Should You Build, Buy, or Scrape Features for a Micro‑App? A Product Manager’s Decision Map

Should you build, buy, or scrape features for a micro‑app? A product manager’s decision map

Hook: You need a working feature in days, not quarters — but you can't sacrifice data quality, compliance, or long‑term maintenance. Choose wrong and your micro‑app will either never launch, leak user data, or become a maintenance nightmare.

This decision map gives product managers a practical framework to decide whether to build, buy (SaaS/embed), or scrape third‑party data when shipping micro‑apps in 2026. It weighs speed, cost, data quality, maintenance, and privacy — and includes code, cost models, and actionable checklists you can use in your next sprint planning session.

Executive summary — the short recommendation

Pick build when you own the core differentiator and need full control (higher TCO but best long‑term flexibility). Pick buy when the feature is commodity, latency‑sensitive, or needs enterprise SLA and compliance. Pick scrape only for augmenting data where no API exists, and when you can accept higher operational and legal risk — or when you need a fast prototype before negotiating an API partnership.

In 2026 the winning strategies are hybrid: prototype by scraping or embedding, then operationalize by buying or building once product/market fit is clear.

Why this matters in 2026

Trends that change the calculus this year:

• AI‑driven low‑code and “vibe‑coding” mean prototypes can go from idea to demo in days (see Rebecca Yu’s Week‑long micro‑app example).
• Regulators and platforms tightened rules on automated access in late 2024–2025; privacy and platform terms are enforced more aggressively in 2025–2026.
• SaaS composability and embeddable feature SDKs matured — fewer features are truly differentiating.
• Scraping tech improved (Headless browsers, stealth proxies, Puppeteer/Playwright orchestration) but operational cost (proxies, CAPTCHA solving, monitoring) rose with enforcement.

Decision framework — 5 dimensions

Evaluate each candidate feature across these five axes. Score 1–5 (low→high) and total the points to guide the decision.

1. Time to market (speed)

Consider prototype vs production timelines.

• Build: Prototype 2–8 weeks; production 2–6+ months.
• Buy: 1–7 days for SaaS/embedded components; integration and compliance checks may add 1–3 weeks.
• Scrape: Prototype 1–7 days (quick wins); production hardening 4–12+ weeks (proxy rotation, anti‑bot, monitoring).

2. Cost (TCO and OpEx)

Include dev hours, infrastructure, third‑party fees, proxies, and legal/compliance overhead.

• Build: High fixed cost (engineering, infra). Good amortization if you reuse across products.
• Buy: Predictable subscription fees; lower initial engineering; potential vendor lock‑in.
• Scrape: Low initial dev cost but rising OpEx (proxies, CAPTCHA services, retesting), and unpredictable costs from site changes or legal actions.

3. Data quality & reliability

Assess freshness, completeness, noise, and SLAs.

• Build: Best if you control the data source or can instrument it.
• Buy: SaaS vendors often provide vetted, enriched data with SLAs and monitoring.
• Scrape: Best effort. HTML changes, rate limits, and deliberate blocking reduce reliability.

4. Maintenance & operational burden

Count the engineering cycles needed for updates and incidents.

• Build: Ongoing (bugfixes, scaling) proportional to your team size.
• Buy: Vendor handles most ops; you manage integration and eventual migration risk.
• Scrape: Continuous maintenance; periodic breakfix work when targets change or detection improves.

5. Privacy, compliance & legal risk

Evaluate regulatory exposure (GDPR/DSA/CPRA-like laws), platform terms of service, and user data risk.

• Build: Full control — easier to certify and audit data flows.
• Buy: Vendor may provide compliance certifications (SOC2, ISO27001); check contractual responsibilities.
• Scrape: Highest legal risk — recent enforcement in late 2024–2025 increased litigation and takedowns; treat scraping as a tactical, ephemeral approach unless cleared by legal.

Practical decision map — step by step

1. Clarify the feature’s strategic value: is it core IP or a commodity? If core → lean build. If commodity → buy.
2. Prototype the UX quickly (1–7 days). Prefer safe, reversible tactics: embed a SaaS widget or use a small scraping prototype behind feature flags.
3. Run a 4‑week cost and reliability experiment. Track failure rate, latency, and maintenance hours per week.
4. Score the feature on the 5 dimensions above. If total favors buy or build, plan migration from prototype tech (scrape/embed) to production architecture.
5. Get legal signoff for scraping or embedding third‑party data. Define fallbacks (cached content, user warnings) in case of takedowns.

Actionable cost model (example)

Quick ROI template for a single feature. Numbers are illustrative for 2026 engineering rates and vendor pricing.

• Engineer fully burdened rate: $160/hour.
• Build: 2 engineers × 3 months = 2 × 480h = 960h → $153,600 (plus infra $2k/mo).
• Buy: SaaS integration: 40h eng = $6,400 + vendor $2k/mo = $30,400 in year 1.
• Scrape (prototype → production): 80h prototype + 160h hardening = 240h → $38,400 + proxies/CAPTCHA/ops = $1k–$5k/mo → $50k year 1.

Interpretation: If feature drives >$100k ARR incremental, building may be justified. For early experiments or features under $50k ARR, buying or scraping is often more economical.

Concrete engineering examples

When you build — skeleton Playbook

Use when you control UX differentiation and need reliable data. Typical stack: microservice API, event streaming, observability, and infra autoscaling.

// Example: feature flag + simple Node.js microservice entrypoint
const express = require('express');
const app = express();
app.get('/feature', (req, res) => {
  // check feature flag
  if (!isEnabled('new-feature', req.user)) return res.status(404).send();
  // call internal service
  const data = getCoreData(req.user.id);
  res.json({ data });
});

When you buy — integration checklist

Prefer vendors that provide: API keys, webhooks, RBAC, data residency options, and SLAs. Checklist for integration:

• Test API latency and rate limits.
• Validate data mapping and transformations in a staging pipeline.
• Set up contract tests and fallback behavior for vendor outages.
• Confirm vendor compliance attestations and data processing agreements (DPAs).

When you scrape — minimal robust pattern (Playwright + rotating proxies)

Only use this for prototypes or when no API option exists. This example shows responsible basics: rate limiting, randomized headers, and proxy rotation.

const { chromium } = require('playwright');
const proxies = ['http://proxy1:8000','http://proxy2:8000'];
async function fetchPage(url, proxy) {
  const browser = await chromium.launch({ args: [`--proxy-server=${proxy}`] });
  const page = await browser.newPage();
  await page.setExtraHTTPHeaders({ 'User-Agent': randomUA() });
  await page.goto(url, { waitUntil: 'networkidle' });
  const html = await page.content();
  await browser.close();
  return html;
}

// Rate limit and rotate proxies
async function safeFetch(url) {
  await sleep(randomBetween(1000, 3000));
  const proxy = pick(proxies);
  return await fetchPage(url, proxy);
}

Important: add continuous monitoring (uptime, extraction success rate) and a legal checklist before storing or exposing scraped personal data.

Operational & legal safeguards for scraping

If you decide to scrape, follow these guardrails to lower risk and maintain reliability:

• Run scraping behind a feature flag and only for logged‑in users that opt‑in where personal data is involved.
• Cache aggressively and respect robots.txt where feasible; maintain a cache TTL and refresh policy.
• Throttle requests per domain, use randomized headers, and rotate proxies to reduce detection.
• Log and alert on extraction failures; automate snapshot tests to detect target page changes.
• Legal: get counsel signoff, maintain auditable logs showing purpose limitation and minimal retention. See enterprise playbooks for operational examples when risk is material.

“Scrape for speed, not as a long‑term data fabric.” — Practical rule of thumb

Case studies & real examples (experience)

Micro‑app prototype move from scrape → buy (example)

Scenario: A marketplace micro‑app needed competitor price signals quickly.

• Week 1: Scraped public pricing pages to validate pricing sensitivity and UX. Cost: 40h dev + $400 proxies.
• Month 1: Data showed positive user engagement and willingness to pay. Team negotiated a vendor API for normalized pricing at $3k/mo. Migration took 3 weeks and 80h engineering.
• Result: Lower operational burden, improved data quality, and predictable cost; total time to revenue halved vs building an internal crawling infra.

When building won — internal identity enrichment

Scenario: A product team needed deeply integrated identity resolution across several owned systems. Because the logic encoded IP and ML models that were core to the roadmap, they built a microservice and saved future vendor fees. Higher initial cost but faster experimentation in product roadmap year 2–3.

Scoring example — run this quick mental model

Score each option 1–5 in the five dimensions. Here’s a sample score for a “local event discovery” micro‑app feature:

• Build: Speed 2, Cost 2, Data quality 5, Maintenance 4, Privacy 5 → Total 18 → Build if differentiator.
• Buy: Speed 5, Cost 4, Data quality 4, Maintenance 5, Privacy 4 → Total 22 → Buy if feature is commodity and budget allows.
• Scrape: Speed 5, Cost 3, Data quality 2, Maintenance 2, Privacy 1 → Total 13 → Good for a prototype but not production.

Future predictions and advanced strategies (2026+)

• More PMs will adopt a two‑phase pattern: (fast prototype via scraping or embedded SaaS) → (harden by buying or building after validation). See our notes on data fabric and API trends.
• Expect more mid‑market vendors to offer modular, embeddable SDK features (analytics, search, enrichment) priced for micro‑apps — examples include Compose.page migrations in the wild (case study).
• Data marketplaces will grow, letting teams purchase cleaned, contractually safe datasets rather than scraping — reducing legal friction.
• AI will accelerate both prototyping and maintenance (automated selector repair, anomaly detection in scraped data), shifting cost curves but not eliminating legal risk.

Checklist: What to validate before deciding

• Is the feature core IP? If yes, favor build.
• Do viable vendor APIs exist with acceptable SLAs and price? If yes, favor buy.
• Can you prototype quickly and ethically with scraping to validate demand? If yes, use scraping for prototyping only.
• Have legal and privacy teams signed off on data flow and retention policies?
• Do you have observability and alerts to measure operational effort weekly? If not, delay productionization — consider edge AI tools to help with monitoring and alerting.

Actionable takeaways

• Use scraping as a short‑lived validation tool, not a long‑term backbone.
• Buy when the feature is commoditized and you need reliability and compliance quickly.
• Build when the feature contains your product differentiation or when vendor lock‑in cost exceeds build TCO.
• Always plan a migration path: prototype → vendor → build (or prototype → build) depending on signals.
• Budget 10–20% of development time for continuous monitoring and maintenance for any external data dependency.

Closing — product manager’s quick decision flow

1. Is the feature core? Yes → Build. No → go to 2.
2. Is there a reputable SaaS with the right data SLAs & compliance? Yes → Buy. No → go to 3.
3. Can you prototype via scraping in <7 days with legal signoff? Yes → Prototype by scraping and validate. No → Build.

In 2026 this flow captures reality: prototyping is faster than ever, vendors are more capable, and legal risk for scraping is real and rising. Use the decision map above to make repeatable product choices that balance speed with long‑term sustainability.

Call to action

If you’re evaluating a micro‑app feature now, run the five‑axis scorecard on your top three candidate approaches and share the results with engineering and legal before committing. Want a ready‑to‑use scoring spreadsheet and prototype templates (Playwright + SaaS integration)? Check our micro‑apps DevOps playbook and the Compose.page case study to convert your decision map into a delivery plan.

Related Reading

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