Gather AI vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Drone-based warehouse inventory. 99.9% accuracy, 80% less manual counting. $74M raised ($40M Series B Feb 2026). Carnegie Mellon spinout. 250% bookings growth.
About
Gather AI is a warehouse automation company that uses autonomous drones to conduct inventory counts with a level of accuracy and frequency that manual processes cannot achieve at comparable cost. A spinout from Carnegie Mellon University, the company was founded on robotics and computer vision research that enables drones to navigate complex warehouse environments, read barcodes and RFID tags, and build precise inventory maps without requiring warehouse modifications or dedicated human operators to supervise each flight.\n\nThe Gather AI system delivers 99.9% inventory accuracy and reduces manual counting labor by 80%, enabling warehouse operators to conduct cycle counts far more frequently than traditional quarterly or annual physical inventories allow. Higher-frequency inventory data directly improves operational efficiency: it reduces stockouts, prevents overordering, accelerates order fulfillment, and gives supply chain teams the real-time visibility they need to respond to demand shifts and supply disruptions. The system is designed to operate during normal warehouse hours without disrupting ongoing picking and fulfillment operations.\n\nGather AI raised $40 million in a Series B in February 2026, bringing total funding to $74 million, and has achieved 250% year-over-year growth as warehouse operators accelerate automation investment. The company is operating in a warehouse automation market driven by e-commerce growth, labor cost inflation, and increasing supply chain complexity. Drone-based inventory, once a proof-of-concept, has matured into a commercially proven solution that Gather AI is deploying at scale across distribution centers, 3PLs, and retailers seeking to modernize their inventory management without capital-intensive fixed infrastructure.
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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