Bring your Kafka questions to booth #407. 👋 AutoMQ is heading to Current San Francisco on November 4–5! If Kafka costs, scaling, or migration are on your mind, stop by and talk through your setup with our team. 📍 Booth #407 · Moscone West, San Francisco 📅 November 4–5, 2026 🎟️ Comment on and repost this post for a chance to win a Current pass. We'll select a few lucky participants to receive standard admission passes. See you at #407! Event details and complimentary standard registration: Current San Francisco #AutoMQ #ApacheKafka #Current2026 #DataStreaming

Moving Kafka to the cloud doesn't automatically lower the bill. Growing storage, capacity reserved for traffic peaks, and daily cluster maintenance can keep costs high. AutoMQ addresses these costs with cloud-native Diskless Kafka: data lives in object storage, while compute and storage scale independently. • Retain more data without adding compute just to accommodate storage growth. • Adjust compute as traffic rises and falls, reducing idle capacity between peaks. • Automate data rebalancing to reduce manual work as the cluster scales. AutoMQ combines this architecture with Kafka protocol and ecosystem compatibility, so teams can keep using their existing Kafka clients and tools. Watch our short introduction to see how it changes Kafka storage, scaling, and day-to-day operations. For more Kafka and AutoMQ engineering insights, follow AutoMQ on LinkedIn or join the AutoMQ Slack community: go.automq.com/slack #ApacheKafka #AutoMQ #CloudNative

We’re excited to share that AWS has officially published AutoMQ’s Partner Success Story featuring FunPlus. AutoMQ is an AWS Differentiated Partner and is available on AWS Marketplace, so AWS customers can subscribe to AutoMQ directly through their existing cloud procurement workflow. The newly published AWS Partner Success Story highlights FunPlus, a global interactive entertainment company behind games such as State of Survival, King of Avalon, Guns of Glory, and DC: Dark Legion. For gaming platforms running high-throughput Kafka workloads on AWS, cost is often hidden in the network path, not only in broker instances. The FunPlus case shows how AutoMQ on AWS helps reduce that pressure: 1️⃣ Reduce cross-AZ traffic fees by changing the Kafka storage and replication model. 2️⃣ Keep Kafka-compatible APIs so existing producers, consumers, and stream processing jobs continue to work. 3️⃣ Run a cloud-native Kafka architecture on AWS while aligning infrastructure spend more closely with workload growth. 🔎 The result: 𝟓𝟎% 𝐥𝐨𝐰𝐞𝐫 𝐊𝐚𝐟𝐤𝐚 𝐜𝐨𝐬𝐭 by reducing cross-AZ traffic fees. 👉 Read the AWS Partner Success Story: aws.amazon.com/partners/succ… Subscribe on AWS Marketplace: aws.amazon.com/marketplace/p… For more Kafka and AutoMQ engineering insights, follow AutoMQ on LinkedIn or join the AutoMQ Slack community: go.automq.com/slack #Kafka #ApacheKafka #AWS #AWSMarketplace #StreamingData #GamingInfrastructure #AutoMQ

Laravel Cloud now runs AutoMQ with Grafana Labs Mimir 3.0 to power its next-generation observability platform. Here is the story from Florian Beer, Engineering Team Lead, SRE at Laravel. As Laravel Cloud grew, its observability platform had to cover platform services, customer workloads, alerting, and incident response across clusters and nodes worldwide. With hundreds of thousands of metric samples arriving every second, the write path needed to scale without turning Kafka broker disks into the next bottleneck. In the article, Laravel shares how it built that path on AWS with Grafana Mimir 3.0 and AutoMQ: 1️⃣ Mimir ingest storage lets writes land in Kafka before ingesters consume them, so entry writes and backend consumption can scale on separate paths. 2️⃣ A traditional Kafka layer would still bring replicated EBS capacity, broker-to-broker cross-AZ traffic, local log recovery, partition movement, and replica catch-up into the middle of the observability stack. 3️⃣ AutoMQ keeps the Kafka-compatible interface Mimir expects while storing Topic data in S3, so the Kafka buffer follows the same object-storage direction as Mimir long-term blocks. After rollout, Laravel Cloud's metrics volume grew by roughly an order of magnitude, while the entry write path, AutoMQ, and ingester consumption side could still scale independently. 👉 Read the full blog: automq.com/blog/laravel-mimi… For more Kafka and AutoMQ engineering insights, follow AutoMQ on LinkedIn or join the AutoMQ Slack community: go.automq.com/slack #GrafanaMimir #LaravelCloud #Kafka #AutoMQ #DisklessKafka #Observability #AWS

AutoMQ has reached 10K GitHub Stars, making it one of the most widely followed open-source Diskless Kafka projects. This is a milestone we are truly grateful for. Thank you to every developer who starred the repo, tried a deployment, opened an issue, challenged the design, shared feedback, or brought AutoMQ into a real Kafka evaluation. AutoMQ started with a concrete belief: Kafka's protocol, semantics, and ecosystem are worth preserving, but durable stream storage should no longer have to stay tied to broker-local disks in the cloud. From the first public open-source release, to developer questions, community discussions, and production deployments, AutoMQ has been shaped step by step by people who cared enough to look closely. We wrote this blog to share that journey: why we chose Diskless Kafka, how the project earned developer attention, and why more Kafka teams are now asking how the storage model should evolve. 👉 Read the full blog: automq.com/blog/automq-10k-s… If you believe open-source Diskless Kafka is worth more attention, please help share this post so more developers can discover AutoMQ, read the code, and join the discussion. For more Kafka and AutoMQ engineering insights, follow AutoMQ on X.com or join the AutoMQ Slack community: go.automq.com/slack #Kafka #ApacheKafka #DisklessKafka #OpenSource #CloudNative #StreamingData

𝟏𝟎𝐊 𝐆𝐢𝐭𝐇𝐮𝐛 𝐬𝐭𝐚𝐫𝐬 𝐟𝐨𝐫 𝐨𝐩𝐞𝐧-𝐬𝐨𝐮𝐫𝐜𝐞 𝐃𝐢𝐬𝐤𝐥𝐞𝐬𝐬 𝐊𝐚𝐟𝐤𝐚 𝐨𝐧 𝐒𝟑. When we launched AutoMQ v0.6.6 as an open source project in November 2023, we started with one belief: Kafka should become truly cloud-native. Traditional Kafka was built around broker-local disks. At cloud scale, that often means expensive storage, cross-AZ replication cost, heavy partition reassignment, and complex operations around scaling. AutoMQ takes a different path. It is a Kafka-compatible streaming storage engine that moves Kafka storage from local disks to S3-compatible object storage, while keeping the clients, APIs, workloads, and ecosystem tools Kafka users already know. Today, AutoMQ is one of the most mature open-source Diskless Kafka implementations, validated in production by industry-leading teams including Grab, JD.com, Tencent, Honda, and more Over the past two and a half years, AutoMQ has continued to contribute to the Kafka ecosystem: 1️⃣ Diskless Kafka on S3, with shared storage instead of broker-bound local disks 2️⃣ Stateless brokers for lighter scaling, reassignment, and recovery 3️⃣ Table Topic, bringing Kafka streams directly into Apache Iceberg tables without a separate ETL pipeline 10K stars is more than a milestone. It is a signal that the Kafka community is actively exploring a more cloud-native future. Thank you to everyone who starred the repo, tried AutoMQ, opened issues, shared feedback, contributed code, or challenged us to make the project better. This is just the beginning. GitHub: lnkd.in/gt7NVRbh #AutoMQ #ApacheKafka #Kafka #OpenSource #CloudNative #S3 #ApacheIceberg #StreamingData

Mimir 3.0 changes the Kafka question for observability teams. With Grafana Mimir's ingest storage architecture, Kafka is no longer just a side dependency. It becomes the write-path commit boundary: distributors write samples to Kafka, Mimir acknowledges writes after Kafka durability, and ingesters consume from Kafka partitions. That means self-hosted Mimir teams need to evaluate more than Kafka API connectivity. 1️⃣ If Kafka sits on the ingest path, broker-local disks can turn scaling into partition data movement. 2️⃣ In high-throughput metrics ingest, inter-broker replication and cross-AZ reads/writes can become a steady cost driver. 3️⃣ If Mimir already uses object storage for long-term blocks, keeping Kafka durability tied to broker disks creates another stateful storage lifecycle to operate. AutoMQ fits this architecture by keeping the Kafka interface Mimir expects while moving persistent Kafka data into shared object storage. Distributors and ingesters still use Kafka semantics; the storage and scaling model becomes Diskless. 👉 Read the full blog: automq.com/blog/automq-mimir… For more Kafka and AutoMQ engineering insights, please join the AutoMQ Slack community: 📷go.automq.com/slack #GrafanaMimir #Kafka #AutoMQ #DisklessKafka #Observability #Kubernetes #SRE