What tech stack does Eli Lilly use?

AWS is Lilly's confirmed primary cloud, used across scientific computing, data engineering, and application hosting. The company uses EC2 for high-performance computing (HPC clusters that can spin up in minutes rather than months on-premise — a documented business case in Lilly's AWS partnership materials), S3 for data storage, Lambda for serverless automation, and EKS (Elastic Kubernetes Service) for containerized application deployments managed via Argo CD and Argo Workflows in a GitOps model. Infrastructure as code is managed through Terraform and Ansible, reflecting a modern DevOps posture within a regulated pharmaceutical environment.

The ERP backbone is SAP, with global standardization across SAP ERP 6.0 and at least 17 connected SAP modules, including SAP Process Control (GRC), SAP Business Warehouse (analytics), SAP SCM (supply chain), SAP SRM (procurement), SAP Identity Management, and SAP Global Trade Services. This SAP investment represents decades of enterprise standardization across Lilly's global manufacturing, supply chain, and finance operations. Lilly's Software Platform Engineering organization also builds internal tooling: knowledge graphs, AI coding infrastructure, data cataloging, developer experience platforms, and the LillyDirect patient-facing digital infrastructure.

For data warehousing and analytics, Lilly has migrated key data pipelines to AWS Redshift, with AWS Glue for ETL orchestration and AWS Athena for ad-hoc querying across S3 data lakes. Power BI is deployed for business dashboards across finance, operations, and commercial teams. Web properties use Google Analytics for site analytics and jQuery-based frontend components, while internal tools such as Lilly Translate use a serverless stack of Node.js, Python, .NET, and Docker containers on AWS.

For commercial and sales technology, Lilly is a confirmed Salesforce customer — it has expanded the Salesforce Platform to build patient support apps, healthcare professional (HCP) engagement tools, and care team coordination workflows across its US commercial organization, particularly for LillyDirect. Veeva CRM is used by the field sales force for HCP call planning and compliance-grade engagement tracking. Workday handles HR, absence management, and people operations globally. In October 2024, Lilly appointed Thomas J. Fuchs as its first Chief AI Officer, signaling a major and sustained investment in enterprise AI infrastructure across drug discovery, clinical operations, and commercial analytics — creating active budget for AI/ML platform vendors.

Lilly's AWS-first posture means cloud-native solutions that deploy on AWS face the lowest integration friction. If you sell data, ML, or analytics tools, AWS-native deployment — or strong Redshift and S3 connectors — is a prerequisite conversation, not a differentiator. SAP's deep entrenchment means ERP displacement is effectively impossible at this scale; however, SAP-adjacent tools (process mining, accounts payable automation, GRC overlays, supply chain analytics) have a clear on-ramp via existing SAP relationships and are actively procured in the pharmaceutical industry.

For CRM and commercial technology, Lilly's Salesforce and Veeva stack is entrenched. Displacing Veeva CRM is a long-cycle, high-risk play for any vendor. However, the expansion of LillyDirect (the D2C patient platform launched in 2025) and Lilly's stated goal of building patient-centric digital apps on Salesforce Force.com creates active demand for Salesforce ISV tools, integration middleware (MuleSoft, Boomi), and patient engagement solutions. The Chief AI Officer appointment signals open and growing budget for AI/ML tooling across R&D (drug discovery, biomarker analysis, clinical trial optimization) and commercial (patient adherence, HCP targeting, market access analytics). Lilly's build-vs-buy posture leans toward buying for enterprise foundations (SAP, Salesforce, Workday, AWS) while building internally for differentiated scientific and AI capabilities — a classic large-pharma pattern that creates predictable vendor opportunity maps.