Comparison
Blazeway vs. Optimizely
Optimizely is the gold standard for enterprise experimentation at scale. Blazeway is built for founders running their first hundred experiments. The gap is intentional.
Last updated: March 2026 · Daniel Janisch, Founder of Blazeway
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The Core Difference
Optimizely is enterprise experimentation infrastructure. It covers A/B testing, feature flags, full-stack experimentation, personalization, and a full digital experience platform. It is deployed by engineering teams at large organizations and typically runs as a multi-year contract.
Blazeway is a focused A/B testing tool with a built-in documentation layer. You write a hypothesis, run the test, and record what you learned. Setup takes five minutes. The monthly cost is less than most enterprise tools charge per user per day.
The products overlap on the concept of A/B testing. They share almost nothing else.
Optimizely is built for organizations where experimentation is an engineering discipline with dedicated infrastructure budget. Blazeway is built for founders who want each test to leave behind a documented insight, not just a result.
Feature Comparison
| Feature | Blazeway | Optimizely |
|---|---|---|
| A/B Testing | ✅ Yes | ✅ Yes |
| Feature Flags | ❌ No | ✅ Yes |
| Full-Stack / Server-side Testing | ❌ No | ✅ Yes |
| Multivariate Testing | ❌ No | ✅ Yes |
| Personalization Engine | ❌ No | ✅ Yes |
| Visual Editor (no-code) | ❌ No | ✅ Yes |
| Multi-language SDK Support | ❌ No | ✅ Yes (10+ languages) |
| Hypothesis Board | ✅ Built-in | ❌ Not included |
| Insight Documentation | ✅ Structured per experiment | ❌ Not included |
| Decision Timeline | ✅ Full experiment history | ❌ Not included |
| LLM Export | ✅ One-click export | ❌ Not included |
| Cookieless Tracking | ✅ No consent banner needed | ⚠️ Cookie-based (consent required) |
| GDPR Compliance | ✅ Privacy-by-design | ✅ Compliant (cookies required) |
| Setup Time | ~5 min, <2KB snippet | Weeks to months for full deployment |
| Free Plan | ✅ 1,000 events/mo | ❌ No |
| Pricing | $20/month | Custom pricing, typically $50k+/year |
Pricing: A Different Category Entirely
Blazeway offers a free plan with 1,000 events/month. The Starter plan is $20/month for 2,000 events/month, with no per-seat pricing and no traffic-based tiers.
Optimizely uses enterprise pricing, available on request. Contracts typically run $50,000 to over $200,000 per year. There is no self-serve plan.
This is not a price comparison in the usual sense. Optimizely's pricing reflects that it is sold to organizations with procurement processes, legal review, and dedicated implementation teams. If you are reading a comparison page to decide between the two, Optimizely is almost certainly not the right scope for your current stage.
Privacy and GDPR: The Consent Problem at Scale
Optimizely's client-side A/B testing uses cookies to track visitor assignment. Under GDPR, that requires informed consent before any cookie is set. Visitors who decline are excluded from experiments.
For large enterprises with legal and compliance teams, managing consent is a solved operational problem. For a small EU-based product with 70-80% cookie rejection rates in Germany, it means your A/B test data captures less than a quarter of your actual audience.
Blazeway uses no cookies and collects no personal data. No consent banner is required. Experiments run on every visitor. Read the full GDPR-compliant A/B testing guide.
What Optimizely Does That Blazeway Doesn't
Feature flags: Optimizely's flag management lets engineering teams roll out features gradually, run experiments in server-side code, and separate deployment from release.
Full-stack experimentation: Run experiments in backend code, APIs, and mobile apps across 10+ language SDKs.
Personalization: Deliver different experiences to different audience segments based on behavioral and attribute data.
Visual editor: Non-technical marketers create test variations without code.
Enterprise governance: Role-based permissions, audit logs, multi-team workflows, and enterprise integrations.
If any of these are requirements, Optimizely is the right scope. Blazeway does not compete with enterprise experimentation infrastructure.
What Blazeway Does That Optimizely Doesn't
Hypothesis documentation: Every experiment starts with a structured hypothesis. Observation, mechanism, prediction.
Insight capture: When a test concludes, you record what you learned about your users. That insight becomes institutional knowledge.
Decision timeline: All experiments form a chronological record of product decisions made through data.
LLM export: Export your full experiment history as a structured prompt for AI analysis.
Self-serve in five minutes: No procurement, no implementation team, no contract negotiation.
Price point that matches early stage: $20/month for a founding team is a sensible experiment. $50k+/year is an organizational commitment.
When to Choose Optimizely
- · You are at a mid-to-large organization with an engineering team dedicated to experimentation
- · You need feature flag management as core infrastructure
- · You run experiments in server-side code, mobile apps, or across multiple platforms
- · You need personalization at scale with advanced audience segmentation
- · Procurement, legal review, and multi-year contracts are normal for your tooling decisions
When to Choose Blazeway
- · You are a founder or small team running experiments without dedicated engineering resources
- · You want to be set up and running in an afternoon, not weeks
- · You want every experiment documented with a hypothesis and a written insight
- · You are building in the EU and want tests to run on your full audience without cookie consent complexity
- · Optimizely's scope is enterprise infrastructure and you are not at that stage
The Honest Summary
Optimizely is the right tool for organizations where experimentation is a core engineering function. It earns its complexity and its price at that scale.
Blazeway is not a lightweight Optimizely. It is a different product for a different stage. The goal is not to run experiments at enterprise scale. The goal is to make every experiment leave behind something more useful than a result: a documented insight, a written hypothesis, a compounding record of product decisions.
If you are evaluating Optimizely and the procurement process alone feels like more overhead than your current team can absorb, that is a useful signal about fit.