DeepInfra raises $107M Series B to scale the inference cloud — read the announcement

Note: The list of supported base models is listed on the same page. If you need a base model that is not listed, please contact us at feedback@deepinfra.com
Rate limit will apply on combined traffic of all LoRA adapter models with the same base model. For example, if you have 2 LoRA adapter models with the same base model, and have rate limit of 200. Those 2 LoRA adapter models combined will have rate limit of 200.
Pricing is 50% higher than base model.
LoRA adapter model speed is lower than base model, because there is additional compute and memory overhead to apply the LoRA adapter. From our benchmarks, the LoRA adapter model speed is about 50-60% slower than base model.
You could merge the LoRA adapter with the base model to reduce the overhead. And use custom deployment, the speed will be close to the base model.
DeepInfra Raises $107M Series B to Scale Inference InfrastructureDeepInfra has raised $107 million in Series B funding to scale its inference cloud, expand global capacity, and support the next generation of open-source and agentic AI workloads.
Inference Economics: True AI Costs at Scale<p>Most teams discover their inference economics the same way: a production bill arrives that looks nothing like the number they expected. The per-token price seemed small enough during testing. Then real traffic showed up, agents started chaining calls, RAG pipelines bloated the context window, and suddenly the math looked completely different. Token prices have fallen […]</p>
MiMo-V2.5 Provider Pricing and Deployment Guide<p>MiMo-V2.5 is worth paying attention to because it puts three things developers usually have to trade off into the same conversation: open weights, a 1 million-token model design, and pricing that can be unusually low depending on where you buy it. On Xiaomi’s first-party API, Artificial Analysis lists MiMo-V2.5 at $0.14 per 1M input tokens […]</p>
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