Pricing

Start free. Scale as you grow.

Every tier includes dashboards, approval workflows, and the contextual bandit. You're never optimizing blind, and nothing goes live without your sign-off.

Starter

Free

For indie developers and early startups evaluating the platform.

Get started
  • 10K decisions/month
  • 1 experiment at a time
  • Up to 3 arms per experiment
  • Standard LLM for ideation
  • Basic personalization
  • Hourly model refits
  • 30-day data retention
  • Community support

Pro

Recommended
$99/mo

For growing startups with real traffic and one product surface to optimize.

Get started
  • 250K decisions/month (then $0.50/1K)
  • 5 simultaneous experiments
  • Up to 8 arms per experiment
  • Frontier LLM with extended thinking
  • Full interaction modeling
  • All ideation heuristics
  • Composite rewards and guardrail metrics
  • Variable-based personalization
  • Unlimited data retention
  • Conclusions with reasoning
  • Email support

Growth

$499/mo

For scaling companies optimizing multiple surfaces that need to prove ROI.

Get started
  • 2M decisions/month (then $0.30/1K)
  • 25 simultaneous experiments
  • Unlimited arms (system-managed)
  • Ensemble model decision-making
  • Deep feature engineering
  • Advanced convergence (faster learning)
  • Holdout and incrementality measurement
  • Cross-experiment learning
  • Surrogate models for long-term reward prediction
  • Webhooks and warehouse exports
  • Priority compute and support
  • Team seats and roles

Enterprise

Custom

For high-traffic companies with compliance requirements and proprietary data.

Talk to us
  • Unlimited experiments and volume
  • Third-party data enrichment
  • Pre-loaded CRM and customer data
  • Custom synthetic data generation
  • Custom reward distributions
  • Identity resolution and CDP integration
  • SLA-backed latency
  • Dedicated environments
  • SSO, audit logs, RBAC
  • Custom contracts, security review, DPA
  • Dedicated solutions engineer

Comparison

Compare every plan.

FeatureStarterProGrowthEnterprise
Volume
Decisions/month10K250K2MUnlimited
Overage rate$0.50/1K$0.30/1KNegotiated
Simultaneous experiments1525Unlimited
Arms per experiment38UnlimitedUnlimited
Data retention30 daysUnlimitedUnlimitedUnlimited
Model refitsHourlyMore frequentPriority computePriority compute
Personalization
Contextual bandit
Basic personalization
Full interaction modeling
Deep feature engineering
Variable-based personalization
Identity resolution / CDP
Intelligence
Standard LLM ideation
Frontier LLM with extended thinking
Core ideation heuristicsAllAllAll
Ensemble decision-making
Surrogate models
Cross-experiment learning
Advanced convergence
Custom synthetic data
Optimization
Composite rewards
Guardrail metrics
Custom optimization instructions
Holdout / incrementality
Custom reward distributions
Trust & visibility
Dashboards and visualization
Approval workflows
Conclusions with reasoning
Third-party data enrichment
Pre-loaded CRM data
Infrastructure
Webhooks / warehouse exports
Dedicated environments
SLA-backed latency
SSO, audit logs, RBAC
Support
Community
Email
Priority
Dedicated solutions engineer
Team seats and roles

FAQ

Questions, answered.

What counts as a decision?

Every call to /decide is one decision. If you show an optimized upgrade prompt to a user, that's one decision. Tracking events (/events, /identify) don't count.

What happens when I hit my limit?

On Starter, decisions after 10K return your control content (no optimization, no disruption). On Pro and Growth, overage pricing kicks in automatically. We send alerts at 50%, 75%, and 100% so there are no surprises.

Can I review what the AI generates before it goes live?

Yes, on every tier. Approval workflows let you review and approve AI-generated arms before they reach your users. This is on by default and you can turn it off when you're comfortable.

What does "simultaneous experiments" mean?

An experiment is one element being optimized (your upgrade prompt, your homepage headline, your onboarding email). Simultaneous experiments is how many you can run at the same time.

How does the contextual bandit differ from A/B testing?

An A/B test splits traffic evenly and waits for statistical significance. A contextual bandit shifts traffic toward what's working as it learns, and personalizes per user based on context. It's faster, wastes less traffic on losing variants, and gives each user the version most likely to work for them.

Do I need Growth to run multiple experiments?

Pro supports up to 5 simultaneous experiments. Growth raises that to 25 and adds cross-experiment learning, where insights from one experiment inform others.