Unifying AI credit systems for Vyond, an AI-powered video creation tool
✦ AI summary
Adam designed a unified AI credit system to replace a fragmented, per-feature quota system that was causing user frustration and operational inefficiency.
The core solution involved creating a single “currency” used across all AI tools (like text-to-video and AI avatars). Key design elements included a scalable UI component with dynamic states, a ledger-style usage tracking page, and a redesigned pricing table that used credit allowances to differentiate subscription tiers.
The project delivered significant business results, including a $50K increase in Monthly Recurring Revenue (MRR) and over $1M in qualified sales within two months. By simplifying usage patterns, the system improved feature engagement and led to a 10% increase in Enterprise plan adoption.
I designed a unified AI credit currency to replace a fragmented quota system, improving user flexibility and simplifying API cost management.
As we expanded our suite of AI-powered tools at Vyond, we launched each new feature with its own isolated quota. This frustrated our users by limiting flexibility, and internally made our API cost management complex and inefficient to track.
To solve this, I designed a unified AI credit system — a single, reusable currency across all AI features. This empowered our users to allocate credits based on their specific needs while giving us a consistent metric to monitor API spending.
The new system resolved user friction and became the foundational framework for a major plan restructure, significantly improving the differentiation between our subscription tiers.
Overall, the initiative drove a $50K increase in MRR and generated over $1M in qualified sales within two months.
The AI credit system was launched alongside restructured plans in May 2025. The new Enterprise plan, featuring unlimited credits, saw a 10% increase in adoption. Across all users, AI features engagement rose, with most consuming less than 50% of their monthly credits, showing both increased use and sustainable limits.
I designed an AI credit system with a scalable, reusable component for multiple AI features, a credit usage page, and a new pricing table highlighting credits as a key plan differentiator.
Users can share the same credits across AI features via a consistent UI pattern
I designed a credit component placed next to each AI feature's call-to-action button, using a coin icon to visually represent the credit system and maintain consistency across AI tools.
The design was lightweight and scalable, making it easy to reuse for future AI features.
Users can make sense of credits through contextual component states
The credit component integrates with the backend to sync data in real time across the platform. I designed multiple states to handle various features and API scenarios, including:
- Syncing and displaying current credit balance
- Calculating and showing dynamic credit costs
- Triggering error messages when issues occur
- Prompting upsell messages when credits are low
Users can track their monthly credit usage through a centralised page
I designed a ledger-style page that records every credit transaction in chronological order, providing transparency and building trust in the system.
This visibility helps reduce user anxiety around spending and empowers users to plan future usage.
Users can clearly see plan differences through credits in the new pricing table
I redesigned the pricing table to spotlight credit allowances, using a side-by-side layout to highlight value differences across plans.
The Enterprise plan featured unlimited credits, creating a compelling upsell narrative and making plan differentiation more transparent to users.
From an animation tool to an all-in-one AI video creation platform.
In recent years, driven by the rapid development of Artificial Intelligence, Vyond evolved from a traditional animation platform into an all-in-one AI video creation suite.
While Vyond invested heavily in developing AI capabilities for the video creation workflow - including features like text-to-video, text-to-speech, text-to-image, video translation, and AI avatars, each feature was launched with its own quota system, causing problems in the long-term.
This project aimed to solve these problems.
The cost of fragmented AI: Inefficient tracking and lost upsell opportunities.
As more and more quota-based AI features were added, maintaining the separate quota system across three to four plan tiers became increasingly difficult and inefficient. The fixed quota system also made it harder for us to understand the actual usage patterns.
Additionally, fragmented usage data complicated analysis and financial planning for third-party API costs.
Finally, the legacy pricing model lacked usage-based differentiation, limiting upsell opportunities for the Sales team.
User friction from inflexible quotas and confusing plan tiers.
Given the quota system, some AI feature usage were low, when compared to others. Users couldn’t transfer unused quota from one feature to another, causing inflexibility.
It was also not easy for users to keep track of the individual quota on each AI feature, causing inefficiency.
Moreover, we’ve also found out that prospective customers struggled to compare and evaluate plans when quotas were inconsistent.
In short, users need a way to use AI feature quotas flexibly and efficiently as well as a simpler way to know which plan best fit their needs.
Designing a scalable UI pattern across diverse AI features
When designing the AI credit system, I faced the challenge of supporting five distinct AI features, each with its own API and interface.
My goal was to create a single, scalable UI pattern that could adapt to future features as well.
Through stakeholder workshops and technical reviews, we aligned on placing the credit component next to each feature’s call-to-action button — an intuitive and flexible location. A coin icon was used to visually signal credit usage.
I then defined comprehensive component states and display logic to ensure seamless implementation. These included syncing balances, calculating dynamic costs, handling errors, and prompting upsell messages — all designed for consistency and future reuse across the platform.
Why we rejected a persistent global credit display and aggressive upselling to prioritise user trust and focus.
I initially explored a persistent, global credit display, a pattern common in AI-native products. However, because Vyond’s core value isn’t solely AI generation, a persistent display commanded too much real estate and attention. We discarded this to avoid misrepresenting the tool’s primary purpose and distracting users from their main workflows.
I also tested a more aggressive upsell pattern, leaving the 'Generate' CTA active even when users lacked sufficient credits. We ultimately rejected this approach because it bordered on a dark pattern. It created a frustrating user experience and risked damaging trust, particularly with our enterprise users. We chose to prioritise transparency instead.
Overall, the AI credit system boosted $50K in MRR growth and $1M in qualified sales, supporting the new pricing in May 2025.
Interested in working together? Reach out at y3vu060312@gmail.com or connect on LinkedIn.