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

Many users requested longer context models to help them summarize bigger chunks of text or write novels with ease.
We're proud to announce our long context model selection that will grow bigger in the comming weeks.
Mistral-based models have a context size of 32k, and amazon recently released a model fine-tuned specifically on longer contexts.
We also recently released the highly praised Yi models. Keep in mind they don't support chat, just the old-school text completion (new models are in the works):
Fine-Tuning vs RAG vs Prompting: 2026 Guide<p>When an AI system yields unreliable answers, the root cause could be an unclear system prompt, missing context, poor retrieval quality, or simply using the wrong base model. Teams end up spending weeks experimenting with prompt changes, retrieval-augmented generation (RAG), or fine-tuning to improve response quality. But before deciding which technique to adopt, it is […]</p>
Introducing the Batch API: Run Large Inference Jobs 20% CheaperDeepInfra's new Batch API lets you submit large volumes of completions, chat, and embedding requests as a single asynchronous job—processed within 24 hours at 20% off real-time pricing. It's fully OpenAI-compatible, so if you've used OpenAI's Batch API, you already know how it works.
vLLM vs SGLang: Performance, Features & Deployment Compared<p>Somebody on your team read a benchmark post, and now there’s a ticket to migrate the inference stack. That’s how most vLLM vs SGLang decisions start. A published test reports a 29 percent throughput gap, the number lands in Slack, and two weeks later you’re debugging kernel version conflicts at midnight while p99 latency sits […]</p>
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