What is LLM Cloud API dependencies risk?
Cloud API dependency risk is the operational and security exposure created when an LLM application relies on provider APIs, libraries, frameworks, credentials, and deployment services.
For instance, open-source packages with LLM capabilities often have many dependencies that make calls to security-sensitive APIs. If these dependencies are compromised, they could affect the security of the LLM.
The dependencies of an LLM can also impact the ease of switching between different LLMs. Different LLMs often require different prompting strategies, and changing the API endpoint is often the easy part. The challenge lies in getting one LLM to behave similarly to another.
When deploying an LLM in a cloud environment, such as Google Cloud, there are several dependencies to consider. These can include various Google Cloud and Vertex AI APIs, as well as local authentication credentials for your Google Account. The deployment process may also involve packaging the model using a tool like Truss, containerizing it using Docker, and deploying it in Google Cloud using Kubernetes.
In addition, there are tools like OpenLLM that allow you to run inference on any open-source LLM, deploy them on the cloud or on-premises, and build powerful AI applications. Platforms like Anyscale also provide infrastructure to power the entire lifecycle of LLM application development and deployment, handling the complexities of the underlying infrastructure.
The Dependency Dilemma and Building Your Models
Reliance on generic cloud API models from providers like OpenAI and Cohere is common for companies eager to integrate LLM features swiftly. However, this dependency carries significant risks. Drawing parallels to the early days of social media marketing, businesses initially benefited from low-cost ads but later faced increased prices as platforms sought higher profits. Similarly, companies solely dependent on API models could experience deteriorating unit economics as providers adjust pricing to reflect their growing leverage.
To mitigate these risks, some organizations choose to develop or fine-tune proprietary, open-source models rather than depend solely on external providers. Owning your models can improve cost control, reduce reliance on third-party providers, and allow for customization to meet specific product and market needs, though it also introduces its own infrastructure and maintenance overhead.