Learn how Sedai uniquely optimizes AWS serverless functions to achieve your performance and cost goals.
Sedai autonomously optimizes two key aspects of AWS Lambdas:
- Memory Allocation: Identifies optimal configuration based on your performance and/or cost goals.
- Autonomous Concurrency: Eliminates or reduces cold starts with marginal cost impact.
Sedai autonomously eliminates cold starts with little-to-no impact on cloud costs. Using a combination of the following, Sedai informs its central control and learning unit to manage continuous autonomous concurrency:
- Sedai AV Lambda Extension: External extension written in Go; analyzes Lambda behavior such as runtime events, cold start duration, and concurrency patterns (this allows close Lambda monitoring without any overhead).
- Traffic Seasonality: Monitors and builds a seasonality model in order to understand local and global trends and anticipate traffic fluctuations.
- Provisioned Concurrency: Monitors concurrency events as well as concurrency scaling based on seasonality.
- NodeJS 12 and above on
- Python 3.7 and above on
- Java 8 and above on
The static and runtime characteristics of the extension are limited to:
- Additional 12 MB size in deployed Lambda layer
- 0.1 ms latency overhead per 1,000 invocations
- 0.11 ms execution time overhead per 1,000 invocations
You can deploy the AV Lambda extension manually or via your preferred IaC tool: