Embeddings can work in serverless environments by utilizing cloud functions (e.g., AWS Lambda, Google Cloud Functions, or Azure Functions) to handle embedding generation and inference without the need to manage servers. In a serverless setup, embeddings are typically generated on-demand when a request is made, and the results are returned quickly, making it ideal for applications with variable workloads or infrequent embedding generation needs.
The serverless model offers automatic scaling, meaning that the system can handle a large number of requests for embeddings without the need for manual intervention. For example, a recommendation system could generate embeddings for users in real time based on their interactions with a web application, scaling automatically to handle spikes in traffic. The generated embeddings can then be stored in cloud storage or a vector database for fast retrieval.
However, serverless environments can have some latency concerns, especially when embeddings require extensive computation. To mitigate this, embeddings can be precomputed and stored in cache or databases to speed up retrieval. Additionally, serverless platforms often have limitations in execution time and memory, so it’s important to design embedding generation processes that are lightweight and efficient in these environments.