Pirros vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Blueprint reuse platform for architects and structural engineers finding past design details; $17M Series A with 120% NRR serving 200+ firms competing with Autodesk for AEC drawing management.
About
Pirros is a construction technology company providing a blueprint and drawing management platform for architects, structural engineers, and construction firms — specifically enabling design professionals to find, reuse, and adapt relevant drawings and details from past projects rather than starting from scratch on each new project. Founded by structural engineers Ari Baranian and Peter Johann and backed by Y Combinator, Pirros raised $19 million total including a $17 million Series A in 2024, serving 200+ firms and achieving $3 million ARR with 10% month-over-month growth and 120% net revenue retention.\n\nPirros's platform serves as an intelligent drawing library and search system for engineering and architectural firms — practitioners search for specific design solutions (connection details, structural systems, specification sections) and find relevant drawings from past projects with intelligent filtering by building type, structural system, code version, and other parameters. Rather than each project starting from blank sheets or generic templates, engineers retrieve proven designs from their firm's institutional knowledge base. This reuse reduces drafting time, improves consistency, and prevents re-solving problems that the firm has already addressed on previous projects.\n\nIn 2025, Pirros competes in the AEC (architecture, engineering, construction) software market with Procore, Autodesk (AutoCAD, Revit, BIM 360), and BlueBeam for construction document management. The 120% net revenue retention rate indicates strong expansion within existing accounts — as more teams within a firm use Pirros and contribute drawings to the knowledge base, the value of the platform grows. The engineering firm market is highly relationship-driven, with word-of-mouth between structural engineering firms being an important growth channel. The 2025 strategy focuses on expanding beyond structural engineering to MEP (mechanical, electrical, plumbing) engineering firms, deepening AI search capabilities that surface the most relevant historical details for current projects, and growing from regional to national engineering firm adoption.
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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