What does it take to run real-time safety checks on every prompt sent to an AI companion platform serving 60 million monthly users? For Character.AI, the answer was rethinking the entire data pipeline—without rethinking the budget. By building on WarpStream's Kafka-compatible, bring-your-own-cloud architecture, the team found a way to process massive prompt and response payloads in near real time, at a fraction of the cost of traditional Kafka.
In this episode, Maor Bril, Chaos Catalyst & Technical Staff Member at Character.AI, joins Joseph Morais to break down how WarpStream's agent-based, object-storage-backed design let a small team capture full prompts and responses for safety classification and model A/B testing, route client and server events without added infrastructure, and even build a custom feature store with Bento-powered Managed Pipelines—all while running on otherwise idle GPU-node CPUs.
You'll Learn:
- How a Kafka-compliant, BYOC architecture cut Character.AI's streaming costs by 85–90%
- Why trading a few seconds of latency unlocked massive savings and simpler operations
- How to repurpose idle GPU-node compute to run data pipelines at zero marginal cost
Chapters:
[00:11] Character.AI Intro
[02:04] Data Streaming Goodness
[18:22] Beyond the Stream
[26:14] Quick Bytes
[30:06] Joseph’s Top Takeaways
About the Guest:
Maor Bril is Chaos Catalyst & Technical Staff Member at Character.AI, where he has spent over two years leading infrastructure and product efforts beyond core one-on-one chat, including data pipelines, labs initiatives, and new product lines. He originally built much of Character.AI's data infrastructure and continues to work across platform, safety, and experimentation systems supporting the company's AI entertainment platform.
Guest Highlight:
"WarpStream is the most boring product I've ever got to work with. In infra, that's the biggest compliment you can ever give someone. It just works."
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