Serverless architecture helps teams ship faster while spending less time on servers and scaling. This guide explores Top 10 Serverless Architecture Patterns that help you design reliable, secure, and cost-aware systems. Each pattern explains when to use it, how events flow, and what pitfalls to avoid, so both beginners and advanced engineers can learn. You will see how to combine managed compute, event buses, and data services to solve common problems in integration, analytics, automation, and APIs. Examples stay portable across clouds, with practical tips for governance, testing, and observability. Use these patterns to sketch solutions, align stakeholders, and build applications that scale under traffic.
#1 API gateway with function-per-endpoint
Expose a secure REST or HTTP API through a managed gateway, then route each method to a small, single-purpose function. This pattern speeds delivery by letting teams deploy endpoints independently while the gateway centralizes authentication, throttling, request validation, and caching. Use it for CRUD services, webhooks, and mobile backends that need predictable latency and clear routes. Keep functions stateless and idempotent, reuse shared layers for clients, and limit cold starts with provisioned concurrency where it matters. Monitor with logs and traces from gateway to function to datastore. Add canary releases at the gateway to de-risk changes and roll back fast.
#2 Queue based asynchronous decoupling
Place a durable queue or task service between producers and consumers, allowing bursts to smooth out and failures to retry safely. Producers publish messages without waiting, while functions consume at a controlled rate that matches downstream limits. Use dead letter queues to capture poison messages and alarms to detect backlog growth. Design idempotent handlers that check for duplicates using message keys and conditional writes. Adopt small message payloads with object storage links for large data. Tune visibility timeouts to processing time, and scale concurrency with reserved limits to protect databases and third party APIs.
#3 Event bus fan out with pub sub
Publish domain events to a managed event bus and let multiple subscribers process them independently. A single order placed event can trigger inventory updates, billing, email notifications, and analytics pipelines without tight coupling. Schema versioning and contracts keep producers and consumers independent, while filter rules target only relevant events. Prefer immutable event payloads and include correlation identifiers for tracing and audit. Use idempotent consumers, per subscriber retries, and dead letter queues to prevent cascading failures. Document event names and ownership clearly, and add replay procedures so teams can recover from outages or onboarding delays.
#4 Workflow orchestration with state machines
Use a managed workflow engine to coordinate long running, multi step business processes such as onboarding, claims handling, or media processing. Each step calls a function or service and reports success, failure, or timeout, while the engine persists state and handles retries and backoff. You gain visual models, audit trails, and compensation paths without writing undifferentiated glue code. Split tasks into short steps to reduce cold start impact and improve visibility. Prefer service integrations where possible to cut cost and latency. Add circuit breakers, timeouts, and alarms per step, and capture input and output schemas for safety and evolution.
#5 Saga pattern for distributed consistency
When a business transaction spans multiple services, coordinate it as a sequence of local commits with compensating actions if steps fail. Implement sagas using the workflow engine or through events that trigger compensations on failure. Design each step to be reversible, such as releasing inventory, refunding payments, or clearing reservations. Store a saga record that tracks correlation identifiers, step status, and deadlines for observability. Prefer idempotent compensations and limit the window where partial states are visible. Define timeouts and alerts to detect stuck sagas, and simulate failure scenarios during tests to validate recovery paths.
#6 CQRS with dedicated read and write paths
Separate the write model from the read model so each can scale and evolve independently. Writes validate and persist commands using a strongly consistent store, while events project materialized views into one or more read optimized databases or caches. Functions perform projections asynchronously, keeping queries fast under load. Use deterministic projection code and include version numbers to safely rebuild views. Adopt access patterns that minimize cross partition queries, and prefer eventual consistency for dashboards and feeds. Provide read repair or on demand recompute for stale views, and expose consumer lag metrics so teams understand freshness trade offs.
#7 Real time stream processing
Ingest clickstreams, device telemetry, or logs into a managed stream and process records with serverless workers. Use windowed aggregations for metrics and anomaly detection, batch writes to storage, and checkpoint progress so processing resumes after failures. Shard by keys that balance traffic, and choose at least once or exactly once delivery depending on business needs. Keep per record work small to control latency, and move heavy enrichment to lookup tables or caches. Expose derived data through APIs, dashboards, or alerts. Plan for backpressure by limiting concurrency and enabling retries with incremental delay to smooth spikes.
#8 Backend for frontend with aggregation
Create a thin backend tailored to a specific client that aggregates calls to internal services and shapes responses for that experience. Implement it with functions behind an API gateway, optionally using GraphQL for flexible queries. You reduce chatty calls from mobile or web clients and keep presentation concerns out of core domains. Add caching for stable fragments, apply schema validation at the edge, and enforce authentication with token exchange. Guard downstream services with rate limits and timeouts per client. Version the contract so clients can upgrade smoothly, and log correlation identifiers to trace a request end to end.
#9 Scheduled batch and serverless ETL
Run time based jobs to collect data, transform it, and load it into durable storage or analytics warehouses without persistent servers. Trigger functions on schedules or data arrivals, and chain steps through events or workflows. Use temporary object storage for intermediate files, and stream results when volumes are large. Package transformation logic in layers for reuse, and validate inputs with schemas to prevent bad data from spreading. Track lineage by including source identifiers and timestamps in outputs. Add retries with backoff, cap concurrency to respect rate limits, and publish metrics on success counts, duration, and error classes.
#10 Edge compute and event driven caching
Move lightweight logic to edge locations close to users to personalize responses, route traffic, and cache dynamic fragments. Use functions at the edge to rewrite requests, authorize sessions, and precompute content that varies by locale, device, or user segment. Keep execution time small and avoid heavy dependencies to control latency. Forward only required headers and cookies to maintain high cache hit ratios. Encrypt secrets with managed keys and rotate regularly. Measure outcomes with real user metrics and synthetic tests, and fall back to origin safely when the edge platform encounters failures or capacity limits.