GluePuddingTool

Edge Function Cold Start Estimator

Estimate cold-start/warm-start latency and TTFB for edge functions based on platform, bundle size, and dependency count, with a cross-platform comparison and optimization tips — based on typical public benchmark values.

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How to Use

  1. Pick an edge platform: Cloudflare Workers, Vercel Edge Functions, Deno Deploy, Netlify Edge Functions, AWS Lambda@Edge, or Azure Edge Functions.
  2. Enter function parameters: bundle size (KB), dependency count, lines of initialization code, and whether the function uses WASM.
  3. Click "Estimate Latency" to see four core metrics: cold-start time, warm-start time, estimated TTFB, and the cold/warm ratio.
  4. Review the "Latency Breakdown" to see how much the platform baseline, bundle size, dependencies, init code, and WASM overhead each contribute.
  5. Check the "Cross-Platform Comparison" table to compare cold-start/warm-start/TTFB across all 6 platforms under the same parameters — the currently selected platform is highlighted.
  6. Read the auto-generated optimization suggestions. Click "Load Sample Data" for a quick Vercel Edge example.

Features

  • Covers 6 major edge platforms: Cloudflare Workers, Vercel Edge Functions, Deno Deploy, Netlify Edge Functions, AWS Lambda@Edge, and Azure Edge Functions.
  • Takes 4 inputs — bundle size, dependency count, init code line count, and WASM usage — and runs them through a calculation engine packaged as a standalone logic.js module.
  • Outputs cold-start time, warm-start time, estimated TTFB, and the cold/warm ratio.
  • Provides a latency breakdown (platform baseline, bundle size, dependency overhead, init code, WASM overhead, network latency range) rendered as a bar chart showing each contributor's share.
  • Includes a side-by-side comparison table across all 6 platforms for the same inputs, with the selected platform highlighted.
  • Auto-generates a list of optimization suggestions tailored to your current inputs.
  • The results panel states explicitly: figures are estimated from typical values in each platform's public benchmarks; actual latency is affected by deployment region, network conditions, and more — this is a reference, not a live probe or load test.

Use Cases

Compare edge platforms before choosing one
Before deciding where to deploy a new project, run the same bundle size and dependency numbers through each platform's comparison row to get a sense of the relative cold-start scale.
Quantify the payoff of trimming dependencies
Suspect a dependency is dragging down cold starts — lower the dependency count and re-estimate to see how much the cold-start time changes, informing whether a dependency cleanup is worth the effort.
Decide whether to add a WASM module
Considering adding a WASM module for image or crypto work inside an edge function — toggle "Uses WASM" to see the estimated cold-start impact and weigh it against the performance benefit.
Explain cold-start latency composition to a team or client
Need to explain why a function's first request is slow — use the latency breakdown to visually show how much bundle size, dependencies, and init code each contribute, helping prioritize optimization work.

FAQ

Does this tool actually measure my edge function's cold start?
No. It doesn't deploy or invoke any real function, and it makes no network requests to measure timing. Every number is computed from typical values in public platform benchmarks combined with your inputs — the page states this explicitly.
Why do the same inputs produce very different estimates across platforms?
Different edge platforms use fundamentally different runtime isolation (e.g. V8 isolates vs. traditional containers/VMs), so their baseline latency genuinely differs — that's an architectural difference, not estimation error.
Are TTFB and cold-start time the same thing?
Not quite. Cold-start time covers only the function instance's initialization. TTFB (Time To First Byte) builds on top of that and also includes request routing and network transit, so it's usually somewhat higher than the pure cold-start figure.
Are the optimization suggestions generic text or based on my actual inputs?
They're generated dynamically from your current platform, bundle size, dependency count, and WASM selection — not a fixed block of copy.