ezyVet vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Cloud Veterinary Practice Management & Client Engagement
Cloud vet practice management acquired by IDEXX; Auckland New Zealand; 5,000+ clinics in US, Australia, NZ, and UK; covers patient check-in through invoicing and end-of-day reconciliation.
About
ezyVet is a cloud-based veterinary practice management and client engagement platform that provides veterinary clinics with patient records, scheduling, invoicing, billing, client communication, and analytics in a fully browser-based system, headquartered in Auckland, New Zealand and acquired by IDEXX Laboratories — the global veterinary diagnostics leader — in 2021 to extend IDEXX's software platform capabilities alongside its diagnostic products. ezyVet serves more than 5,000 veterinary clinics globally with particular strength in the United States, Australia, New Zealand, and the United Kingdom.\n\nezyVet's platform provides a comprehensive clinical workflow covering patient check-in, consultation and treatment planning, prescription management, invoicing, and end-of-day reconciliation. The two-way client communication features include automated appointment reminders via SMS and email, post-visit summaries, and a client portal where pet owners can access records and request appointments. Deep integration with IDEXX's diagnostic products — including in-clinic analyzers, reference laboratory services, and imaging systems — allows results to flow directly into patient records, providing a significant competitive advantage now that IDEXX owns the platform.\n\nAs an IDEXX-owned platform, ezyVet benefits from IDEXX's global distribution network, integration with IDEXX's full diagnostics portfolio, and the ability to bundle software with diagnostic equipment and service agreements. ezyVet competes with Provet Cloud, Vetspire, Digitail, and the legacy IDEXX Cornerstone platform in the veterinary software market. IDEXX's ownership creates both an advantage (deep diagnostics integration) and a competitive consideration for clinics that use non-IDEXX diagnostic products.
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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