Customer Highlights
How Langtrace AI Built Their Self-Serve Billing in Hours on Maple
We have been working closely with the team at Langtrace AI, an open-source observability tool that collects and analyzes traces and metrics to help you improve LLM applications.
Through a seamless integration with Maple, within hours, Langtrace AI built their self-serve pricing motion that included comprised of a usage-based component for the number of traces along with a seat-based component.
- Time to Build
- ~6 hours
- Engineering Hours Saved
- ~100 hrs
Challenge
Langtrace AI was looking to build a usage-based plan for their product along with a prorated seat-based component for their hosted offering. This meant that Langtrace AI would have to take on the engineering lift to build and manage usage-data and seat-data for their customers at scale and periodically roll up that information for billing.
Knowing the importance of flexibility, Langtrace AI also aimed to support future iterations of their pricing plans. However, managing intricate billing scenarios along with experimentation for pricing and packaging would become overly cumbersome for their engineering team and take away valuable cycles from the momentum of their core product.
Solution
Recognizing the challenge, Langtrace AI leveraged Maple's Customer APIs to quickly import their existing customers into Maple. They then quickly integrated with Maple's They then integrated with Maple's Objects API to enable seat-based pricing along with Maple's Usage Events API to enable usage-based billing for the number of traces.
"Maple has a highly flexible and customizable billing product with a clean developer experience. It took us only a few hours to setup a self serve subscription system for Langtrace."
Results
- Time to Build
- ~6 hours
- Engineering Hours Saved
- ~100 hrs
Through this partnership with Maple, Langtrace AI quickly launched their usage-based pricing model with a seamless contract signature flow.
- Quick Self-Serve Launch: Langtrace AI's team went from worrying about the implementation of billing to focusing on their core launch plans. Within hours, they had an implementation for usage-based and seat-based billing.
- Readiness for Sales-Led: Langtrace AI set themselves up for the future by deploying Maple as a unified solution that automates their revenue operations for self-serve and sales-led deals. All along with a flexible engineering system to quickly iterate on their billing features with minimal code changes.
- Reduced Costs: Langtrace AI saved on weeks of engineering time to implement a usage-based and seat-based system on their own. They also no longer have to purchase additional billing stack tools such as acontract-management solution for bigger deals or a metrics solution to track their core business metrics and pay steep percentage of volume fees.
Conclusion
By leveraging Maple's holistic billing capabilities, Langtrace AI not only addressed the immediate pricing needs but also positioned itself for sustained pricing iterations in the future without the heavy engineering lift.
Check out the benefits of our flexible Usage Events APIs and Objects API to quickly execute on modern pricing plans with Maple.