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Kafka Infrastructure Cost Optimization

2 posts · May 2017 – Aug 2026

What this trail covers

Confluent Cloud continues to evolve its cost optimization features for Kafka, focusing on reducing data transfer costs, providing better cost control mechanisms, and enabling more flexible scaling. This includes advancements in follower fetching, Private Network Interface (PNI) networking, enhanced metrics for understanding eCKU scaling drivers, client quotas for Enterprise and Freight clusters, and new 'Queues for Kafka' functionality to consolidate messaging infrastructure. The goal is to enable more efficient and cost-effective Kafka operations through serverless autoscaling (eCKUs) and optimized resource utilization.

All Confluent trails
  1. New in Confluent Cloud and WarpStream: Evolving the Data Streaming Platform for AI, Scale, and ControlThis post details Confluent Cloud's ongoing efforts to optimize Kafka infrastructure costs. It highlights the expansion of Freight clusters to support transactions and idempotent produce for cost efficiency, increased scale limits for Enterprise and Dedicated clusters (up to 32 eCKUs and 252 CKUs respectively), and the enablement of automatic Private Network Interface (PNI) setup to reduce data transfer costs. New metrics in the Metrics API are introduced to reveal key drivers of eCKU scaling, aiding client quota decisions and capacity management. The release priority feature for Dedicated clusters is also mentioned as a way to improve production confidence during upgrades.
  2. Confluent Cloud Q1 2026: Queues for Kafka, KCP for migrationIntroduces 'Queues for Kafka' (KIP-932) with 'share group' and 'share consumer' abstractions to enable multiple consumers to cooperatively process messages from the same topic, allowing elastic scaling independent of partition count. This consolidates traditional queuing workloads onto Kafka. Also introduces KCP (Kafka Cloud Platform) CLI tool to automate migration from hosted/open-source Kafka to Confluent Cloud, including discovery, provisioning, data migration, and client migration. Enhances scalability by allowing serverless clusters to configure maximum eCKUs for cost control and Enterprise clusters to scale up to 32 eCKUs. Extends client quotas to Enterprise and Freight clusters for precise ingress/egress throughput limits on principals. Enables Fetch-from-follower for Enterprise clusters to reduce cross-AZ traffic and egress costs on AWS.
  3. Cut the Costs of Hosted Kafka with Confluent Cloud’s Price GuaranteeThis post details how Confluent Cloud's Kora Engine, elastic autoscaling (from zero to GBps+), Freight Clusters (reducing cross-AZ networking fees by up to 90%), and Private Networking Interface (PNI) offer significant cost savings (60% or more) and reduced operational toil compared to hyperscaler-hosted Kafka services. It highlights the avoidance of overprovisioning, hidden networking costs, and manual maintenance tasks, emphasizing Confluent's truly fully managed and serverless approach with a 99.99% uptime SLA. The post also contrasts Confluent's flexibility across environments with hyperscaler vendor lock-in.
  4. Confluent Cloud Q3 '25 Unlocks Cost-Effective Data StreamingThis post introduces Private Network Interface (PNI) for Confluent Cloud Enterprise and Freight clusters on AWS, a new networking option that leverages AWS Elastic Network Interfaces (ENIs) within customer VPCs. PNI aims to reduce cloud networking costs by 20-50% at scale compared to PrivateLink, while providing granular traffic control via existing security groups. It also announces increased client connection limits for Enterprise and Freight clusters, quadrupling the limit for Enterprise (to 18,000 connections per eCKU) and doubling it for Freight, along with a doubling of connection attempts per second. These changes are designed to optimize scalability and cost efficiency for microservices and hub-and-spoke architectures.
  5. Introducing Private Network Interface (PNI) on AWS: Lower Kafka Networking Costs by 50%Introduces Private Network Interface (PNI) on AWS for Confluent Cloud Enterprise and Freight clusters. PNI leverages AWS Elastic Network Interfaces (ENI) for secure, private connectivity directly within customer VPCs, offering a cost-effective alternative to VPC peering and PrivateLink. It reduces networking costs by approximately 22% for Enterprise clusters and up to 50% for Freight clusters by eliminating cross-AZ data transfer fees and PrivateLink endpoint charges. The post quantifies savings with a logging workload example and highlights how zone-aligned architecture and follower fetching contribute to cost reduction.
  7. Use Freight Clusters and Lambda for Cost-Effective Streaming PipelinesThis post details the integration of Confluent Cloud Freight clusters with AWS Lambda for building cost-effective, high-throughput streaming pipelines. It demonstrates how Freight clusters, optimized for relaxed latency and lower cost, can ingest high-volume data like e-commerce web clicks. AWS Lambda, used with Event Source Mapping (ESM) in Provisioned Mode and VPC connectivity, handles downstream processing, database lookups, and customer notifications. The solution leverages private ENI-based connectivity between Freight clusters and Lambda for enhanced security and cost savings. Key technical aspects include configuring Lambda ESM for Kafka, using filtering capabilities, and accessing private RDS instances.
  8. Confluent Cloud Freight Clusters in GAThis post announces the General Availability of Confluent Cloud Freight clusters on AWS. Freight clusters are designed for high-throughput, latency-insensitive workloads, offering up to 90% lower infrastructure costs compared to self-managed Kafka. Key technical advancements include autoscaling to optimize resource utilization and avoid over-provisioning, and a unique architecture that eliminates expensive cross-AZ charges by writing data directly to object storage (e.g., S3) and bypassing local storage replication. This 'direct write' mode trades latency (up to a second or two) for reduced network costs. Freight clusters also leverage Fetch From Follower to intelligently retrieve data from followers across availability zones, further removing cross-zone traffic costs when clients are configured with zone-aware produce and consume. The post also highlights the integration with Private Networking Interface (PNI) for cost-effective and secure VPC interconnection. Freight clusters utilize Elastic CKUs (eCKUs) and can auto-scale from 2 eCKUs up to 152 eCKUs (over 30 GBps throughput).
  9. Kafka Scaling Reinvented with Elastic CKUsThis post introduces Elastic Confluent Units (eCKUs) powered by the Kora engine for serverless Kafka clusters. It details how eCKUs enable instantaneous, automatic scaling up and down based on demand, eliminating the need for manual capacity planning, broker provisioning, and partition rebalancing. The post contrasts this with pre-provisioning for peak demand and scaling for predictable demand, highlighting cost savings in infrastructure, development, and operations personnel, and reduced downtime risk. It quantifies the benefits using a food delivery service workload profile, emphasizing the 'always right-sized' nature of serverless clusters.
  10. Uncovering Kafka’s Hidden Infrastructure CostsThis post, the first in a four-part series, focuses on the infrastructure costs of running Kafka. It highlights that networking, particularly cross-AZ traffic, is often the largest infrastructure cost, frequently exceeding 50% of the total. The post details how compute, storage, and networking costs are calculated for sample workloads, emphasizing the significant impact of cross-AZ data transfer due to replication and producer/consumer traffic. It also briefly mentions additional tooling costs and the potential savings offered by Tiered Storage (KIP-405) and Confluent Cloud's cloud-first storage engine. The importance of optimizing resource utilization is also stressed.
  11. How Michelin Cut Kafka Costs by 35% Using ConfluentThis post details Michelin's migration from self-managed on-prem Kafka to Confluent Cloud, resulting in an estimated 35% cost savings. The migration addressed the operational complexity and scaling challenges of self-managing Kafka, allowing Michelin to offload management responsibilities and redirect technical resources to innovation. The company also experienced increased reliability with Confluent Cloud's SLA and improved inventory visibility, streamlining their supply chain.
  12. From Apache Kafka to Confluent Cloud: Optimizing for Speed, Scale, and StorageThis post details Confluent's experience optimizing Apache Kafka for Confluent Cloud, focusing on performance, price, and availability. It outlines three guiding principles: understanding user workloads and planning for them using simulation frameworks like Trogdor; recognizing the importance of infrastructure (hardware, OS, JVM) and its impact on cost and performance; and the necessity of effective observability. The post also discusses architectural changes that have led to significant performance gains over self-managed Kafka, including hardware selection based on performance, price, and capacity, OS tuning (e.g., io_uring, dirty_background_bytes), and JVM optimization with controlled rollouts via feature flags. Nightly performance tests are used to gate new releases and monitor for regressions.
  13. Tail Latency at Scale with Apache KafkaThis post provides a detailed breakdown of the five components of end-to-end latency in Apache Kafka: produce time, publish time, commit time, catch-up time, and fetch time. It explains how client configurations (e.g., linger.ms, batch.size, max.inflight.requests.per.connection, compression.type) and broker configurations (e.g., replica.fetch.min.bytes, replica.fetch.wait.max.ms) influence these stages. The post also clarifies the relationship between producer/consumer latencies and end-to-end latency, and discusses strategies for bounding tail latencies by managing micro-bursting and head-of-line blocking.
  14. Optimizing Your Apache Kafka® Deployment | ConfluentThis post introduces the concept of optimizing Apache Kafka deployments by tuning configuration parameters based on specific service goals: throughput, latency, durability, and availability. It highlights the tradeoffs between these goals and provides a list of key producer, consumer, streams, and broker configuration parameters that can be adjusted. The post emphasizes the importance of benchmarking and monitoring using Confluent Control Center to validate these optimizations for specific use cases and hardware profiles.