Apify Cost Tuning
Overview
Apify charges on three axes: compute units (CU), proxy traffic (GB), and storage.
One CU = 1 GB of memory running for 1 hour, so cost scales with both memory
allocation and run duration. This skill walks the investigate → tune → guard loop
that finds where spend is going, cuts it at the biggest lever (memory), and installs
guardrails so it stays down.
Full pricing tables (plan CU prices, proxy rates, storage rules) live in
pricing-model.md.
Prerequisites
- An Apify account with API access and
APIFY_TOKEN set in the environment.
- The
apify-client package installed (npm install apify-client).
- At least one Actor with run history to analyze.
Instructions
The workflow is six steps. Each is summarized here with its core lever; the full,
runnable code for every step is in
implementation.md.
Analyze current costs — roll up the last N days of runs into total CU, USD, and
duration, and surface the single most expensive run:
import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const { items: runs } = await client.actor(actorId).runs().list({ limit: 1000, desc: true });
const totalUsd = runs.reduce((s, r) => s + (r.usageTotalUsd ?? 0), 0);
Reduce memory allocation (biggest lever) — sweep memory from 4096 MB down to
256 MB and stop at the first failure to find the sweet spot. Most CheerioCrawler
Actors are over-provisioned. Sweet spots: simple Cheerio 256-512 MB, complex
512-1024 MB, Playwright 2048-4096 MB.
Optimize crawl duration — higher maxConcurrency, tighter
requestHandlerTimeoutSecs, a maxRequestsPerCrawl cap, fewer retries, and
selective enqueueLinks. Faster crawls consume fewer CUs.
Minimize proxy costs — prefer datacenter (free with plan), only reach for
residential when a site blocks it, block images/fonts/CSS to save residential GB,
and reuse proxy sessions with useSessionPool.
Cost guard for runaway Actors — start the run, poll usageTotalUsd every 30s,
and .abort() once spend crosses a hard cap.
Monitor monthly usage — iterate every Actor's runs since the 1st of the month
and print a cost-descending report so the top spenders are obvious.
See full walkthrough for the complete code of each
step, including the memory sweep, proxy hooks, budget guard, and monthly report.
Output
Running this skill produces:
- A per-Actor cost analysis (runs, total CU, total USD, avg CU/run, avg cost/run,
most expensive run) for a chosen lookback window.
- A memory profile table mapping memory settings to status, duration, CU, and USD
so you can pick the cheapest allocation that still succeeds.
- A monthly cost report ranking every Actor by spend, with a grand total.
- Tuned Actor configuration (reduced memory, capped crawls, proxy resource blocking)
and an optional budget guard that aborts runs exceeding a USD ceiling.
Cost Optimization Checklist
Error Handling
| Issue |
Cause |
Solution |
| Unexpected cost spike |
No maxRequestsPerCrawl |
Always set an upper bound |
| High residential proxy cost |
Scraping images/fonts |
Block non-essential resources |
| Over-provisioned memory |
Default 1024MB |
Profile and reduce to minimum |
| Too many scheduled runs |
Aggressive cron |
Reduce frequency if data freshness allows |
Examples
Three worked scenarios chain the steps against concrete symptoms — a CheerioCrawler
bill that tripled, runaway residential-proxy GB, and guarding a brand-new Actor. Each
shows the full investigate → tune → verify loop. See
examples.md.
Quick guard example — abort any run that exceeds $0.50:
// runWithBudget polls usageTotalUsd every 30s and aborts past the cap
const run = await runWithBudget('user/scraper', input, 0.50);
Resources
Next Steps
For architecture patterns, see apify-reference-architecture.
Source: jeremylongshore/claude-code-plugins-plus-skills → plugins/saas-packs/apify-pack/skills/apify-cost-tuning/SKILL.md
1---2name: apify-cost-tuning3description: 'Optimize Apify platform costs through memory tuning, compute unit management, and proxy budgeting. Use when analyzing Apify billing, reducing Actor run costs, or implementing usage monitoring and budget alerts. Trigger with "apify cost", "apify billing", "reduce apify costs", "apify pricing", "apify expensive", "apify budget", "compute units".'4---56# Apify Cost Tuning78## Overview910Apify charges on three axes: compute units (CU), proxy traffic (GB), and storage.11One CU = 1 GB of memory running for 1 hour, so cost scales with both memory12allocation and run duration. This skill walks the investigate → tune → guard loop13that finds where spend is going, cuts it at the biggest lever (memory), and installs14guardrails so it stays down.1516Full pricing tables (plan CU prices, proxy rates, storage rules) live in17[pricing-model.md](references/pricing-model.md).1819## Prerequisites2021- An Apify account with API access and `APIFY_TOKEN` set in the environment.22- The `apify-client` package installed (`npm install apify-client`).23- At least one Actor with run history to analyze.2425## Instructions2627The workflow is six steps. Each is summarized here with its core lever; the full,28runnable code for every step is in29[implementation.md](references/implementation.md).30311. **Analyze current costs** — roll up the last N days of runs into total CU, USD, and32 duration, and surface the single most expensive run:3334 ```typescript35 import { ApifyClient } from 'apify-client';36 const client = new ApifyClient({ token: process.env.APIFY_TOKEN });3738 const { items: runs } = await client.actor(actorId).runs().list({ limit: 1000, desc: true });39 const totalUsd = runs.reduce((s, r) => s + (r.usageTotalUsd ?? 0), 0);40 ```41422. **Reduce memory allocation** (biggest lever) — sweep memory from 4096 MB down to43 256 MB and stop at the first failure to find the sweet spot. Most CheerioCrawler44 Actors are over-provisioned. Sweet spots: simple Cheerio 256-512 MB, complex45 512-1024 MB, Playwright 2048-4096 MB.46473. **Optimize crawl duration** — higher `maxConcurrency`, tighter48 `requestHandlerTimeoutSecs`, a `maxRequestsPerCrawl` cap, fewer retries, and49 selective `enqueueLinks`. Faster crawls consume fewer CUs.50514. **Minimize proxy costs** — prefer datacenter (free with plan), only reach for52 residential when a site blocks it, block images/fonts/CSS to save residential GB,53 and reuse proxy sessions with `useSessionPool`.54555. **Cost guard for runaway Actors** — start the run, poll `usageTotalUsd` every 30s,56 and `.abort()` once spend crosses a hard cap.57586. **Monitor monthly usage** — iterate every Actor's runs since the 1st of the month59 and print a cost-descending report so the top spenders are obvious.6061See [full walkthrough](references/implementation.md) for the complete code of each62step, including the memory sweep, proxy hooks, budget guard, and monthly report.6364## Output6566Running this skill produces:6768- A **per-Actor cost analysis** (runs, total CU, total USD, avg CU/run, avg cost/run,69 most expensive run) for a chosen lookback window.70- A **memory profile** table mapping memory settings to status, duration, CU, and USD71 so you can pick the cheapest allocation that still succeeds.72- A **monthly cost report** ranking every Actor by spend, with a grand total.73- Tuned Actor configuration (reduced memory, capped crawls, proxy resource blocking)74 and an optional budget guard that aborts runs exceeding a USD ceiling.7576## Cost Optimization Checklist7778- [ ] Memory profiled (start low: 256-512MB for Cheerio)79- [ ] `maxRequestsPerCrawl` set to prevent runaway crawls80- [ ] Datacenter proxy used when possible (free with plan)81- [ ] Residential proxy: images/CSS/fonts blocked to save bandwidth82- [ ] `maxConcurrency` tuned (higher = faster = fewer CUs)83- [ ] Scheduled runs have appropriate frequency (don't over-scrape)84- [ ] Cost guard implemented for expensive runs85- [ ] Monthly usage reviewed8687## Error Handling8889| Issue | Cause | Solution |90|-------|-------|----------|91| Unexpected cost spike | No `maxRequestsPerCrawl` | Always set an upper bound |92| High residential proxy cost | Scraping images/fonts | Block non-essential resources |93| Over-provisioned memory | Default 1024MB | Profile and reduce to minimum |94| Too many scheduled runs | Aggressive cron | Reduce frequency if data freshness allows |9596## Examples9798Three worked scenarios chain the steps against concrete symptoms — a CheerioCrawler99bill that tripled, runaway residential-proxy GB, and guarding a brand-new Actor. Each100shows the full investigate → tune → verify loop. See101[examples.md](references/examples.md).102103Quick guard example — abort any run that exceeds $0.50:104105```typescript106// runWithBudget polls usageTotalUsd every 30s and aborts past the cap107const run = await runWithBudget('user/scraper', input, 0.50);108```109110## Resources111112- [Apify Pricing](https://apify.com/pricing)113- [Usage & Resources](https://docs.apify.com/platform/actors/running/usage-and-resources)114- [Compute Unit Calculator](https://help.apify.com/en/articles/3490384-what-is-a-compute-unit)115- [pricing-model.md](references/pricing-model.md) — full pricing tables (local)116- [implementation.md](references/implementation.md) — complete step-by-step code (local)117- [examples.md](references/examples.md) — worked cost-tuning scenarios (local)118119## Next Steps120121For architecture patterns, see `apify-reference-architecture`.122123---124125**Source:** [`jeremylongshore/claude-code-plugins-plus-skills`](https://github.com/jeremylongshore/claude-code-plugins-plus-skills) → `plugins/saas-packs/apify-pack/skills/apify-cost-tuning/SKILL.md`