Session description
GraphQL simplifies client development through flexible, expressive data queries. However, in serverless production environments, that flexibility can quietly increase latency and infrastructure cost.
In AWS-based architectures using Lambda, DynamoDB, Aurora Serverless, and distributed services, resolver design and query structure directly impact execution time, cold starts, and overall spend. Unlike REST, GraphQL shifts cost dynamics to query complexity and resolver fan-out, and often in ways teams don’t anticipate until production traffic scales.
This session examines common performance and cost anti-patterns in serverless GraphQL systems, including N+1 resolver cascades, unbounded query depth, over-fetching, and inefficient resolver fan-out. We’ll explore how these patterns affect Lambda duration, concurrency, and downstream data stores.
Attendees will learn practical mitigation strategies such as batching with DataLoader, caching and persisted queries, query complexity limits, schema guardrails, and observability techniques to detect bottlenecks early.
