Together AI vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Open-source AI cloud. $300M ARR (Sep 2025). $3.3B valuation. $533M total raised. Backed by Salesforce, NVIDIA, Kleiner Perkins. Founded by ex-Stanford AI researchers.
About
Together AI was founded in 2022 with a mission to build the leading open-source AI cloud—a platform where developers and enterprises can train, fine-tune, and run inference on open-weight models without the constraints and costs of proprietary AI APIs. The company recognized early that as powerful open-weight models like Llama, Mistral, and FLUX proliferated, there was a massive opportunity to provide optimized infrastructure for running and customizing them. Together AI built a multi-cloud GPU platform with custom inference kernels and distributed training optimizations specifically engineered for open-source models.\n\nTogether AI's platform offers fine-tuning, inference, and training services across a curated library of leading open-weight models, with performance-optimized endpoints that often outperform what users can achieve running models on general-purpose cloud infrastructure. The company targets AI engineers, ML researchers, and enterprises that want flexibility—either for cost reasons, privacy requirements, or the need to customize model behavior through fine-tuning. Together's API design closely mirrors OpenAI's, making migration straightforward. Its pricing is consistently below proprietary model APIs for comparable capability tiers.\n\nTogether AI has achieved $300M in annualized revenue as of September 2025, growing to a $3.3B valuation with $533M in total funding. Investors include NVIDIA, Salesforce, and Kleiner Perkins—a combination that provides both strategic GPU supply chain relationships and enterprise go-to-market leverage. The open-source AI cloud market is a significant and growing segment as enterprises prioritize model flexibility and cost control alongside the maturation of open-weight models that increasingly compete with frontier proprietary models.
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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