Head-to-head comparison
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Decision brief
AWS Lambda vs Cloudflare Workers
Use this page when you already have two candidates. It focuses on the constraints and pricing mechanics that decide fit—not a feature checklist.
Verified — we link the primary references used in “Sources & verification” below.
- Why compared: This is a high-intent comparison contrasting regional serverless with edge-first compute
- Real trade-off: Regional serverless ecosystem depth vs edge-first latency model and request-path execution
- Common mistake: Choosing based on “faster/cheaper” claims instead of mapping your workload to execution model constraints (latency, limits, state, cost drivers)
At-a-glance comparison
AWS Lambda ↗
Regional serverless compute with deep AWS event integrations, commonly used as the default baseline for event-driven workloads on AWS.
See pricing details
- ✓ Deep AWS ecosystem integrations for triggers and event routing
- ✓ Mature operational tooling for enterprise AWS environments
- ✓ Strong fit for event-driven backends (queues, events, storage triggers)
Cloudflare Workers ↗
Edge-first serverless runtime optimized for low-latency request/response compute near users, commonly used for middleware and edge API logic.
See pricing details
- ✓ Edge execution model improves user-perceived latency globally
- ✓ Strong fit for request-path compute (middleware, routing, personalization)
- ✓ Reduces regional hop latency for globally distributed users
Where each product pulls ahead
These are the distinctive advantages that matter most in this comparison.
AWS Lambda advantages
- ✓ Deep AWS event ecosystem and integrations
- ✓ Familiar regional cloud model for enterprises
- ✓ Strong baseline for event-driven architectures
Cloudflare Workers advantages
- ✓ Edge-first latency model for global request paths
- ✓ Great fit for middleware and request-path compute
- ✓ Global distribution with constraints designed for edge usage
Pros & Cons
AWS Lambda
Pros
- + You need deep AWS-native triggers and integrations
- + Your compute is event-driven and background-heavy
- + You prefer regional cloud operational patterns and IAM governance
- + You can model retries/idempotency and trace distributed failures
Cons
- − Regional execution adds latency for global request-path workloads
- − Cold starts and concurrency behavior can become visible under burst traffic
- − Cost mechanics can surprise teams as traffic becomes steady-state or egress-heavy
- − Operational ownership shifts to distributed tracing, retries, and idempotency
- − Lock-in grows as you rely on AWS-native triggers and surrounding services
Cloudflare Workers
Pros
- + User-perceived latency is a primary KPI
- + Your compute is on the request path (middleware, personalization, routing)
- + You can design within edge constraints and state patterns
- + You want global execution by default
Cons
- − Edge constraints can limit heavy dependencies and certain runtime patterns
- − Stateful workflows require deliberate patterns (KV/queues/durable state choices)
- − Not a drop-in replacement for hyperscaler event ecosystems
- − Operational debugging requires solid tracing/log conventions
- − Platform-specific patterns can increase lock-in at the edge
Which one tends to fit which buyer?
These are conditional guidelines only — not rankings. Your specific situation determines fit.
AWS Lambda
Pick this if
Best-fit triggers (scan and match your situation)
- ✓ You need deep AWS-native triggers and integrations
- ✓ Your compute is event-driven and background-heavy
- ✓ You prefer regional cloud operational patterns and IAM governance
- ✓ You can model retries/idempotency and trace distributed failures
Cloudflare Workers
Pick this if
Best-fit triggers (scan and match your situation)
- ✓ User-perceived latency is a primary KPI
- ✓ Your compute is on the request path (middleware, personalization, routing)
- ✓ You can design within edge constraints and state patterns
- ✓ You want global execution by default
Quick checks (what decides it)
Use these to validate the choice under real traffic
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Metrics that decide itFor request-path compute, test p95/p99 globally and measure origin-call ratio; for event compute, test peak throughput + retries + DLQ visibility. Cold-start delta matters any time users wait on the result.
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Architecture checkIf you need heavy dependencies or long-running compute, edge constraints can be the blocker; if you need complex event topology, platform web functions can be the blocker.
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Cost checkModel requests + duration + bandwidth/egress under real load and identify the first cost cliff—then pick the model you can live with.
Sources & verification
We prefer to link primary references (official pricing, documentation, and public product pages). If links are missing, treat this as a seeded brief until verification is completed.