Certn vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Modern Background Screening for SMBs and Global Hiring
Modern API-first background screening platform for SMBs and international hiring; Vancouver BC; raised $52M+; faster turnaround times and digital-first candidate experience versus legacy paper-based verification providers across criminal, employment, and identity checks.
About
Certn is a modern, API-first background screening platform that provides criminal checks, soft checks, employment verification, identity verification, and international screening in a fast and candidate-friendly digital experience designed for SMBs, high-growth companies, and employers with global hiring needs. Founded in 2016 and headquartered in Vancouver, British Columbia, Certn has raised more than $52 million and built a platform that emphasizes faster turnaround times, better candidate experience, and seamless integration capabilities compared to legacy background screening providers that rely on paper-based processes and manual verification workflows.\n\nCertn's platform delivers a digital-first candidate experience where screened individuals complete consent, upload identification documents, and receive status updates through a clean mobile-optimized interface — improving completion rates compared to cumbersome legacy portals. The company offers coverage across Canada, the United States, United Kingdom, and more than 200 countries for international background checks. API-first architecture allows HR software platforms, gig economy marketplaces, and ATS vendors to integrate Certn's screening directly into their hiring workflows as a seamlessly embedded capability.\n\nCertn competes with Checkr, Sterling, and regional Canadian background screening companies in the SMB and mid-market segment. Its Canadian headquarters and strong Canadian market coverage give it advantages in serving Canadian employers underserved by US-centric screening providers, while its global coverage and API capabilities differentiate it for companies with international hiring programs and for HR tech platforms seeking to embed background screening as a feature.
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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