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Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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In the competitive landscape of open core software, finding the right pricing strategy can mean the difference between sustainable growth and stagnation. While your product may have a free, open-source foundation, monetizing the premium features effectively requires systematic experimentation, not guesswork. For product and growth leaders at open core companies, knowing which pricing experiments to prioritize can dramatically accelerate your path to revenue optimization.
Understanding the Open Core Pricing Challenge
Open core companies face a unique pricing dilemma: they must balance free community adoption with premium feature monetization. This hybrid model creates specific challenges for pricing strategy, as users already have access to core functionality without paying. The premium features must therefore demonstrate sufficient value to justify the upgrade.
According to OpenView Partners' 2022 SaaS Benchmarks report, companies that run systematic pricing experiments grow revenue 30% faster than those that don't. Yet many open core companies delay pricing optimization, focusing instead on product development and community growth.
Essential Pricing Experiments for Open Core Companies
Let's explore the most impactful pricing experiments you should consider running first, based on where they've proven most effective for similar companies.
1. Feature Value Testing
Before setting any prices, determine which premium features actually drive willingness to pay.
The experiment: Create multiple feature bundles and test user interest through:
- Limited-time beta access offers
- Surveys with feature ranking exercises
- Usage tracking of free trial features
MongoDB used this approach when transitioning from their open source database to their Atlas cloud platform. They discovered that automated backups and scaling capabilities drove significantly higher willingness to pay than originally assumed.
2. Freemium Threshold Testing
Finding the optimal dividing line between free and paid is critical for open core companies.
The experiment: Test different feature demarcations by:
- Moving specific features across the free/paid boundary
- Testing different usage limits (users, storage, API calls)
- Measuring conversion impact and community sentiment
Elastic famously ran extensive threshold tests to determine which security and monitoring features should remain open source versus becoming part of their paid X-Pack extension.
3. Price Sensitivity Analysis
Understanding price elasticity helps optimize revenue without sacrificing adoption.
The experiment: Use the Van Westendorp Price Sensitivity Meter method:
- Survey users on what they consider too expensive, expensive but worth it, a bargain, and too cheap
- Map the optimal price range based on intersections
- Segment results by company size and use case
According to Price Intelligently, SaaS companies typically leave 30-40% of potential revenue on the table by not optimizing pricing based on willingness to pay data.
4. Tiered Packaging A/B Tests
Optimizing your tier structure can dramatically impact average contract value and conversion rates.
The experiment: Create two or more tier structures with:
- Different feature distributions across tiers
- Varied pricing gaps between tiers
- "Good-better-best" versus use-case specific packages
GitLab has continuously optimized their tier structure, evolving from a simple free/paid model to a sophisticated multi-tier approach that increased their average deal size by over 200% in three years.
5. Annual Discount Testing
Finding the optimal incentive for annual commitments is especially important for open core models.
The experiment: Test different annual payment incentives:
- Vary discount percentages (10%, 15%, 20%, etc.)
- Test "months free" versus percentage discounts
- Experiment with limited-time discount offers
HashiCorp found that a two-month free incentive (16.7% effective discount) outperformed a straight 15% discount for annual commitments, despite being mathematically similar.
Practical Implementation Tips
When running these pricing experiments, consider these best practices:
Segment your experiments - Different user segments (enterprise vs. SMB, different verticals) may respond differently to pricing changes.
Use cohort analysis - Compare conversion and retention metrics between experiment groups over time, not just at the point of purchase.
Be transparent with your community - Open core communities value transparency, so communicate the reasoning behind pricing changes.
Test one variable at a time - Avoid confounding results by changing multiple pricing elements simultaneously.
Consider grandfathering - For significant price increases, consider grandfathering existing customers to maintain goodwill.
Measuring Success Beyond Conversion Rates
While conversion rates are an obvious metric, successful pricing experiments should be measured across multiple dimensions:
- Average Revenue Per User (ARPU) - Are users selecting higher-value plans?
- Customer Acquisition Cost (CAC) - Has the sales cycle shortened with optimized pricing?
- Net Revenue Retention - Are customers expanding usage over time?
- Community Growth - Has pricing affected open source adoption?
According to OpenView's Product Benchmarks, companies that optimize pricing see a 10-15% improvement in net revenue retention on average.
Getting Started With Your First Experiment
The most effective first experiment for most open core companies is feature value testing combined with a basic price sensitivity analysis. This foundation helps ensure you're monetizing the right features at approximately the right price point before fine-tuning with more sophisticated experiments.
Start small, with a subset of new prospects, rather than rolling out pricing changes to your entire user base. Document your hypotheses clearly before each test and be prepared to iterate based on results.
Remember that pricing optimization is not a one-time project but an ongoing process. The most successful open core companies, like Elastic, MongoDB, and GitLab, run pricing experiments quarterly, continuously refining their approach as they scale.
By systematically testing these key pricing variables, you'll build a monetization engine that respects your open source roots while maximizing the commercial potential of your premium offerings.