Hopin vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Acquired by RingCentral $15M Aug 2023 (from $7.8B valuation); sold to Bending Spoons Apr 2024; $1B+ funding raised at peak; rebranded to RingCentral Events; pandemic-era unicorn decline; virtual events platform
About
Hopin was founded in 2019 by Johnny Boufarhat as a virtual event platform designed to replicate the spontaneous networking and multi-session structure of in-person conferences in an online format. The platform launched just before the COVID-19 pandemic forced the global events industry to shift entirely to digital, creating an extraordinary product-market fit moment that drove Hopin from near-zero to a $7.8 billion valuation in under two years — one of the fastest valuation escalations in European startup history. Hopin's core technology offered a multi-stage event architecture with simultaneous sessions, expo halls, and AI-powered attendee matching that no incumbent platform could replicate at launch.\n\nHopin's platform supported virtual and hybrid events ranging from small team off-sites to conferences with tens of thousands of attendees, with features including breakout networking rooms, sponsor booths, live streaming, and analytics dashboards for organizers. The company aggressively expanded through acquisitions, purchasing StreamYard, Streamable, and several other media and production tools to build a broader creator and events infrastructure stack. At its peak, Hopin served thousands of event organizers across enterprise, media, and nonprofit sectors.\n\nHopin's trajectory became one of the defining cautionary narratives of pandemic-era startup valuations. As in-person events returned, demand for virtual-first platforms collapsed. RingCentral acquired Hopin's event platform assets for $15 million in August 2023 — a 99.8% markdown from peak valuation — and rebranded it RingCentral Events. In April 2024, Bending Spoons acquired additional Hopin assets. The company raised over $1 billion in venture funding during its growth phase, making it one of the most studied examples of COVID-era valuation inflation and its aftermath.
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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