Shepherd Veterinary Software vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
YC-backed cloud veterinary PMS designed from the ground up by veterinarians for modern clinic workflow efficiency. San Francisco CA; founded 2018; integrates scheduling, medical records, treatment tracking, invoicing, and inventory in a single workflow-first platform built to reduce the friction of busy multi-doctor practices.
About
Shepherd Veterinary Software is a San Francisco-based cloud veterinary practice management system backed by Y Combinator and designed from the ground up for the operational workflow needs of modern veterinary clinics. Founded in 2018 by veterinarians and technologists who experienced firsthand the frustrations of legacy practice management systems, Shepherd built a clean, intuitive platform that integrates appointment scheduling, medical records, treatment tracking, invoicing, and inventory management into a unified workflow optimized for the pace and multitasking demands of a busy veterinary practice. Unlike older systems that were designed around a desktop paradigm and adapted for web, Shepherd was built as a cloud-native application from day one.\n\nShepherd's clinical workflow design reflects its founding team's veterinary experience. The platform's whiteboard feature provides a real-time view of every patient in the clinic—which exam room they are in, what treatments have been administered, what orders are outstanding, and who is responsible for each task. This "at a glance" operational view reduces the communication overhead between veterinarians, technicians, and receptionists that leads to errors and delays in busy multi-doctor practices. The treatment sheet auto-populates charges based on administered treatments, reducing the billing leakage common in practices where staff must manually add charges from memory.\n\nShepherd targets independent veterinary practices and small veterinary groups in the United States, competing with established systems including IDEXX's AVImark, ezyVet, Cornerstone, and ImproMed in the veterinary practice management market. The company differentiates on user experience quality, cloud-native reliability, and the speed of its development cycle—as a well-funded startup with a modern tech stack, Shepherd can ship product improvements significantly faster than legacy software vendors. Shepherd has gained traction primarily through referrals within veterinary professional networks and targeted digital marketing reaching veterinary clinic owners and practice managers.
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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