Together AI Threatens DeepInfra via Training

Together AI is more dangerous to DeepInfra when an inference customer starts asking for everything around inference too. Both can serve open models through OpenAI compatible endpoints and move a team from shared traffic to dedicated capacity without changing app code, but Together adds fine tuning, training workflows, and self service GPU clusters, which makes it easier to keep a customer as their usage becomes more complex.

• DeepInfra is optimized for a narrow land and expand path, shared serverless API, then private deployments, then raw GPU instances or DeepCluster. Together sells a wider stack from the start, including fine tuning, training, reinforcement learning tooling, and Instant Clusters, so it can capture both the first API call and the later infrastructure budget.
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• The overlap is real at the product surface. DeepInfra lets developers swap an OpenAI SDK to its endpoint and access 190 plus models, while Together offers serverless inference, dedicated reasoning clusters, and 100 plus open models. For a startup just choosing where to run Llama, DeepSeek, or Mistral, the initial buying decision can look almost identical.
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• What separates vendors in practice is less sticker price than operational fit. Inference buyers care about how fast new models show up, whether bursty chat traffic stays responsive, and whether the same vendor can later support custom tuning or reserved capacity. That is why the category is drifting from simple model hosting toward broader AI infrastructure bundles.
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The next step in this market is convergence. Specialist inference clouds are moving down toward owned clusters and up toward training and workflow tooling, because the plain API layer is too easy to compare and too easy to switch. That favors platforms that can start with cheap self serve inference and then absorb more of a customer's ML stack over time.