> apify-cost-tuning

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: "apify cost", "apify billing", "reduce apify costs", "apify pricing", "apify expensive", "apify budget", "compute units".

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$curl "https://skillshub.wtf/jeremylongshore/claude-code-plugins-plus-skills/apify-cost-tuning?format=md"
SKILL.mdapify-cost-tuning

Apify Cost Tuning

Overview

Apify charges based on compute units (CU), proxy traffic (GB), and storage. One CU = 1 GB memory running for 1 hour. This skill covers how to analyze, reduce, and monitor costs across all three dimensions.

Pricing Model

Compute Units (CU)

CU = (Memory in GB) x (Duration in hours)

Example: 2048 MB (2 GB) running for 30 minutes = 2 x 0.5 = 1 CU
PlanCU PriceIncluded CUs
FreeN/ALimited trial
Starter$0.30/CUVaries by plan
Scale$0.25/CUVolume discounts
EnterpriseCustomNegotiated

Proxy Costs

Proxy TypeCostUse Case
DatacenterIncluded in planNon-blocking sites
Residential~$12/GBSites that block datacenters
Google SERP~$3.50/1000 queriesGoogle search results

Storage

Named datasets and KV stores persist indefinitely but count against storage quota. Unnamed (default run) storage expires after 7 days.

Instructions

Step 1: Analyze Current Costs

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });

async function analyzeActorCosts(actorId: string, days = 30) {
  const { items: runs } = await client.actor(actorId).runs().list({
    limit: 1000,
    desc: true,
  });

  const cutoff = new Date(Date.now() - days * 86400_000);
  const recentRuns = runs.filter(r => new Date(r.startedAt) > cutoff);

  let totalCu = 0;
  let totalUsd = 0;
  let totalDurationSecs = 0;

  for (const run of recentRuns) {
    totalCu += run.usage?.ACTOR_COMPUTE_UNITS ?? 0;
    totalUsd += run.usageTotalUsd ?? 0;
    totalDurationSecs += run.stats?.runTimeSecs ?? 0;
  }

  const avgCuPerRun = recentRuns.length > 0 ? totalCu / recentRuns.length : 0;
  const avgCostPerRun = recentRuns.length > 0 ? totalUsd / recentRuns.length : 0;

  console.log(`=== Cost Analysis: ${actorId} (last ${days} days) ===`);
  console.log(`Runs:              ${recentRuns.length}`);
  console.log(`Total CU:          ${totalCu.toFixed(4)}`);
  console.log(`Total cost:        $${totalUsd.toFixed(4)}`);
  console.log(`Avg CU/run:        ${avgCuPerRun.toFixed(4)}`);
  console.log(`Avg cost/run:      $${avgCostPerRun.toFixed(4)}`);
  console.log(`Total duration:    ${(totalDurationSecs / 3600).toFixed(2)} hours`);

  // Find the most expensive run
  const mostExpensive = recentRuns.reduce(
    (max, r) => ((r.usageTotalUsd ?? 0) > (max.usageTotalUsd ?? 0) ? r : max),
    recentRuns[0],
  );
  if (mostExpensive) {
    console.log(`Most expensive:    $${mostExpensive.usageTotalUsd?.toFixed(4)} (${mostExpensive.id})`);
  }

  return { totalCu, totalUsd, avgCuPerRun, avgCostPerRun, runs: recentRuns.length };
}

Step 2: Reduce Memory Allocation

Memory is the biggest cost lever. Most CheerioCrawler Actors are over-provisioned.

// Test with progressively lower memory to find the sweet spot
for (const memory of [4096, 2048, 1024, 512, 256]) {
  try {
    const run = await client.actor('user/actor').call(testInput, {
      memory,
      timeout: 600,
    });

    console.log(
      `${memory}MB: ${run.status} | ` +
      `${run.stats?.runTimeSecs}s | ` +
      `${run.usage?.ACTOR_COMPUTE_UNITS?.toFixed(4)} CU | ` +
      `$${run.usageTotalUsd?.toFixed(4)}`
    );

    if (run.status !== 'SUCCEEDED') break;
  } catch (error) {
    console.log(`${memory}MB: FAILED — ${(error as Error).message}`);
    break;
  }
}

Typical memory sweet spots:

Actor TypeStart AtSweet Spot
CheerioCrawler (simple)256 MB256-512 MB
CheerioCrawler (complex)512 MB512-1024 MB
PlaywrightCrawler2048 MB2048-4096 MB
Data processing1024 MB1024-2048 MB

Step 3: Optimize Crawl Duration

Faster crawls = fewer CUs consumed:

const crawler = new CheerioCrawler({
  // Higher concurrency = faster completion
  maxConcurrency: 30,

  // Don't wait too long on slow pages
  requestHandlerTimeoutSecs: 20,

  // Stop early when you have enough data
  maxRequestsPerCrawl: 1000,

  // Avoid unnecessary retries
  maxRequestRetries: 2,  // Default: 3

  requestHandler: async ({ request, $, enqueueLinks }) => {
    // Only extract what you need
    await Actor.pushData({
      url: request.url,
      title: $('title').text().trim(),
      // Don't scrape entire page body if you don't need it
    });

    // Only enqueue relevant links (not every link on the page)
    await enqueueLinks({
      selector: 'a.product-link',  // Specific selector, not 'a'
      strategy: 'same-domain',
    });
  },
});

Step 4: Minimize Proxy Costs

// Strategy 1: Use datacenter proxy first (free with plan)
const dcProxy = await Actor.createProxyConfiguration({
  groups: ['BUYPROXIES94952'],
});

// Strategy 2: Only use residential proxy when needed
// Don't waste residential bandwidth on non-blocking sites

// Strategy 3: Minimize data transfer through residential proxy
const crawler = new PlaywrightCrawler({
  proxyConfiguration: resProxy,
  preNavigationHooks: [
    async ({ page }) => {
      // Block images, fonts, CSS (saves residential proxy GB)
      await page.route('**/*.{png,jpg,jpeg,gif,svg,webp,ico,woff,woff2,ttf,css}',
        route => route.abort()
      );
    },
  ],
});

// Strategy 4: Session stickiness (reduces new proxy connections)
const crawler = new CheerioCrawler({
  proxyConfiguration: resProxy,
  useSessionPool: true,
  sessionPoolOptions: {
    sessionOptions: {
      maxUsageCount: 100,  // More reuse = fewer new connections
    },
  },
});

Step 5: Cost Guard for Runaway Actors

async function runWithBudget(
  actorId: string,
  input: Record<string, unknown>,
  maxCostUsd: number,
) {
  const run = await client.actor(actorId).start(input, {
    memory: 512,
    timeout: 3600,
  });

  // Poll every 30 seconds
  const interval = setInterval(async () => {
    try {
      const status = await client.run(run.id).get();
      const cost = status.usageTotalUsd ?? 0;

      if (cost > maxCostUsd) {
        console.error(`Budget exceeded: $${cost.toFixed(4)} > $${maxCostUsd}. Aborting.`);
        await client.run(run.id).abort();
        clearInterval(interval);
      }
    } catch {
      // Ignore polling errors
    }
  }, 30_000);

  const finished = await client.run(run.id).waitForFinish();
  clearInterval(interval);
  return finished;
}

// Usage: max $0.50 per run
const run = await runWithBudget('user/scraper', input, 0.50);

Step 6: Monitor Monthly Usage

async function monthlyUsageReport() {
  // Get all Actors
  const { items: actors } = await client.actors().list();

  let grandTotalUsd = 0;
  const report: { actor: string; runs: number; cost: number }[] = [];

  for (const actor of actors) {
    const { items: runs } = await client.actor(actor.id).runs().list({
      limit: 1000,
      desc: true,
    });

    const thisMonth = new Date();
    thisMonth.setDate(1);
    thisMonth.setHours(0, 0, 0, 0);

    const monthlyRuns = runs.filter(r => new Date(r.startedAt) >= thisMonth);
    const monthlyCost = monthlyRuns.reduce(
      (sum, r) => sum + (r.usageTotalUsd ?? 0), 0,
    );

    if (monthlyRuns.length > 0) {
      report.push({
        actor: actor.name,
        runs: monthlyRuns.length,
        cost: monthlyCost,
      });
      grandTotalUsd += monthlyCost;
    }
  }

  // Sort by cost descending
  report.sort((a, b) => b.cost - a.cost);

  console.log('\n=== Monthly Cost Report ===');
  console.log(`${'Actor'.padEnd(30)} | ${'Runs'.padEnd(6)} | Cost`);
  console.log('-'.repeat(55));
  for (const r of report) {
    console.log(`${r.actor.padEnd(30)} | ${String(r.runs).padEnd(6)} | $${r.cost.toFixed(4)}`);
  }
  console.log('-'.repeat(55));
  console.log(`${'TOTAL'.padEnd(30)} | ${' '.padEnd(6)} | $${grandTotalUsd.toFixed(4)}`);
}

Cost Optimization Checklist

  • Memory profiled (start low: 256-512MB for Cheerio)
  • maxRequestsPerCrawl set to prevent runaway crawls
  • Datacenter proxy used when possible (free with plan)
  • Residential proxy: images/CSS/fonts blocked to save bandwidth
  • maxConcurrency tuned (higher = faster = fewer CUs)
  • Scheduled runs have appropriate frequency (don't over-scrape)
  • Cost guard implemented for expensive runs
  • Monthly usage reviewed

Error Handling

IssueCauseSolution
Unexpected cost spikeNo maxRequestsPerCrawlAlways set an upper bound
High residential proxy costScraping images/fontsBlock non-essential resources
Over-provisioned memoryDefault 1024MBProfile and reduce to minimum
Too many scheduled runsAggressive cronReduce frequency if data freshness allows

Resources

Next Steps

For architecture patterns, see apify-reference-architecture.

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