Roofr vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Roofing Estimating & Proposal Platform
Roofr raised $23M+ for instant aerial roof measurement and proposal software, letting roofing contractors deliver accurate estimates faster than any manual method (San Francisco, 2016).
About
Roofr is a roofing estimating and proposal platform that provides instant roof measurements from aerial imagery combined with professional proposal creation tools, enabling roofing contractors to deliver accurate estimates and polished proposals to homeowners more quickly and professionally than traditional methods allow. Founded in 2016 and headquartered in San Francisco, California, Roofr has raised more than $23 million and built a growing customer base among residential roofing contractors in North America. The company's integrated approach to measurement and proposal — combining instant aerial measurement with customizable proposal templates in a single workflow — differentiates it from competitors that provide measurement data separately from proposal software.\n\nRoofr's platform delivers instant roof measurement reports generated from satellite imagery, providing area, pitch, and facet measurements within minutes of entering an address without waiting for aerial imagery orders. Proposal creation tools allow contractors to build professional, branded proposals that include measurement data, selected materials, pricing, photos, and payment options in a format homeowners can review and digitally sign. The platform tracks proposal views and responses, allowing sales teams to follow up at the right moment. Payment collection tools support deposit collection and final payment processing directly through the platform.\n\nRoofr competes with EagleView and Hover in roof measurement and with JobNimbus, AccuLynx, and other contractor CRMs in the broader roofing software space. Its integration of measurement and proposal in a single affordable platform at a price point accessible to smaller roofing contractors has driven strong adoption among contractors looking for a modern alternative to manual measurement and PDF proposals. The company has been adding CRM and job management features to expand from measurement and proposal into a more complete business management platform for roofing companies.
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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