The Aiven blog article delves into the performance characteristics of Diskless Kafka topics, a new approach in Apache Kafka that stores data directly in object storage like AWS S3, offering significant cost savings and limitless retention with strong durability. This method flips the traditional Kafka architecture by bypassing local disk storage and using a batch coordinator, such as PostgreSQL, for metadata management. While this setup achieves extreme durability and reduces operational complexity, it introduces latency due to the need for batching and object storage uploads, making it more suited for high-throughput workloads. The article emphasizes that while Diskless Kafka topics offer compelling advantages, including a potential 80% reduction in the total cost of ownership, they are not direct replacements for traditional Kafka topics due to their unique performance trade-offs. Performance testing reveals challenges with single producer scenarios, but scaling with multiple producers and partitions can mitigate latency spikes. The article also highlights the importance of optimizing the batch coordinator, especially as partition counts increase, and provides tuning recommendations for various deployment scenarios. As Diskless Kafka topics continue to evolve, understanding their trade-offs is crucial for making informed architectural decisions.