Thinking Machines Lab vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
AI research lab founded by ex-OpenAI CTO Mira Murati. Raised $2B at $12B valuation from a16z, Nvidia, AMD; signed multi-year Nvidia compute deal in 2026.
About
Thinking Machines Lab was founded by Mira Murati, who served as Chief Technology Officer of OpenAI before departing in 2024. Murati's tenure at OpenAI oversaw the development and launch of GPT-4, DALL-E, and ChatGPT, giving her unmatched firsthand experience with the technical and organizational challenges of frontier AI development. She founded Thinking Machines Lab with the mission of building AI systems that are both highly capable and deeply aligned with human values and intent.\n\nThinking Machines Lab operates as an AI research organization focused on developing next-generation foundation models and the scientific frameworks needed to make powerful AI systems more reliable, interpretable, and beneficial. The company has assembled a team of researchers from leading AI institutions and is building the compute infrastructure necessary for frontier model training. It secured a multi-year compute agreement with NVIDIA, ensuring access to the GPU capacity required to compete at the frontier of AI research.\n\nThe company raised $2 billion at a $12 billion valuation, with participation from Andreessen Horowitz, NVIDIA, and AMD — a capital raise that ranks among the largest first rounds in technology history. The involvement of both major AI chip vendors as strategic investors signals deep alignment between Thinking Machines Lab's compute roadmap and the hardware ecosystem. With Murati's profile, the caliber of its investor base, and its frontier research mandate, Thinking Machines Lab is positioned as one of the most closely watched AI labs in the post-ChatGPT era.
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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