ON24 vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
B2B digital event platform with $160M revenue; webinar engagement data (poll responses, downloads, time watched) as first-party pipeline intelligence feeding Salesforce and Marketo.
About
ON24 is a digital experience platform for B2B marketing, enabling companies to run webinars, virtual conferences, and on-demand content experiences that capture audience engagement data and convert viewers into qualified sales pipeline. Founded in 1998 and headquartered in San Francisco, ON24 is listed on NYSE (NYSE: ONTF) and generates approximately $160 million in annual revenue serving enterprise marketing teams at companies including Microsoft, SAP, and Merck who use digital events as a primary demand generation channel.\n\nON24's platform goes beyond basic webinar hosting — every attendee interaction (poll responses, Q&A questions, resource downloads, content watched, time spent) is captured as first-party engagement data that flows into CRM and marketing automation systems (Salesforce, Marketo, HubSpot) as behavioral intent signals. This transforms webinars from one-way broadcasts into data-generating demand generation assets. ON24 Intelligence analyzes this engagement data to score prospects by their buying signal strength based on their webinar behavior patterns.\n\nIn 2025, ON24 operates in the digital events and webinar platform market that exploded during COVID-19 and has since contracted as in-person events returned. The company has repositioned from "webinar platform" to "digital experience platform" emphasizing the first-party data and pipeline generation value rather than the meeting technology. ON24 competes with Zoom Webinars, Hopin (now RingCentral Events), Cvent, and video marketing platforms for B2B event and content marketing budgets. The 2025 strategy focuses on ON24 AI-powered personalized content experiences (where each viewer sees customized content based on their profile), growing its Content Hub (always-on content experience) product, and helping marketers demonstrate revenue attribution from digital events.
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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