Embeddings in serverless environments work by combining vector representation techniques with cloud-based, event-driven compute resources. Serverless platforms like AWS Lambda, Google Cloud Functions, or Azure Functions execute code in response to events (e.g., HTTP requests, database updates) without requiring developers to manage servers. When generating embeddings, a serverless function typically loads a pre-trained machine learning model (e.g., BERT, Word2Vec) or uses an API to convert raw data (text, images) into dense vector representations. For example, a Lambda function might process a user-submitted text query, run it through a TensorFlow model to produce a 512-dimensional embedding, and return the result via an API Gateway endpoint. The stateless nature of serverless requires models to be loaded on each invocation or cached in memory for reuse across requests.
Optimizing embeddings in serverless involves balancing latency, memory, and cost. Since serverless platforms impose time limits (e.g., 15 minutes for AWS Lambda) and memory caps (e.g., 10 GB for Azure Functions), models must be lightweight or split into smaller components. For instance, using ONNX Runtime or TensorFlow Lite can reduce model size and inference time. Developers often store precomputed embeddings in serverless databases like DynamoDB or Firestore to avoid redundant processing. To mitigate cold starts—delays when a function initializes—some teams use provisioned concurrency (AWS) or deploy models as separate layers to keep them loaded in memory. For example, a recommendation system might precompute product embeddings and store them in a vector database, then use serverless functions to compare user query embeddings against stored vectors in real time.
Practical use cases include real-time semantic search, chatbots, and personalized content delivery. A news aggregator app could deploy a serverless function that converts article headlines into embeddings, then uses cosine similarity in another function to find related stories. Serverless embeddings also integrate with managed AI services (e.g., OpenAI API, AWS SageMaker) for scalability. For example, a serverless pipeline might process user feedback by sending text to OpenAI’s embeddings API, storing results in BigQuery, and triggering analysis workflows via Pub/Sub. While serverless simplifies scaling and reduces operational overhead, developers must monitor costs (e.g., per-millisecond billing) and ensure models fit within platform constraints. Tools like AWS Lambda’s Arm64 Graviton processors or Google’s Vertex AI integration can further optimize performance for embedding workloads.