A/B Testing & Experimentation Tools: Statsig, GrowthBook, Eppo, LaunchDarkly, Optimizely, VWO, AB Tasty, Convert

⬅️ DevOps & Tools Overview

If you're shipping a SaaS in 2026 and not running experiments, you're guessing. This is the consolidated comparison. Most indie SaaS over-pay for Optimizely (legacy enterprise tool with classic-era pricing) when Statsig free or GrowthBook OSS would cover them through $5M ARR. Most mid-market tries to extend feature flags into experimentation (LaunchDarkly experimentation tier; PostHog experiments) when a dedicated experimentation tool would deliver cleaner stats. The right pick depends on whether you're testing marketing-page conversion (visual editors win) or product-feature impact (SDK-based tools win) — they're different products with overlapping vendors.

TL;DR Decision Matrix

The first decision is what kind of experimentation you're doing. Marketing-page A/B (Optimizely / VWO / Convert), product-feature experimentation (Statsig / GrowthBook / Eppo / LaunchDarkly), warehouse-native rigorous statistics (Eppo / GrowthBook / Statsig Warehouse), and bundled-with-analytics (PostHog / Amplitude) are different problems with overlapping tools.

Decide What You Need First

Tools are not interchangeable. Pick by experiment type + stack.

Product-feature experimentation (the 50% case for SaaS)

You ship features and want to measure if they move retention, conversion, or revenue. SDK-based; server-side flag flips; long-running tests.

Right tools:

• Statsig — modern indie default; generous free tier
• GrowthBook — OSS; SQL-warehouse-based; auditable stats
• Eppo — warehouse-native; rigorous; mid-market+
• LaunchDarkly — if you already use LD for flags

Marketing-page A/B (the 25% case)

You're testing landing-page copy, hero images, CTA buttons. Visual editor; client-side; short-running tests.

Right tools:

• VWO — visual editor; heatmaps; mid-market
• Convert — Optimizely alternative at lower price
• Optimizely Web — enterprise default
• AB Tasty — EU equivalent

Bundled-with-analytics (the 15% case)

You already use PostHog, Amplitude, or Mixpanel and want experiments without another tool.

Right tools:

• PostHog Experiments — bundled OSS / cloud
• Amplitude Experiment — bundled
• Mixpanel Experiments — bundled (newer)

Warehouse-native rigorous (the 10% case)

You have a data warehouse (Snowflake, BigQuery, Databricks); you want experiments computed on warehouse data; data-team review of metrics.

Right tools:

• Eppo — built for this
• GrowthBook — OSS option
• Statsig Warehouse Native — newer tier

Provider Deep-Dives

Statsig — modern indie default

The 2026 winner for indie SaaS that wants real product experimentation without enterprise pricing. Founded 2021 by ex-Facebook experimentation team; raised by Sequoia/Madrona; very generous free tier (1B events/mo on Pro).

Pricing in 2026: Free (1B events / 1M users / unlimited flags / unlimited experiments — yes really), Pro $150/mo+, Enterprise custom. The free tier is the most generous in the experimentation space; competitors charge from event 1.

Features: feature flags, A/B/n experiments, holdouts, sequential testing, CUPED variance reduction, SDK in 20+ languages, server- and client-side, warehouse-native option, layers (concurrent experiments without contamination), auto-segmentation, analytics (events / funnels / retention), session replay add-on.

Stats engine: sequential / fixed-horizon; CUPED variance reduction; Bayesian option. Defaults are sensible — most teams won't outgrow the stats engine.

Why Statsig wins: Free tier handles indie SaaS through ~Series A. Stats are rigorous (not the rough heuristics some competitors use). SDK quality is best-in-class. Layers solve the "concurrent experiments contaminate each other" problem most tools ignore.

Trade-offs: visual editor for marketing pages is weaker than VWO / Optimizely. Some enterprise buyers still want LaunchDarkly for procurement reasons. Pricing tiers above free have jumps that surprise growing teams.

Pick if: you're indie-to-mid-market; want rigorous experimentation; appreciate generous free tier. Don't pick if: you only care about marketing-page A/B with visual editor.

GrowthBook — OSS warehouse-native

The OSS choice. GrowthBook computes experiment stats from your data warehouse — you control where data lives. Cloud-hosted option exists but most users self-host or use the free cloud tier.

Pricing in 2026: OSS free (self-host), Cloud Starter $40/mo+, Cloud Pro $200/mo+, Enterprise custom.

Features: feature flags, A/B/n experiments, warehouse-native (Snowflake / BigQuery / Postgres / Databricks / Redshift / ClickHouse), Bayesian + frequentist stats, sequential testing, CUPED, holdouts, SDK in 10+ languages.

Why GrowthBook is appealing: data-team-friendly. Stats are computed from your warehouse — auditable, no double-write of events. OSS means no vendor lock-in.

Trade-offs: requires a data warehouse to be useful (warehouse-native is the strength). Self-hosting adds ops burden. UI less polished than Statsig.

Pick if: you have a warehouse; want OSS / data-team review; principled. Don't pick if: no warehouse / want polished SaaS UX.

Eppo — warehouse-native, mid-market+

Eppo (rhymes with "hippo") is the warehouse-native option for mid-market+. Built for companies whose data team owns experimentation rigor. Used by companies like Notion, Webflow, Cameo.

Pricing in 2026: custom; typically $30K-$100K/yr+. Not for sub-$5M ARR.

Features: warehouse-native (Snowflake / BigQuery / Databricks / Redshift), CUPED variance reduction, sequential testing, fact-table modeling, deep metric definitions, experiment orchestration, multi-arm bandit, decision-frameworks built in.

Why Eppo: stats rigor that rivals in-house data-science teams. UX for data-savvy users. Trusted by data-team-led orgs.

Pick if: $10M+ ARR; data-team-led experimentation; need rigor. Don't pick if: indie scale (overkill and overpriced).

LaunchDarkly Experimentation

LaunchDarkly is the dominant feature flag tool; their experimentation tier extends it. Strong if you already pay for LD.

Pricing in 2026: bundled with LaunchDarkly Pro ($20-50/seat/mo for flags) + Experimentation add-on; total tends $1K-$5K/mo for mid-market+.

Features: tightly coupled to LD flags; unified dashboard; sequential testing; CUPED; experimentation-as-code via LD SDK.

Trade-offs: stats engine is correct but less sophisticated than Statsig/Eppo defaults. Experimentation tier is expensive on top of base LD. UI is dated relative to Statsig.

Pick if: already on LaunchDarkly and don't want a second vendor. Don't pick if: starting from scratch — Statsig + a flag tool may cost less.

PostHog Experiments

PostHog is product analytics + flags + replay + experiments + surveys + AI bundled. OSS available; cloud generous.

Pricing in 2026: cloud free up to 1M events/mo; OSS free; paid tiers usage-based ~$0.00031/event.

Features: experiments tied to PostHog events; client- and server-side; OSS-self-hostable.

Why PostHog appeals: consolidation. Replace 4-5 tools with one. Pricing scales with usage.

Trade-offs: stats engine is "good enough" but less sophisticated than Statsig/Eppo. Experimentation isn't the core product — it's one of nine.

Pick if: you want bundled analytics + flags + experiments; OSS-friendly. Don't pick if: experimentation is your central practice and you need rigor.

Optimizely Web

The original visual A/B tool. Acquired Episerver in 2020; now a "digital experience platform" with web A/B as one product.

Pricing in 2026: custom; typically $50K-$200K/yr+. Pricing is the elephant in the room — hasn't matched market shift.

Features: visual editor (drag-drop changes), client-side A/B, multi-variate, personalization, AI-powered recommendations.

Trade-offs: client-side A/B has fundamental issues (FOOC — flash of original content), expensive, slower-moving than modern tools. Heavy enterprise sales motion.

Pick if: enterprise marketing team with budget; existing Optimizely user. Don't pick if: starting fresh — VWO / Convert / Statsig deliver more for less.

Optimizely Feature Experimentation (formerly Full Stack)

Server-side / SDK-based experiments. Different product than Web; sometimes confused.

Pricing in 2026: enterprise; custom.

Features: server-side flags + experiments; SDK in many languages; rolloutsetc.

Trade-offs: feels stagnant compared to Statsig / LaunchDarkly. Pricing assumes enterprise budget.

Pick if: existing Optimizely customer needing server-side. Don't pick if: starting fresh.

VWO — visual A/B with heatmaps

VWO (Visual Website Optimizer) bundles A/B + heatmaps + session recording + on-site surveys. Mid-market marketing default.

Pricing in 2026: Growth $400/mo (50K visitors), Pro $1K/mo, Enterprise custom.

Features: visual editor, multi-page tests, heatmaps, session recordings, on-site surveys, web push, server-side via SmartCode.

Pick if: marketing-team-led A/B; want bundled heatmaps. Don't pick if: indie scale or product-experimentation focus.

Convert / AB Tasty

• Convert — Optimizely alternative at $400-1000/mo; visual editor; client-side; smaller team
• AB Tasty — French; popular EU; similar feature set; privacy-conscious option

Both fill the "Optimizely-but-cheaper" niche.

Amplitude Experiment

Bundled with Amplitude Analytics. Strong if Amplitude-locked.

Pricing in 2026: Plus $0/mo flags + experiments add-on, Growth $$, Enterprise custom.

Features: experiments tied to Amplitude events / cohorts; flags; SDK in major languages; Bayesian-default stats.

Pick if: Amplitude is your analytics. Don't pick if: starting fresh — Statsig + dedicated analytics outperforms.

Split — flags + experimentation

Founded 2015; LaunchDarkly's main competitor in flags. Acquired by Harness in 2024.

Pricing in 2026: Free (gen), Team $$, Enterprise custom.

Features: flags, experiments, monitoring; SDK in major languages.

Pick if: you want LaunchDarkly alternative at lower price; mid-market. Don't pick if: indie (Statsig free is more generous).

Unleash / Confidence — OSS for purists

• Unleash — Norwegian OSS feature flags; basic experimentation; self-host
• Confidence — Spotify-released OSS warehouse-native experimentation framework

Both for principled-OSS shops who self-host.

What Experimentation Tools Won't Do

Buying an experimentation tool doesn't:

1. Make your stats correct. Sample-ratio mismatch, novelty effects, peeking, multiple comparisons, effect-size errors — tools surface these but don't prevent them. Train the team.
2. Tell you what to test. Tools test hypotheses; they don't generate them. Hypothesis quality drives experiment ROI more than tool quality.
3. Replace product judgment. Some things shouldn't be A/B tested (brand, vision, illegal-things). Tools don't know the difference.
4. Speed up slow products. If your weekly active users are 1000, no statistical tool will detect a 5% lift in a reasonable time. Power constraints are real.
5. Replace an analytics setup. You still need event tracking, metric definitions, data quality. Tools sit on top of analytics; they don't replace it.

The honest framing: an experimentation tool is leverage for a team that already has analytics, hypotheses, and traffic. Without those upstream pieces, no tool helps.

Pragmatic Stack Patterns

Pattern 1: Indie SaaS launch ($0-150/mo)

• Statsig Free — flags + experiments + analytics
• PostHog OSS / Cloud Free — analytics + replay
• Total: $0-50/mo at indie scale

Pattern 2: Indie SaaS with traction ($150-500/mo)

• Statsig Pro — primary experimentation
• PostHog Cloud — analytics
• Total: $200-500/mo

Pattern 3: Mid-market product team ($1K-5K/mo)

• Statsig OR GrowthBook Cloud — experimentation
• Amplitude OR Mixpanel — analytics
• LaunchDarkly — feature flags (if you need flag governance)
• Total: $2K-5K/mo

Pattern 4: Marketing A/B testing layer

• VWO OR Convert — visual A/B for landing pages
• Statsig OR PostHog — product experimentation
• Total: $400-1K/mo for VWO + product-side

Pattern 5: Warehouse-native enterprise ($10K+/mo)

• Eppo OR GrowthBook Enterprise — experimentation
• dbt — metric modeling
• Snowflake / BigQuery / Databricks — warehouse
• LaunchDarkly — flags (separate product)

Pattern 6: OSS / self-hosted

• Unleash OR GrowthBook OSS — flags + experiments
• PostHog OSS — analytics + replay
• Total: $0 + hosting; ops burden

Decision Framework: Three Questions

1. What are you testing — pages or features?

   • Pages → VWO / Convert / Optimizely
   • Features → Statsig / GrowthBook / LaunchDarkly
   • Both → Statsig + a marketing tool

2. Do you have a data warehouse?

   • Yes → GrowthBook / Eppo (warehouse-native is best practice)
   • No → Statsig / LaunchDarkly (event-based works fine)

3. What's your stage / budget?

   • Pre-Series A → Statsig free + PostHog
   • $1M-10M ARR → Statsig Pro / GrowthBook Cloud
   • $10M+ → Eppo / Statsig Enterprise / LaunchDarkly + experiments

Verdict

For 60% of SaaS in 2026: Statsig. Free tier handles indie scale; rigorous stats; SDK quality; layers solve the contamination problem. The default pick.

For 20%: GrowthBook. OSS warehouse-native; data-team-friendly; auditable. Pick if you have a warehouse and care about OSS.

For 10%: PostHog Experiments. Bundled with analytics + replay + flags. Pick when consolidating tools matters more than experimentation rigor.

For 5%: VWO or Convert. Marketing-page A/B with visual editor. Pick when the use case is landing-page optimization, not product experimentation.

For 5%: Eppo / LaunchDarkly Experimentation / Optimizely. Specific situations — already-locked-in or mid-market+ with specific stack constraints.

The mistake to avoid: buying Optimizely because "everyone uses it" without checking what year that was true. Optimizely's price and feature pace haven't kept up. Most teams who pay $100K/yr for Optimizely could get more value from $5K/yr Statsig + a heatmap tool.

The second mistake: trying to A/B test with too little traffic. A 5% lift on a 1,000-visitor-per-week page needs 6+ months to reach significance. The tool can't fix that — only traffic + bigger lifts can. Don't blame the tool.

See Also

• Feature Flag Providers — flags as the operational base
• Product Analytics Providers — analytics underneath experiments
• Session Replay Providers — qualitative context
• BI Analytics Tools — data warehouse layer
• Web Analytics Providers — site analytics for marketing tests
• Survey & NPS Providers — qualitative companion
• VibeWeek: A/B Testing Implementation — how to wire it up
• LaunchWeek: Pricing Experiments — pricing-specific A/B
• LaunchWeek: A/B Testing Strategy — when and what to test