Mistral in the cloud: Shared idea with different faces
As models such as Mistral Small and Mistral Nemo make their way into real-world applications, many users wonder whether they need to set up servers to use them. The answer is no. Both AWS Bedrock and Google Vertex AI now offer Mistral models as part of their hosted services, making it easier than ever to plug these powerful tools into your product or workflow—without ever touching a GPU.
While the cloud platforms differ in look and tooling, the core concept remains the same: you send a prompt, the cloud runs the model, and you get the result—all in a matter of milliseconds. Whether you’re building a chatbot, a summarizer, or a search assistant, the cloud takes care of scaling, security, and speed so you can focus on what really matters.
This approach allows developers and businesses to experiment faster, launch prototypes sooner, and scale up on demand, while relying on the robustness of the underlying infrastructure.
Figure 1.7: Mistral deployed via AWS or Google Cloud, returning a response to the user
The diagram shows two side-by-side pipelines: one for AWS Bedrock, the other for Google Vertex AI. Both start with a user input on the left, flow through respective cloud services in the center, and converge on the Mistral model. Arrows continue back to the user, delivering results. Visual icons include cloud platforms, gear symbols for processing, and a chatbot icon on the return path.
We’re now approaching the end of our purely theoretical exploration of LLMs. From understanding their strengths to exploring use cases and deployment paths, this chapter has laid a strong foundation. In the next chapter, we shift into practical mode—spinning up your own AI chatbot, hands-on.
