apigee, BrightInsight's small team could rapidly deploy globally compliant digital health solutions for pharma clients – instead of each pharma building their own backend. As BrightInsight's CTO put it, "Having Apigee handle the APIs for us is key. Apigee allows us to offer a full feature set to our customers from day one." (^[46]). In December 2024, BrightInsight announced a partnership with Sanofi to build drug companion apps for its major specialty therapies, demonstrating continued industry adoption of the platform. This example underscores how GCP enables integration of healthcare IoT and software as a medical device in a compliant manner. Pharma companies using BrightInsight or similar platforms can more quickly launch patient-centric tools (like companion apps that track dosage adherence or report side effects) and derive real-world data to augment their product value propositions.

Apigee is also used internally by pharmas to modernize legacy systems. Many large pharma IT landscapes include decades-old systems (for ERP, lab management, etc.) that need to interface with newer cloud apps. By placing Apigee as an API gateway, companies create a unified interface for developers to access backend data, without exposing the old systems directly. This supports initiatives like creating a single "data fabric" across R&D, manufacturing, and commercial units, which is a goal of many digital transformation projects. GCP's integration services (Cloud Pub/Sub, Data Fusion, Workflows) complement Apigee to enable event-driven and batch data integration – for example, ingesting equipment sensor data into analytics, or synchronizing a cloud data lake with on-prem databases.

GCP vs. AWS and Azure in the Pharma Cloud Race

How does Google Cloud stack up against its two main competitors in the pharma sector? AWS (Amazon Web Services) remains the market leader in cloud and is widely used across biotech and pharma. In fact, AWS is often the default choice for biotech startups and is used by large pharmaceutical companies as well (^[49]). Its early lead and breadth of services mean many developers and IT consultants are deeply familiar with AWS. Pharma companies like Moderna have relied on AWS for core infrastructure (Moderna named AWS its preferred cloud in 2020 for certain workloads (^[50])), and other big players have long-standing engagements with AWS for scalable computing and storage. AWS has also developed life-science-specific offerings – for example, a Genomics CLI and blueprint solutions for multi-omics pipelines (^[51]) – to cater to research needs. This, combined with AWS's large partner ecosystem, makes it a strong incumbent especially for high-throughput sequencing data analysis, HPC workloads, and traditional enterprise IT in pharma.

Microsoft Azure, on the other hand, has gained traction particularly among big, established pharma companies that value its enterprise integration. Azure often appeals to highly regulated organizations and those with a lot of Microsoft technology stack in-house (^[52]). Pharma firms that are Windows/.NET shops or that want seamless integration with tools like Active Directory and Office 365 may lean toward Azure. Microsoft has struck notable pharma partnerships – a prime example being Novartis's AI innovation lab with Azure, a multi-year collaboration to apply Microsoft's AI across Novartis' drug development and manufacturing (^[53]) (^[54]). Azure's emphasis on hybrid cloud is also a draw: many pharma data centers run VMware or Windows Server, and Azure Arc/Azure Stack allow a gradual, hybrid approach to cloud. For instance, a pharma can keep sensitive workloads on-premises but use Azure for burst computing or AI model training, all managed under one framework. Azure's focus on security and compliance is another selling point – it starts with more restricted default settings (which can feel "locked down" but appeals to compliance teams) (^[55]). Overall, Azure has positioned itself as the safe, enterprise-friendly cloud, and we see companies like Merck, Johnson & Johnson, and AstraZeneca using Azure for parts of their cloud portfolio (often alongside other clouds).

Google Cloud, relative to AWS and Azure, has a smaller overall market share but is making significant inroads in life sciences by leveraging its strengths in data analytics, AI, and open collaboration. One strategic move was Google's partnership with Accenture for the Intient platform, which effectively onboarded several big pharmas onto GCP via a domain-tailored solution (^[12]). Another differentiator is Google's leadership in AI – pharmas interested in cutting-edge AI (e.g. image recognition, generative models, or advanced analytics like AlphaFold for protein folding) find GCP attractive. Google's Nobel Prize-winning AlphaFold technology and the growing capabilities of Gemini and Med-Gemini provide unique value for drug discovery and medical AI applications. For example, AstraZeneca has been exploring AI for drug discovery with various partners; while they use multiple clouds, Google's AI offerings (like the Vertex AI platform and collaborations via Google's AI teams) can be a compelling reason to engage with GCP for specific projects. Google also tends to embrace open-source technologies (TensorFlow, Kubernetes, Apache Beam, etc.) which resonate with scientific computing communities and avoid vendor lock-in. This openness can be appealing to pharma IT architects who want flexibility and multi-cloud portability in the long run.

Many large pharma companies are not choosing one cloud over another, but rather adopting multi-cloud strategies to play to each provider's strengths. As noted, Moderna is explicitly multi-cloud, using AWS for some core data storage but GCP for analytics and BI, thereby "using and integrating the best tools for the job at hand" (^[56]) (^[17]). Johnson & Johnson likewise has a multi-cloud approach – they have publicly discussed using "the right cloud for the right workload" to drive innovation while managing risk (^[57]). The December 2025 AWS-Google Cloud multicloud partnership further enables this approach, allowing customers to connect the two cloud platforms more seamlessly through AWS Interconnect and Google Cloud's Cross-Cloud Interconnect. In multi-cloud setups, Google Cloud often serves as the analytics and machine learning hub, interfacing with transactional data stored in AWS or Azure. Tools like Apigee and BigQuery Omni (which can query data in other clouds) facilitate this interoperability. The Intient platform itself was designed with an open architecture on GCP to integrate many software vendors and even other clouds' data sources (^[12]) (^[58]). For pharma companies, this means they can run, say, an AI-driven analysis pipeline on GCP using data that might reside in an AWS S3 bucket or on-prem Oracle database – thus leveraging GCP's analytics without abandoning existing investments on other platforms.

In terms of competitive landscape, AWS still hosts a larger share of pharma cloud workloads (benefiting from being the first mover), and Azure's deep enterprise relationships have won it strategic deals in pharma (especially for corporate IT and collaboration services). However, Google Cloud is rapidly growing its footprint in pharma R&D and analytics, particularly as generative AI becomes central to drug discovery. Google Cloud is committing $25 billion over two years for data center and AI infrastructure expansion, with total capital expenditure for 2025 ranging from $75–85 billion. A recent analysis of cloud AI case studies noted that while AWS leads in overall market share, Google's share of new AI/ML case studies (18%) is higher than its general cloud market share, indicating outsized momentum in AI applications (^[59]). The pharma industry, increasingly convinced of cloud value, is expected to utilize all three major clouds in various capacities. GCP's role is often that of the "innovation cloud" – the place to experiment with big data and AI, to run collaborative projects, and to tap into Google's unique technologies (from TPUs and BigQuery to specialized APIs for healthcare and Gemini-powered AI agents). As pharma companies race to shorten drug development cycles and derive more insight from data, Google Cloud's capabilities in handling large-scale data and providing off-the-shelf AI solutions position it as a key partner.

Conclusion

In summary, Google Cloud Platform has emerged as a critical enabler for U.S. pharmaceutical companies aiming to become more data-driven and agile. From drug discovery (where GCP's AI and HPC help design molecules and identify targets faster) to clinical trials (where integrated cloud data platforms provide real-time insights and efficiency) to manufacturing and compliance (where IoT data and ML optimize operations under strict regulations), GCP is being leveraged in numerous ways across the pharma value chain. We see big pharmas like Pfizer, Moderna, Sanofi, and Bayer taking advantage of Google Cloud's offerings – often in combination with other clouds – to accelerate innovation while controlling costs and maintaining compliance. Popular GCP services in pharma include BigQuery for large-scale analytics, Looker for interactive data exploration, Vertex AI for building and deploying machine learning models, Gemini and Med-Gemini for healthcare-specific generative AI, TPU accelerators for compute-intensive research, and Apigee for secure API management and system integration. Real-world partnerships and case studies illustrate these in action: Intient on GCP bringing AI to R&D at GSK and Pfizer (^[12]), Moderna's multi-cloud trial data hub improving collaboration (^[18]), Bayer's TPU-powered quantum chemistry speeding up lead discovery (^[26]), Recursion's expanded cloud AI platform yielding new treatments in record time (^[32]), Isomorphic Labs bringing the first AI-designed drugs to clinical trials, and BrightInsight+Apigee enabling compliant digital health solutions with ease (^[60]) (^[61]).

Looking ahead, as pharmaceutical companies continue to embrace cloud-first or hybrid-cloud strategies, Google Cloud is poised to play an even larger role. Its ongoing investments in healthcare-specific AI (e.g. AlphaFold 3, MedGemma, healthcare AI agents) and industry partnerships (Accenture, Recursion, Isomorphic Labs, etc.) are likely to expand the toolkit available to pharma IT and data teams. The 2024 Nobel Prize for AlphaFold and the impending clinical trials of AI-designed drugs represent inflection points that validate Google's AI-first approach to life sciences. The competitive dynamic with AWS and Azure will push all providers to address pharma's needs for better data governance, cost efficiency, and validated solutions. For IT professionals in pharma, the key is understanding each cloud's strengths: AWS for its mature ecosystem and breadth, Azure for enterprise integration, and GCP for cutting-edge data science, generative AI, and open innovation. With the right architecture (often a multi-cloud one), pharma companies can leverage GCP's unique capabilities while interoperating with other platforms – achieving a balance of innovation, compliance, and reliability. The end result is a tech infrastructure that empowers researchers and operations teams to deliver new therapies to patients faster and more efficiently than ever before, fulfilling the promise of digital transformation in healthcare.

Sources:

• Accenture and Google Cloud exclusive partnership for the Intient pharma platform – top pharmas (GSK, Pfizer, Bayer) among initial users (^[12]).
• Moderna case study – adopted Google Cloud and Looker for multi-cloud data integration, yielding a complete view of clinical trials, higher efficiency, and real-time decision making (^[18]) (^[19]).
• Bayer collaboration with Google – using Cloud TPUs to accelerate quantum chemistry simulations for drug discovery (protein–ligand modeling) (^[26]) (^[27]).
• Recursion case study and 2024 partnership expansion – uses GCP (TPUs, TensorFlow, GKE, Gemini) to process cellular images and train deep learning models; achieved one of the first ML-identified drugs to reach clinical trials (^[33]) (^[32]) (^[6]).
• AlphaFold 3 launch (May 2024) and Nobel Prize (October 2024) – Google DeepMind's breakthrough AI for predicting protein structures and molecular interactions; now used by over 3 million researchers globally (^[4]).
• Isomorphic Labs – Alphabet's AI drug discovery spinoff preparing first human clinical trials for AI-designed drugs in 2025; raised $600M in funding and secured partnerships with Eli Lilly and Novartis (^[5]).
• BrightInsight case study and 2024 generative AI expansion – built a regulated digital health platform on GCP; Apigee manages ~20 million API calls/day from pharma devices/apps with <0.5s latency; expanded partnership with Google Cloud for Gemini and MedLM integration (^[60]) (^[61]) (^[8]).
• Med-Gemini and MedGemma – Google's healthcare-specific AI models achieving 91.1% accuracy on medical benchmarks; MedGemma launched at Google I/O 2025 as open-source healthcare AI (^[7]).
• Pharmaceutical cloud computing market size – estimated at $18-21B in 2024, projected to exceed $55B by 2033 (^[1]).
• AI investment in pharma – estimated to reach $2.51B in 2026 and $16.49B by 2034 (^[2]).
• AWS-Google Cloud multicloud partnership (December 2025) – joint collaboration for easier multicloud connectivity (^[10]).
• Pfizer security transformation – Pfizer leverages Google Cloud Security Operations (Chronicle) to unify and protect all its cloud environments, strengthening cyber defense in the pharma landscape (^[44]).