Klu raises $1.7M to empower AI Teams  

LLM Cloud API Dependencies

by Stephen M. Walker II, Co-Founder / CEO

What is LLM Cloud API dependencies risk?

LLM (Large Language Model) Cloud API dependencies refer to the various software components that a cloud-based LLM relies on to function correctly. These dependencies can include libraries, frameworks, and other software modules that the LLM uses to perform its tasks. They are crucial for the operation of the LLM, but they can also introduce potential vulnerabilities if not managed properly.

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. A study found that many projects on GitHub reference an average of 208 direct and transitive dependencies, with some relying on over 500 dependencies.

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, it's crucial to develop proprietary, open-source models. This approach not only positions you to compete effectively but also ensures the economic sustainability of your business. Owning your models secures cost control, reduces reliance on third-party providers, and allows for customization to meet specific product and market needs.

Control Your Destiny with Klu

Transitioning from API dependency to model ownership is achievable with Klu. Our platform supports the ingestion of large volumes of telemetry data, which is both performant and secure. Klu also facilitates top-down dataset curation, enabling you to filter and refine your data for targeted model training.

Embracing technology ownership is vital in the dynamic AI landscape. Klu equips you with the necessary tools and resources for strategic foresight and adaptability.

Conclusion

Adopting open-source models is not merely advantageous—it's a strategic imperative. Partnering with Klu empowers your enterprise to innovate and expand without the constraints of external dependencies. Take charge of your AI journey with Klu, ensuring you proceed with confidence, integrity, and a vision for the future.

More terms

Uunsupervised Learning

Unsupervised learning is a machine learning approach where models are trained using data that is neither classified nor labeled. This method allows the model to act on the data without guidance, discovering hidden structures within unlabeled datasets.

Read more

What is IBM Deep Blue?

IBM Deep Blue was a chess-playing expert system run on a unique purpose-built IBM supercomputer. It was the first computer to win a game, and the first to win a match, against a reigning world champion under regular time controls. The development of Deep Blue began in 1985 at Carnegie Mellon University under the name ChipTest. It then moved to IBM, where it was first renamed Deep Thought, then again in 1989 to Deep Blue.

Read more

It's time to build

Collaborate with your team on reliable Generative AI features.
Want expert guidance? Book a 1:1 onboarding session from your dashboard.

Start for free