When Should You Use Edge Compute vs Centralized Services and What Are the Cost and Complexity Trade Offs?

Edge compute reduces latency for dynamic logic by executing near users, eliminating round trips to centralized regions. For example, JWT token validation at the edge takes under 10 ms versus 50 to 150 ms for a round trip to a central authentication service. Similarly, personalized redirects or A/B test bucketing complete in single digit milliseconds at edge rather than adding 100+ ms central service calls. This latency reduction improves user experience metrics and can directly increase conversion rates. Additionally, edge compute reduces backbone transit costs and central region load by handling requests at the edge without forwarding them to origin services. Use cases that excel include stateless transformations (header rewrites, URL normalization), authorization and authentication (token validation, signed URL checks), content personalization (serving different assets based on user attributes), and lightweight API composition (aggregating responses from multiple backends cached at edge). However, edge compute imposes significant constraints. Execution environments limit CPU time (typically under 50 ms), memory (128 to 512 megabytes), code size (few megabytes), and network access. Functions must remain stateless or rely on edge key value stores. Accessing traditional databases from edge functions incurs cross region latency that negates edge benefits. For example, querying a central PostgreSQL database from an edge function adds 50 to 150 ms, destroying the latency advantage. Edge also lacks the rich tooling, libraries, and debugging environments of centralized platforms. Vendor lock in risk is high because edge runtimes often use proprietary APIs or constrained language subsets. Cold starts on container based edge compute (hundreds of milliseconds) make them unsuitable for latency sensitive paths unless you pre warm capacity. Centralized compute offers richer ecosystems, easier stateful interactions, fuller observability, and simpler development workflows at the cost of higher user latency and backbone costs. The decision hinges on whether latency justifies the constraints. For latency insensitive workloads (asynchronous processing, batch jobs, complex transactions requiring strong consistency), centralized services are simpler and more powerful. For latency sensitive, stateless logic on the request path, edge compute provides measurable improvements. Hybrid architectures often combine both: edge functions handle fast authentication and routing while central services handle complex business logic and stateful operations. Production deployments carefully monitor edge function execution time, error rates, and cold start frequencies to stay within budget and avoid tail latency spikes that degrade user experience.

An e-commerce platform runs JWT token validation at edge in under 5 ms per request, rejecting invalid tokens without central service calls. This reduces authentication latency from 80 ms (central service) to 5 ms and decreases central authentication service load by 40%, lowering costs.

A media site uses edge compute for A/B test bucketing. User ID hashing and variant assignment complete in under 2 ms at edge. The assigned variant determines which cached page variation to serve. Centralizing bucketing would add 100 ms round trip, hurting engagement metrics by 3% based on A/B test results.

Amazon Lambda@Edge handles image URL rewrites at edge to serve optimized formats (WebP for Chrome, JPEG for Safari). The logic runs in under 10 ms. However, the same team tried fetching user purchase history from a central database in an edge function and saw 150 ms latency spikes, so they moved that logic back to central region and pass needed data via edge cache or cookies instead.