WarpStream Blog

Eternal Ltd., the company behind Zomato, Blinkit, Hyperpure, and District, replaced self-hosted Apache Kafka® with WarpStream for logging and observability, cutting costs by 60%. By also swapping managed Kafka for WarpStream in its benchmarking pipeline, Eternal saved an additional 80% and cut cluster creation time from 30 minutes to seconds, all while auto-scaling to handle workloads that swing from 2.3 GiB/s to 25+ GiB/s throughout the day.

Character.AI built its real-time trust and safety and experimentation pipelines on WarpStream, slashing streaming costs by 85–90% with zero added operational overhead. Here's the story, straight from their data infra lead.

By switching from Kafka to WarpStream for their logging workloads, Robinhood saved 45%. WarpStream auto-scaling always keeps clusters right-sized, and features like Agent Groups eliminate issues like noisy neighbors and complex networking like PrivateLink and VPC peering.

Cursor’s AI-powered IDE fuses creativity and human intelligence. At its data-streaming heart is WarpStream, which makes it possible to train models securely, deliver lighting-fast Tab completions, and scale telemetry with zero ops.

With WarpStream and ClickHouse Cloud, what was once a patchwork of managed services and constant disk resizing has become a cohesive architecture that connects streaming and analytics in a single, resilient pipeline. Asked to sum up the stack in three words, Superwall co-founder and CTO Brian Anglin said, “Durable, scalable, powerful.”

Goldsky’s mission to stream and index massive volumes of blockchain data quickly ran into the scaling and cost limits of traditional Kafka. With tens of thousands of partitions and petabytes of data, their clusters became expensive, fragile, and hard to operate. By migrating to WarpStream, Goldsky cut costs by over 10x, eliminated performance bottlenecks, and unlocked seamless scaling to 100 PiB and hundreds of thousands of partitions, all without the operational headaches of legacy Kafka deployments.

By switching from open-source Apache Kafka to WarpStream, ShareChat was able to implement zero-ops auto-scaling and saved 60% vs. multi-AZ Kafka. They also shared some best practices for optimizing WarpStream.

"...we needed to ensure WarpStream could also support our scalability needs before settling on it. Given the size of Grafana Cloud Metrics, we knew it needed to handle read and write rates of tens of gigabytes per second."