Side-by-side comparison of AI visibility scores, market position, and capabilities
B2B contact data platform for finding phone numbers and emails of prospects. Paris France, acquired by Cognism, provides LinkedIn-integrated prospecting data for European SDR teams.
Kaspr is a B2B contact data platform that helps sales development representatives find phone numbers and email addresses of business prospects, with tight LinkedIn integration that allows data extraction directly from LinkedIn profiles. Founded in 2018 and headquartered in Paris, France, Kaspr was acquired by Cognism to strengthen the combined entity's European contact data coverage and distribution through complementary product channels. Kaspr targets SDR teams seeking fast, LinkedIn-native access to prospect contact data.\n\nKaspr's Chrome extension integrates directly into LinkedIn, allowing SDRs to reveal contact details for prospect profiles with a single click without leaving the LinkedIn interface. The platform's contact data covers mobile phone numbers and professional email addresses for European and international business contacts. Contacts can be pushed to CRM systems and sales engagement platforms directly from the extension, reducing manual data entry in the prospecting workflow.\n\nFollowing its acquisition by Cognism, Kaspr operates as a complementary product in the Cognism portfolio — with Kaspr serving individual SDRs and smaller teams seeking affordable LinkedIn-native prospecting tools, while Cognism's enterprise platform serves larger revenue teams with bulk data exports, intent signals, and GDPR compliance infrastructure. The acquisition strengthened Cognism's coverage of French and broader European prospect data while adding a LinkedIn-integrated product channel to its portfolio.
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.
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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