Your architecture shapes delivery speed, reliability, and cost. Request-driven designs are simple and predictable, while event-driven designs are flexible and scalable. The right choice depends on your product, team, and risk profile.

What request-driven does well

A client calls a service and waits for a response. This is simple, synchronous, and easy to reason about.

• Great for APIs, dashboards, and user actions that need instant answers
• Easy debugging and tracing with a single call path
• Straightforward timeouts, retries, and error handling

Where request-driven strains

• Long-running tasks tie up resources or hit timeouts
• Cross-service chains become fragile and slow
• Spikes cause contention that hurts tail latency

What event-driven does well

A producer publishes an event, and consumers react asynchronously. Loose coupling and natural scaling.

• Ideal for workflows like order processing, billing, notifications, analytics
• Each consumer scales independently and fails without taking others down
• Natural audit trail via immutable event logs

Where event-driven strains

• Harder to debug without good observability
• Event schemas and versioning require discipline
• Eventual consistency demands careful UX and idempotency
• More moving parts to secure and operate

A simple chooser

• Pick request driven when the user waits, and correctness is immediate. Examples: authentication, search results, pricing checks.
• Pick event-driven when work can run in the background. Examples: sending emails, generating reports, settlements, and data ingestion.

Hybrid patterns you can ship now

• Synchronous front, async back: return fast, enqueue work, update status later
• Outbox pattern: write data and event together to avoid lost messages
• Saga pattern: orchestrate multi-step workflows with compensating actions
• CQRS light: keep a read model updated by events for fast queries

Data and consistency guidelines

• Design events as facts, not commands
• Version event schemas and keep them stable
• Use idempotent consumers with deduplication keys
• Make user facing status clear if the work is still processing

Observability and reliability

• Correlate request IDs with event IDs
• Emit structured logs and metrics for publish, consume, fail, and retry
• Set sane DLQ rules and alert on age and size, not just count
• Run game days that simulate consumer failure and backlog recovery

Security and cost

• Protect brokers like any critical datastore
• Scope credentials by topic or stream
• Track per-event cost and backlog storage to avoid surprises
• Right size partitions and retention based on throughput and recovery needs

Migration tips without the rewrite

• Start by eventing audit friendly domains like analytics and notifications
• Introduce an outbox table to publish changes safely
• Move one long-running job out of the sync path first
• Add a lightweight status endpoint so clients do not poll your core API

You do not need to pick a single style forever. Use request-driven paths for fast answers and event-driven flows for background work and scale. If you want an architecture plan that matches your roadmap without overcomplicating delivery, ping us at Code Scientists.