DocuSign CLM vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
FY 2025 (ended Jan 31, 2025): Revenue $2.977B (+8% YoY); 1.7M customers in 180 countries; 1B+ users; 1,131 customers with $300K+ ACV (up from 1,060 in 2024)
About
DocuSign CLM is the contract lifecycle management platform from DocuSign, the San Francisco-based agreement cloud company founded in 2003. While DocuSign's eSignature product revolutionized document execution, CLM addresses the broader contract management lifecycle — authoring, negotiation, approval workflows, obligation tracking, and renewal management — giving legal, sales, and procurement teams a unified system for every stage of a contract from first draft to renewal. The platform's core technology uses AI to extract, classify, and surface contract metadata from both executed agreements and legacy documents.\n\nDocuSign CLM serves enterprise customers in financial services, life sciences, technology, and professional services who manage high volumes of complex contracts with multiple counterparties and jurisdictions. Key differentiators include deep integration with DocuSign eSignature, Salesforce, and major ERP systems, enabling contracts to flow automatically through CRM and procurement workflows. The Intelligent Agreement Management layer — powered by DocuSign AI — adds risk flagging, obligation extraction, and clause recommendations that reduce legal review time and surface buried contract risks.\n\nDocuSign reported FY2025 revenue of $2.977 billion, an 8% year-over-year increase, with 1.7 million customers across 180 countries and more than one billion users having touched the platform. The company counts 1,131 customers with $300,000+ annual contract values, reflecting strong enterprise adoption of its expanded platform beyond eSignature. As companies face increasing pressure to extract value from contracted commitments and reduce compliance risk, DocuSign CLM's position within the world's most trusted agreement infrastructure gives it a privileged entry point into enterprise contract intelligence.
Serverless GPU cloud platform for AI/ML with Python-native deployment and per-second billing; developer-favorite scaling from zero competing with Replicate and Beam for AI compute.
About
Modal is a serverless cloud computing platform purpose-built for AI and machine learning workloads — providing on-demand GPU compute that scales instantly from zero with per-second billing, container management, distributed training support, and a Python-native developer experience that makes running ML workloads in the cloud feel as simple as running code locally. Founded in 2021 in New York City and backed by Redpoint Ventures and other investors, Modal has grown rapidly as AI development has accelerated demand for flexible, developer-friendly GPU infrastructure.\n\nModal's developer experience is its primary differentiator — engineers write Python functions decorated with @modal.function() and deploy them to the cloud with a single command, with Modal handling container building, GPU provisioning, auto-scaling, and execution. The platform supports training jobs that need distributed compute across multiple GPUs, model serving endpoints that scale to zero when unused (eliminating idle GPU costs), and batch inference jobs that process large datasets. The per-second billing model means developers pay only for actual compute time, not provisioned instances.\n\nIn 2025, Modal competes in the AI infrastructure market with Replicate, Beam, Banana, and major cloud providers' managed ML services (AWS SageMaker, Google Vertex AI, Azure ML) for serverless GPU compute. The market for AI-specific cloud infrastructure has grown dramatically as the number of ML engineers deploying models to production has expanded — traditional cloud providers require significant DevOps expertise to use GPU instances effectively, while Modal's Python-native approach reduces the barrier to entry. Modal has attracted a strong developer following among AI researchers and ML engineers building production AI applications. The 2025 strategy focuses on growing the developer community, adding enterprise features (dedicated GPU capacity, private networking, compliance), and expanding the hardware options available (H100 GPUs, custom accelerators).
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