Switch to gym software built for martial arts — no exit fees, no per-location pricing, and no monthly subscription that spikes as you grow.
Pricing is quoted and scales with active member count — the bill grows in tier steps as your roster grows.
One price rule for every gym: 2% on memberships ($2 minimum), 3% on seminars/merch/drop-ins, $0 on POS. No tiers to decode, no quote to chase.
Onboarding is sales-assisted and typically runs multiple weeks before you're live.
Every feature included — white-labeled member app, AI churn alerts, waivers, training journal. Nothing is sold back to you as an add-on.
Funds route through their integrated payment processor, not a merchant account you own.
Done-for-you migration, included in the one-time $499 setup: we import your roster from your current software, set up Stripe Connect with you on a call, and theme the app to your brand.
Want the unspun, side-by-side version? Read the full OLM vs Zen Planner breakdown — including where they beat us.
CompareOLM is purpose-built for martial arts (BJJ, judo, muay thai, karate, MMA, wrestling). For CrossFit affiliates, Zen Planner's WOD library and programming integrations are a real advantage that OLM doesn't replicate. If you run both, weigh which side of the business is bigger.
OLM's belt and stripe tracking is per-discipline with promotion lineage — designed specifically for martial arts rank progression. Zen Planner's skill-tracking is more generic; it works but isn't tuned for BJJ stripes or Dan grades.
Zen Planner has a long bench of dashboards for fitness-gym KPIs. OLM's reporting is more focused — MRR, churn, attendance by member, revenue per member, instructor utilization, and a few others — but every report is CSV-exportable and the data model is queryable. If you live in spreadsheets, OLM's exports get you there; if you need pre-built fitness-industry dashboards, Zen Planner has more.
Yes via CSV export. Member roster, active subscriptions, and contact info migrate cleanly. Skill / rank records typically need to be recreated since the data shape differs.