Experimentation Platforms — Build vs Buy Decision Framework

The Temptation to Build

Every engineering team that gets serious about experimentation eventually asks: "Why are we paying $50K/year for a platform when we could build this ourselves?" The answer is usually more nuanced than either camp admits.

A series B startup built their own experimentation platform. It took 3 engineers 4 months. It worked for simple A/B tests. Then they needed multivariate tests, statistical significance calculations, mutual exclusion groups, and integration with their data warehouse. Eighteen months later, they had 2 full-time engineers maintaining the platform instead of building product.

When to Buy

Buy an experimentation platform when:
  ✓ You run fewer than 50 experiments per quarter
  ✓ Your engineering team is under 30 people
  ✓ You need results quickly (weeks, not quarters to get started)
  ✓ You don't have a dedicated data science team
  ✓ Your experiments are primarily UI/UX changes
  ✓ You need compliance features (audit logs, approval workflows)

Platform comparison:
  LaunchDarkly:  Best for feature flags first, experiments second
  Optimizely:    Best for marketing/content teams running experiments
  Statsig:       Best for product-led growth with statistical rigor
  Eppo:          Best for warehouse-native experimentation
  GrowthBook:    Best open-source option (self-hosted)

When to Build

Build your own when:
  ✓ You run 100+ experiments per quarter
  ✓ You have a dedicated experimentation or data science team
  ✓ You need deep integration with proprietary data systems
  ✓ You need sub-millisecond assignment latency
  ✓ Your experiments involve backend algorithms, not just UI
  ✓ You're spending > $200K/year on a vendor platform

The Hybrid Approach

Most mature teams land on a hybrid — buy the SDK, own the analysis:

// Use a vendor SDK for assignment and bucketing
import { GrowthBook } from "@growthbook/growthbook";
 
const gb = new GrowthBook({
  apiHost: "https://cdn.growthbook.io",
  clientKey: process.env.GROWTHBOOK_KEY,
  trackingCallback: (experiment, result) => {
    // Send to YOUR data warehouse, not the vendor's
    analytics.track("experiment_viewed", {
      experimentId: experiment.key,
      variationId: result.variationId,
      userId: getCurrentUserId(),
      timestamp: Date.now(),
      sessionId: getSessionId(),
      // Add your own context
      cartValue: getCartTotal(),
      userSegment: getUserSegment(),
    });
  },
});

Own the Analysis Layer

-- Run your own analysis in your data warehouse
-- This gives you full control over metrics and methodology
 
WITH experiment_users AS (
  SELECT
    user_id,
    variation_id,
    MIN(timestamp) AS first_exposure
  FROM experiment_events
  WHERE experiment_id = 'checkout_redesign_v2'
  GROUP BY user_id, variation_id
),
conversions AS (
  SELECT
    eu.user_id,
    eu.variation_id,
    COALESCE(SUM(o.revenue), 0) AS revenue,
    COUNT(DISTINCT o.order_id) AS orders
  FROM experiment_users eu
  LEFT JOIN orders o ON eu.user_id = o.user_id
    AND o.created_at >= eu.first_exposure
    AND o.created_at <= eu.first_exposure + INTERVAL '14 days'
  GROUP BY eu.user_id, eu.variation_id
)
SELECT
  variation_id,
  COUNT(*) AS users,
  AVG(revenue) AS avg_revenue_per_user,
  SUM(orders)::float / COUNT(*) AS conversion_rate,
  STDDEV(revenue) AS revenue_stddev
FROM conversions
GROUP BY variation_id;

The Assignment Architecture

If you do build, the assignment layer is the critical piece:

// Deterministic assignment using hashing
// Same user always gets the same variation
function assignVariation(
  userId: string,
  experimentId: string,
  variations: string[],
  trafficAllocation: number = 1.0
): string | null {
  const hash = murmurhash3(`${experimentId}:${userId}`);
  const normalized = hash / 0xFFFFFFFF; // 0 to 1
 
  // Traffic allocation: only include a percentage of users
  if (normalized > trafficAllocation) return null;
 
  // Assign to variation
  const bucket = Math.floor((normalized / trafficAllocation) * variations.length);
  return variations[Math.min(bucket, variations.length - 1)];
}
 
// Mutual exclusion: prevent users from being in conflicting experiments
function checkMutualExclusion(
  userId: string,
  experimentId: string,
  exclusionGroups: Map<string, string[]>
): boolean {
  for (const [groupId, experiments] of exclusionGroups) {
    if (!experiments.includes(experimentId)) continue;
    
    // User is assigned to one experiment per exclusion group
    const groupHash = murmurhash3(`exclusion:${groupId}:${userId}`);
    const assignedExperiment = experiments[groupHash % experiments.length];
    
    if (assignedExperiment !== experimentId) return false;
  }
  return true;
}

Statistical Rigor Checklist

Whether you build or buy, ensure your platform handles:

  ✓ Sample size calculation (how long to run the test)
  ✓ Sequential testing (peeking without inflating false positives)
  ✓ Multiple comparison correction (testing many metrics)
  ✓ Novelty/primacy effects (results change over time)
  ✓ Sample Ratio Mismatch detection (uneven assignment = bug)
  ✓ Minimum Detectable Effect size (what's worth detecting?)

  Common mistakes:
  ✗ Stopping tests early when results "look significant"
  ✗ Running tests on segments after seeing overall results
  ✗ Not accounting for day-of-week effects
  ✗ Using overall conversion rate instead of per-user metrics

The Real Cost of Building

Year 1 costs of building an experimentation platform:
  3 engineers × 4 months = $200K in salary
  Infrastructure (feature flag service, analytics): $20K
  Opportunity cost (features not built): $300K+
  Total: ~$520K

Year 2+ ongoing costs:
  1-2 engineers maintaining and improving: $150-300K/year
  Infrastructure: $30K/year

Vendor platform costs:
  Year 1: $30-100K (depending on scale)
  Year 2+: $50-150K/year (grows with usage)

Break-even: Usually 3-4 years at scale (100+ experiments/quarter)

The right answer for most teams: buy a platform, own your analysis, and invest engineering time in building product features that need testing — not the testing infrastructure itself. Build only when experimentation is a core competency that differentiates your business.