Blacksmith vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
San Francisco GitHub Actions CI runner at $3.5M ARR with 700+ customers by Sept 2024; $13.5M total ($10M GV Series A) with 2x speed/75% cost reduction through gaming-grade CPUs founded Jan 2024 from YC W24.
About
Blacksmith is a San Francisco, California-based high-performance CI/CD infrastructure platform — backed with $13.5 million in total funding including a $10 million Series A in September 2024 led by Google Ventures (four months after a $3.5 million seed round also led by Google Ventures) — providing development teams with drop-in replacement GitHub Actions runners that deliver up to 2x faster CI pipeline execution and up to 75% compute cost reduction through purpose-built gaming-grade CPUs rather than standard AWS/Azure cloud instances. Founded in January 2024 by University of Waterloo alumni Aditya Jayaprakash (CEO), Aayush Shah, and Aditya Maru (Y Combinator Winter 2024 batch), Blacksmith reached $1 million ARR within one month of launch and $3.5 million ARR with 700+ customers by September 2024. Notable angel investors include Spencer Kimball (CEO Cockroach Labs), David Cramer (co-founder Sentry), and other developer tools founders.
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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