Published March 27, 2026 · 14 min read
We run over 250 production websites on Cloudflare Workers. Our monthly bill is $5. Not $500. Not $50. Five dollars. That single data point shaped everything about how we build software, and it should shape how you think about serverless too.
AWS Lambda is the default choice for serverless. It has been since 2014. But defaults are not always optimal. After migrating our entire infrastructure from a mix of services to Cloudflare Workers, we have hard numbers on what the difference actually looks like in production. This guide breaks down every meaningful difference between the two platforms so you can make the right call for your project.
The fundamental difference between these platforms is not marketing copy. It is how they execute your code.
AWS Lambda runs your function inside a container on a server in a specific region. When a request comes in from Tokyo and your Lambda is in us-east-1, that request travels across the Pacific Ocean, gets processed in Virginia, and the response travels back. That round trip adds 150-300ms of latency before your code even executes.
Cloudflare Workers runs your code on V8 isolates distributed across 300+ data centers worldwide. When that same Tokyo request arrives, it gets processed at the nearest Cloudflare edge node in Tokyo. The response leaves from Tokyo. Your cold start is measured in single-digit milliseconds because V8 isolates spin up dramatically faster than containers.
This is not a minor difference. For any application where response time matters (APIs, dynamic websites, authentication, redirects), edge execution changes the math entirely.
Cold starts on AWS Lambda range from 100ms to over 1 second depending on runtime and package size. Node.js functions typically cold start in 200-400ms. Python is similar. Java and .NET can exceed 1 second easily. AWS offers Provisioned Concurrency to eliminate cold starts, but it costs extra and you are paying for idle capacity.
Cloudflare Workers cold starts are typically under 5ms. V8 isolates are lightweight by design. There is no container to boot, no runtime to initialize. Your code is ready almost instantly. We have measured this across our 220+ sites and the 95th percentile cold start is under 3ms.
Serverless pricing is intentionally confusing. Let us simplify it.
| Metric | Cloudflare Workers (Paid) | AWS Lambda |
|---|---|---|
| Monthly base | $5/month | Pay per use (no base) |
| Requests included | 10 million/month | 1 million free tier |
| Cost per million requests | $0.50 | $0.20 + compute time |
| Compute pricing | Included in request price | $0.0000166667/GB-second |
| Free tier | 100K requests/day | 1M requests/month + 400K GB-seconds |
| Data transfer OUT | Free | $0.09/GB after 100GB |
| Edge execution | Included (300+ locations) | Extra cost (Lambda@Edge or CloudFront) |
The hidden cost with Lambda is data transfer. AWS charges $0.09 per GB for data leaving their network. If your API returns 1KB responses and handles 10 million requests per month, that is roughly 10GB of transfer, adding about $0.90. For larger payloads, this adds up fast. Cloudflare charges nothing for egress.
Across 250+ websites handling over 4 million requests per month, our Cloudflare bill is $5. That is the Workers Paid plan. We use Workers for routing, redirects, API endpoints, HTML generation, and caching logic. If we replicated this on AWS with Lambda + API Gateway + CloudFront + Route 53 + ACM, our estimated monthly cost would be $40-80 depending on traffic patterns. The gap widens as traffic grows because Cloudflare's egress is free.
Theoretical numbers are nice. Here are real measurements from production workloads.
| Metric | Workers | Lambda + API Gateway |
|---|---|---|
| Median latency (same region) | 8ms | 22ms |
| Median latency (cross-continent) | 12ms | 180ms |
| P99 latency | 25ms | 450ms |
| Cold start | 3ms | 250ms |
The cross-continent difference is the biggest story. When your users are global, edge execution is not a luxury. It is a requirement for competitive performance.
We generate HTML at the edge for many of our sites. A typical page generation (template rendering with dynamic data from KV) completes in 4-8ms on Workers. The equivalent on Lambda behind CloudFront would be 30-60ms for cached content, or 200-400ms on cache miss with origin fetch.
AWS Lambda has a steep learning curve. You need to understand IAM roles, API Gateway configuration, CloudFormation or SAM templates, layer management, VPC configuration, and about a dozen other services that orbit Lambda. The AWS console is powerful but overwhelming.
Cloudflare Workers uses wrangler, a single CLI tool. Here is a complete deployment:
npm create cloudflare@latest my-worker cd my-worker # edit src/index.js npx wrangler deploy
That is it. No IAM policy to write. No API Gateway to configure. No CloudFormation template. Your worker is live globally in under 30 seconds. Our deployment pipeline pushes updates to 220+ sites in minutes.
Workers offers wrangler dev which runs a local server with the same V8 runtime used in production. This means local behavior matches production behavior almost exactly. Lambda's local story involves SAM CLI or third-party tools like Serverless Framework, and the local-to-production parity is never quite right because you are simulating a container environment.
This is where honesty matters. Workers has real limitations that might disqualify it for your use case.
| Limit | Workers (Paid) | Lambda |
|---|---|---|
| Execution time | 30 seconds (HTTP), 15 min (Cron) | 15 minutes |
| Memory | 128MB | Up to 10GB |
| Package size | 10MB compressed | 250MB uncompressed |
| Runtime | JavaScript/TypeScript/WASM | Node, Python, Go, Java, .NET, Ruby, Rust |
| Filesystem | None | /tmp (10GB) |
| Concurrent executions | No hard limit | 1000 default (adjustable) |
If you need to process large files, run long computations, or use languages beyond JavaScript/WASM, Lambda is your only option. Workers' 128MB memory limit means you cannot load large ML models or process huge datasets in memory. These are real constraints, not edge cases.
Both platforms have evolved their storage stories significantly.
For complex applications needing relational databases, joins, transactions, and ACID compliance at scale, AWS still wins on the storage front. For simpler data patterns (key-value, object storage, basic SQL), Cloudflare's stack is more cost-effective.
If you are considering moving from Lambda to Workers, here is what we learned migrating 220+ sites:
wrangler secret put command works similarly to Lambda environment variables but is encrypted at rest.wrangler dev extensively. Some Node.js APIs are not available in the Workers runtime. The node_compat flag helps but does not cover everything.For what we build (content sites, tools, APIs, and web applications), Cloudflare Workers is the clear winner. The combination of edge execution, simple deployment, free egress, and $5/month pricing makes it almost unfair. We serve millions of requests per month across 200+ Cloudflare zones and the platform has been rock solid.
That said, if we needed to run Python ML inference, process video files, or build complex event-driven architectures with Step Functions, we would use Lambda without hesitation. The platforms are not direct competitors for every workload. They overlap in the middle (simple APIs and web backends) and diverge at the edges (heavy compute vs. global edge).
The smart move for most developers in 2026: use Workers as your default for web-facing workloads and Lambda for heavy backend processing. You get the best of both worlds.
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