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

The text generation inference open source project by huggingface looked like a promising framework for serving large language models (LLM). However, huggingface announced that they will change the license of code with version v1.0.0. While the previous license Apache 2.0 was permissive, the new one is restrictive for our use cases.
We decided to fork the project and continue to maintain it under the Apache 2.0 license. We will continue to contribute to the project and keep it up to date. We will accept pull requests from the community, and we will keep the project truly open source and free to use.
Here is a link to the code: https://github.com/deepinfra/text-generation-inference
We hope that in time a community of other developers and organizations that want to keep this project truly open source will form around it.
Sadly it is becoming more and more common for popular open source projects to change their license after they gain some traction. This happened with MongoDB, Grafana, ElasticSearch, and many others. As a developer, when you decide to adopt a particular open source project, you start investing time and effort into using it. You build your application around it, and you start depending on it. Then, suddenly, the license changes, and you might be forced to find an alternative.
Imagine if meta changes the license of pytorch. Or if tomorrow huggingface decides to change the license of transformers in a similar way to prohibit commercial use.
We believe that the changing of the license of open source projects mid-flight is a unfriendly move towards the community.
If you need any help, just reach out to us on our Discord server.
Nemotron 3 Nano Explained: NVIDIA’s Efficient Small LLM and Why It Matters<p>The open-source LLM space has exploded with models competing across size, efficiency, and reasoning capability. But while frontier models dominate headlines with enormous parameter counts, a different category has quietly become essential for real-world deployment: small yet high-performance models optimized for edge devices, private on-prem systems, and cost-sensitive applications. NVIDIA’s Nemotron family brings together open […]</p>
Pricing 101: Token Math & Cost-Per-Completion Explained<p>LLM pricing can feel opaque until you translate it into a few simple numbers: input tokens, output tokens, and price per million. Every request you send—system prompt, chat history, RAG context, tool-call JSON—counts as input; everything the model writes back counts as output. Once you know those two counts, the cost of a completion is […]</p>
GLM-5 API Benchmarks: Latency, Throughput & Cost<p>GLM-5 is the latest open-weights reasoning model released by Z AI (Zhipu AI) in February 2026, characterized by high “thinking token” usage. It is a Mixture of Experts (MoE) model with 744B total parameters and 40B active parameters, scaling up from GLM-4.5’s 355B parameters. The model was pre-trained on 28.5T tokens and features a 200K+ […]</p>
© 2026 DeepInfra. All rights reserved.