The clearest answer is release-specific: DeepSeek says it open-sourced R1 to support research. For DeepInfer, its inference framework, DeepSeek’s stated goal—as reported by Gizmodo from company posts on X—was to help lower compute costs, reduce energy use, and make AI more affordable. These reasons concern particular releases; they do not establish one official motive for every DeepSeek model. (DeepSeek-R1 repository; Gizmodo)
That distinction matters if you’re choosing a model for production: open model weights, open-source inference software, and hosted API access are different things. One does not automatically include the others.
What does “DeepSeek is open-source” mean?
It depends on which release you mean. A model’s weights are the learned parameters used to generate outputs. An inference framework is software for running a model. A hosted API lets you send requests to a provider’s service; it is not the same as downloading and running the model yourself.
For R1, DeepSeek’s official repository says the code repository and model weights are licensed under MIT and that the R1 series supports commercial use, modifications, and derivative works. For DeepInfer, the official repository identifies it as an open-source inference framework, and its license file specifies Apache 2.0. Those terms apply to the artifacts identified by those repositories; check the specific model, tool, and version you plan to use rather than assuming every DeepSeek release has identical materials or licensing.
What reasons has DeepSeek given?
DeepSeek’s R1 repository says it released R1 and related models “to support the research community.” Its earlier releases are not needed to infer a universal policy: the available statement supports a research rationale for the R1 release in particular. (DeepSeek-R1 repository)
For DeepInfer, Gizmodo attributes a more operational explanation to DeepSeek: sharing its production stack would help the community lower compute costs, reduce energy use, and make AI more affordable. This is a reported statement about DeepInfer, not a general explanation of DeepSeek’s model-release policy. DeepSeek describes DeepInfer in its repository as an inference framework for its large-scale production services; the affordability rationale above is reported by Gizmodo, rather than established by that repository. (Gizmodo)
Publishing model weights or serving software can let outside teams study, adapt, or run those artifacts. Those are potential effects of openness, not motives to attribute to DeepSeek without a source that directly supports the claim.
What does openness change for a production model or API choice?
Choose based on what your team needs to operate:
- You want the simplest route to test or integrate a model: Consider a hosted API. DeepSeek’s API documentation lists model identifiers and request formats. API access is a service choice; it does not mean you are self-hosting the model.
- You want to run or adapt released weights yourself: Check that the specific weights and license permit your intended use, then assess the compute, deployment, and maintenance your team will need. Open weights may offer more control, but do not guarantee lower total cost or easier operation.
- You are evaluating DeepInfer: Treat it as an inference framework, not as a hosted API. DeepSeek describes it as providing offline inference; check the repository’s current model support, version, hardware requirements, and surrounding serving components against your own production needs. (DeepInfer repository)
For API budgeting, consult DeepSeek’s current pricing page. It lists prices per million tokens and distinguishes peak and off-peak rates; prices vary by model and input/output category, and the page says they may change. These API charges are separate from the cost of operating downloaded weights and infrastructure.
Bottom line: DeepSeek has connected the R1 release with research support, and its DeepInfer release with affordability and efficiency goals, as reported by a third party. The evidence does not establish a single reason for every release. For a production decision, verify the exact artifact and license, then choose between hosted API access and self-operated weights or software based on your operational needs.
Sources
DeepSeek-R1 — official GitHub repository
DeepInfer — official GitHub repository
DeepInfer — Apache License 2.0
DeepSeek Dials Up the Pressure on OpenAI and Anthropic With Open-Source Inference Tool — Gizmodo