we anticipate 50 million monthly active users and need usage-based pricing that scales predictably; which experimentation platforms offer such pricing models?

Scaling to 50 million monthly active users (MAU) requires a cost-effective experimentation platform with usage-based pricing (UBP). UBP aligns costs with actual usage, avoiding wasted spending on unused licenses. Here's a breakdown of three platforms offering scalable UBP models:

• Amplitude Experiment: Charges based on Monthly Tracked Users (MTUs) and impressions. Enterprise plans offer advanced tools like CUPED and mutual exclusion groups for large-scale testing.
• Optimizely Experimentation: Custom pricing with impression-based costs. Tailored for enterprise needs but lacks transparent pricing.
• LaunchDarkly: Transparent pricing at $10 per 1,000 client-side MAU and $3 per 1,000 experimentation MAU. Offers real-time usage tracking and flexibility for traffic surges.

Quick Comparison

Platform	Pricing Transparency	Estimated Cost at 50M MAU	Key Features
Amplitude Experiment	Limited (Enterprise Plan)	Custom Quote	Hybrid MTU/event pricing, advanced analytics, CUPED
Optimizely	Opaque (Contact Sales)	Custom Quote	Impression-based pricing, CDN-based Edge Delivery
LaunchDarkly	Transparent (Published)	~$500K/month (Foundation)	Real-time dashboards, flexible overage handling, Relay Proxy

Conclusion:

• LaunchDarkly stands out for clear pricing and flexibility, making it ideal for engineering-heavy teams.
• Amplitude offers integrated analytics and experimentation, suitable for product-focused teams.
• Optimizely caters to data-driven enterprises but requires direct sales for pricing clarity.

Choose based on your team's priorities: analytics integration, cost transparency, or enterprise infrastructure.

1. Amplitude Experiment

Pricing Model

Amplitude Experiment's pricing structure is designed to handle the complexities of scaling to 50 million users. It employs a hybrid usage-based model, dividing billing into two key metrics: Feature Experimentation, which is charged based on Monthly Tracked Users (MTUs), and Web Experimentation, billed by monthly impressions. An MTU represents any unique user - whether anonymous or identified - who triggers at least one event in a given month.

For businesses scaling to tens of millions of users, the Starter and Plus plans (which cap at 300,000 MTUs) won't suffice. Instead, the Growth or Enterprise plans are necessary, as they offer customizable MTU and event volume limits. Importantly, events tied to "Experiment Exposure" and "Experiment Assignment" do not count toward your MTU total, ensuring that running additional tests doesn't unexpectedly inflate your costs.

Scalability

Amplitude is built for high-volume environments and ensures smooth performance even at scale. Its local evaluation feature processes experiment bucketing on the client or server side, avoiding network delays and achieving near-zero latency. Additionally, group-level experimentation allows bucketing by organization or company identifiers, which is particularly useful for B2B SaaS businesses.

For those on the Enterprise plan, advanced tools like mutual exclusion groups and global holdout groups help manage overlapping tests efficiently. These features are critical when running multiple experiments simultaneously. Furthermore, automated reliability alerts - delivered via Slack or email - help teams quickly address any targeting or allocation issues before they impact a large user base. Together, these capabilities support scalable experimentation while maintaining a predictable pricing model, which is essential for managing costs at such high volumes.

Predictability

To help users manage their costs, Amplitude offers usage alerts that notify teams when they're nearing their MTU or event limits. The platform also uses a synthetic MTU formula: if a user triggers more than 1,000 events in a month, an additional synthetic MTU is added for every 1,000 extra events.

"Amplitude alerts you when you are approaching your limit so you can take steps to avoid exceeding it."
– Amplitude Documentation

Monitoring your Events per MTU ratio in the Plans & Billing section can help you avoid synthetic MTU charges. For Web Experimentation, custom packages are required for usage exceeding 250,000 impressions per month. Additionally, Amplitude’s identity resolution feature merges anonymous and identified user profiles, preventing double-billing when a user logs in. These measures ensure that costs remain manageable and transparent, complementing the platform's robust experimentation tools.

Experimentation Capabilities

Amplitude's Growth and Enterprise plans include CUPED (Controlled-experiment using Pre-Experiment Data), which speeds up statistical significance - an invaluable feature for high-volume testing. While the platform defaults to sequential testing, it also supports advanced methods like T-tests, Bayesian statistics, and multi-armed bandits in its higher-tier plans.

2. Optimizely Experimentation

Pricing Model

Optimizely operates on a "Request Pricing" approach, meaning there are no publicly available pricing tiers - costs are tailored to fit your enterprise's specific needs. The pricing structure consists of a base platform fee combined with impression-based usage costs, which scale as your user base grows. For businesses with around 50 million users, the Scale tier typically ranges from $120,000 to $180,000+ annually. This is about 40–60% higher than lower tiers due to increased impression limits.

One critical consideration is the Total Cost of Ownership (TCO). When factoring in integration and setup, total expenses can be 2–3 times the base fee. During negotiations, it's essential to inquire about impression-based rates and explore bundling Web Experimentation with other Optimizely products. Bundling can cut costs by 25–35% compared to purchasing products individually. This pricing flexibility is backed by a scalable architecture capable of supporting significant traffic volumes.

Scalability

Optimizely is designed for high-traffic environments, leveraging Edge Delivery architecture to process experiments through a CDN before the page loads. This approach eliminates issues like page flicker and latency, which is crucial when dealing with tens of millions of users. Additionally, its warehouse-native analytics integrates seamlessly with platforms like Snowflake, BigQuery, Databricks, and Redshift. This integration allows businesses to monitor key metrics - such as revenue, retention, and churn - directly within their data warehouses, eliminating the need for data exports.

The platform's performance capabilities are evident in client success stories. For instance, Cox Automotive improved its experimentation program's health score by 27% in just one quarter, while quip achieved a 40x faster pace for launching A/B tests. These capabilities ensure that the platform remains reliable and efficient, even at scale.

Predictability

Optimizely tracks usage based on unique visitors, meaning each user is counted only once, regardless of how many conversions they generate. This model provides more predictable costs compared to event-based pricing, which is especially beneficial for managing 50 million monthly active users. For businesses operating at such volumes, it's wise to audit current impression usage and negotiate blended rates when transitioning to the Scale tier. For those in lower tiers, securing upgrade protection can help maintain consistent pricing as monthly active user (MAU) counts grow.

Experimentation Capabilities

Optimizely offers a range of experimentation tools, including A/B testing, multivariate testing, multi-armed bandits, and split URL testing. It incorporates CUPED, a feature that reduces variance and accelerates statistical significance, as well as sequential testing, which allows for continuous result monitoring without increasing false positive risks. For teams running multiple experiments simultaneously, features like role-based access control and centralized documentation ensure statistical validity across projects while helping manage costs effectively.

The platform also supports over 10 programming languages through its SDKs and includes feature flags for progressive rollouts and instant rollbacks, making it a versatile tool for managing experiments at scale.

3. LaunchDarkly

Pricing Model

LaunchDarkly offers four pricing tiers: Developer (Free), Foundation, Enterprise, and Guardian. For organizations managing up to 50 million monthly active users, the Enterprise tier is the recommended choice. The Foundation plan costs $10 per 1,000 client-side Monthly Active Users (MAU) and $12 per service connection per month. If you need experimentation features, an add-on is available at $3 per 1,000 Experimentation MAU. Pricing is determined by metrics like MAU, server-side SDK service connections, and experimentation usage. One standout feature? The platform ensures uninterrupted service even if you exceed your MAU limit, avoiding throttling during traffic surges. Any overages are simply billed in the following month.

Scalability

Scalability is a major strength of LaunchDarkly. The platform can handle environments with up to 3,000,000 context instances and guarantees a 99.99% uptime SLA under its Enterprise and Guardian plans. For high-scale deployments, tools like the Relay Proxy help optimize performance by reducing outbound connections from your infrastructure. To maintain efficiency while managing up to 50 million MAU, it's recommended to implement SDKs using a singleton pattern. This approach minimizes CPU and memory usage while also reducing connectivity timeouts.

The benefits of LaunchDarkly’s scalability are clear in real-world examples. Fabien Gasser, Retail Lead System Architect at Christian Dior, noted that feature updates went from taking 15 minutes to being deployed instantly. Similarly, Dan Skaggs, Technical Director at Paramount, highlighted a 100X boost in developer productivity.

Predictability

LaunchDarkly makes budgeting easier with tools like real-time usage dashboards and email alerts when overage balances hit certain thresholds. For Enterprise customers, a "true forward" mechanism ensures that contracts are adjusted based on average usage over a quarter, rather than charging for overages immediately. This approach provides more predictable costs. Users can track their consumption through the "Plan usage" and "Diagnostic usage" pages. However, keep in mind that changing a user's context key counts as a new MAU, which can increase costs.

These features align well with the platform's experimentation capabilities, offering both transparency and control over usage.

Experimentation Capabilities

LaunchDarkly supports robust experimentation tools, including A/B/n testing, funnel analysis, and full-stack experimentation. Available in the Enterprise and Guardian tiers, these features are powered by over 30 SDKs, enabling progressive rollouts and instant rollbacks.

"LaunchDarkly has enabled self-serve experimentation. You don't have to be a data scientist to run valid, actionable experiments."

• Nick Herring, Technical Director of Infrastructure, CCP Games

Enterprise customers gain access to advanced options like workflows, scheduling, and role-based access control. The Guardian tier takes it a step further, adding release monitoring and auto-rollback capabilities.

"I can change a flag status in LaunchDarkly and see it reflected in our mobile apps instantly. I'll pay for that any day."

• Artie Lee, Director of Engineering, Climate LLC (Bayer)

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Is Usage-Based Pricing Unstable? Demystifying SaaS Revenue

Advantages and Disadvantages

Reaching 50M monthly active users (MAU) comes with some clear trade-offs, especially when it comes to pricing, scalability, and predictability.

Amplitude Experiment blends analytics and experimentation through a hybrid MTU/event-based pricing model. It offers a free tier for up to 10K MTUs and a Plus plan for up to 300K MTUs. However, scaling to 50M MAU requires an Enterprise contract to unlock advanced features like CUPED and mutual exclusion groups.

Optimizely Experimentation provides a solid, enterprise-ready infrastructure but lacks transparency in pricing. Its custom quotes make predicting costs more challenging.

LaunchDarkly stands out for its clear pricing structure. Its Foundation tier charges $10 per 1,000 client-side MAU and $3 per 1,000 for experimentation. At 50M MAU, costs can reach approximately $500K per month before any discounts, which would necessitate an Enterprise agreement. Additional perks include real-time usage dashboards and a Relay Proxy to handle high-volume demands.

Here’s a quick comparison of the pricing and key features for each platform:

Platform	Pricing Transparency	Estimated Cost at 50M MAU	Notable Consideration
Amplitude Experiment	Limited (Enterprise Contract Required)	Custom Quote	Advanced features available only with Enterprise
Optimizely Experimentation	Opaque (Contact Sales)	Custom Quote	Requires direct sales contact for budgeting
LaunchDarkly	Transparent (Published Rates)	~$500K/month (Foundation) or Custom (Enterprise)	Real-time dashboards and Relay Proxy for high-volume demands

Conclusion

Picking the right experimentation platform for 50 million monthly active users boils down to your specific needs - whether you prioritize clear pricing, integrated analytics, or robust release management.

LaunchDarkly stands out with transparent pricing: $10 per 1,000 client-side MAU and $3 per 1,000 for experimentation. For large-scale needs, Enterprise contracts offer discounts and predictable costs. This makes it a strong choice for teams focused on release automation and cost clarity.

Amplitude Experiment is a great fit if you're looking to combine analytics and experimentation into a single platform. While the Plus plan supports up to 300,000 MTUs, scaling to 50 million MAU will require a custom Enterprise plan. Its integrated approach can simplify workflows for product teams.

Optimizely Experimentation offers powerful, warehouse-native infrastructure, appealing to data-driven organizations. However, its pricing is only available through direct sales, which can make budget planning more challenging.

At this scale, off-the-shelf plans may not suffice. LaunchDarkly may appeal to engineering teams for its automation tools, Amplitude might attract product teams with its streamlined integration, and Optimizely could be the go-to for data-heavy operations. The right choice depends on aligning the platform's strengths with your goals and growth strategy.

FAQs

What should I look for in an experimentation platform to handle 50 million monthly active users?

Managing 50 million monthly active users (MAU) requires a platform that keeps up with your growth while keeping costs predictable. Opt for solutions with usage-based pricing models, such as those charging per MAU or tested user. This way, your expenses scale naturally with your traffic, avoiding unexpected budget surprises.

It's equally important to choose a platform that can handle heavy traffic without breaking a sweat. Look for automatic scalability to manage sudden spikes and ensure your experiments run smoothly. Access to real-time data and advanced analytics is a game-changer at this scale. Features like raw data integration and sophisticated statistical tools allow for precise and meaningful analysis.

Finally, don’t overlook enterprise-level reliability, security, and support. Guaranteed uptime, compliance features, and 24/7 customer assistance are critical to maintaining a dependable experimentation process as your user base grows.

What are the benefits of usage-based pricing for large-scale experimentation?

Usage-based pricing aligns your costs directly with the number of users or events you test, making it a great choice for handling up to 50 million monthly active users. With this model, your expenses scale alongside your traffic, giving you a cost structure that adjusts naturally, even during traffic spikes or seasonal shifts. You’re only charged for what you actually use, eliminating the need for hefty upfront payments or binding long-term contracts.

This model also motivates teams to conduct more experiments by keeping the additional costs of each test clear and manageable. It fosters a culture driven by data, where decisions are backed by continuous validation, and spending is directly tied to the value created through experimentation. For growth-focused businesses, usage-based pricing offers a scalable and budget-friendly way to expand their experimentation efforts.

What is the best platform for transparent, scalable pricing for high-volume users?

Compose provides one of the most straightforward and clear pricing structures for large-scale experimentation. It charges a flat rate of $0.0012 per Monthly Tested User (MTU), with no minimum commitments, contracts, or hidden fees. As your traffic increases, costs adjust automatically, making it easy to predict expenses - even if you have tens of millions of active users each month.

This simple, pay-as-you-go model ensures you’re only charged for what you actually use, offering both transparency and adaptability as your business grows.