BuildJet vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Tallinn Estonia GitHub Actions runner platform using gaming CPUs for 2x faster CI at lower cost; YC W23 $500K with $330K ARR competing with Depot and Blacksmith for CI/CD performance optimization infrastructure.
About
BuildJet is a Tallinn, Estonia-based continuous integration (CI) performance platform — backed by Y Combinator (W23) with $500,000 raised from YC in 2023 — providing developer teams with GitHub Actions runners powered by gaming CPUs (AMD Ryzen and Intel Core processors optimized for high single-core clock speeds) that execute CI/CD builds 2x faster and at lower cost than GitHub's standard hosted runners, enabling companies to reduce their CI infrastructure spend and development cycle time simultaneously. Founded in 2022 by Adam Shiervani and Lian Duan and generating $330,000 in annual recurring revenue as of September 2025 with a 3-person team, BuildJet serves the developer community's need for faster and more affordable GitHub Actions compute.
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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