Kafka Support

24/7 Apache Kafka Support with a 15-Minute Emergency SLA

AceMQ supports Kafka in production — rebalance storms, under-replicated partitions, consumer lag that won't drain, and KRaft or ZooKeeper controller failures. Self-managed, Amazon MSK, or Confluent Platform. Every ticket reaches a named senior engineer who already knows your cluster layout.

Senior Apache Kafka engineers on call right now — 24/7/365
15 min emergency SLA24 /7 global coverage130 + enterprise customers26 + countries served

Trusted for mission-critical Apache Kafka by teams in finance, healthcare, defense, and telecom

Escalation Path

Your first hour of a Apache Kafka outage

Most vendors publish an SLA number. This is what actually happens, minute by minute, when you page a senior AceMQ engineer.

T+0

You page us

Phone, email, or Slack — any channel reaches the on-call senior engineer directly. No web form, no tier-1 queue.

T+15

Named engineer live

A senior engineer who already knows your environment joins a live bridge. Zero cold-start, no re-explaining your topology.

T+30

Root cause isolated

Direct broker access, log and metric review, and a working hypothesis with a rollback plan before we touch anything.

Post

Written RCA

Documented root cause, the fix applied, and the prevention steps — delivered after every P1, not just when asked.

Response Times

SLA tiers, contractually guaranteed

Every tier reaches a senior Apache Kafka engineer. There is no tier-1 triage layer to get through.

P1 — Emergency
15 min

Production down, messages not flowing, cluster or broker failure

P2 — Critical
1 hour

Severe degradation, rising error rates, approaching capacity limits

P3 — High
4 hours

Performance issues, configuration problems, non-critical failures

P4 — Standard
Next day

Questions, guidance, best practices, non-urgent improvements

Incident Triage

Apache Kafka problems we fix every week

These are real symptoms from real Apache Kafka production environments — and the first thing our engineers check when one comes in.

Consumer lag climbing steadily on one partition only
What we check firstPartition leader skew and per-consumer commit latency. One slow member holds the whole group back — and if processing exceeds max.poll.interval.ms the member is evicted mid-batch, triggering a silent rebalance loop that looks like lag.
Typical resolution1–2 hours
Consumer group rebalancing constantly, throughput near zero
What we check firstsession.timeout.ms against heartbeat.interval.ms, whether the group is on the eager or cooperative-sticky assignor, and whether pods are being rescheduled. Rolling deploys on the eager assignor stop-the-world on every pod restart.
Typical resolution1–3 hours
ISR shrinking and expanding repeatedly, URP alerts flapping
What we check firstreplica.lag.time.max.ms against follower fetch latency, then disk I/O wait and network saturation on the lagging broker. This is almost always a storage or NIC bottleneck on one node, not a Kafka configuration problem.
Typical resolution2–4 hours
Producer throwing TimeoutException: Expiring N records
What we check firstWhether the batch is aging out in the accumulator (request.timeout.ms, delivery.timeout.ms, linger.ms) or the partition leader is unavailable. With acks=all and min.insync.replicas=2, losing one replica stalls every produce to that partition.
Typical resolutionUnder 2 hours
Disk filling on brokers despite short retention settings
What we check firstWhether log cleanup is actually running: compacted topics with a stuck cleaner thread, open file handles pinning deleted segments, or segment.ms so large that retention.ms never gets a closed segment to delete.
Typical resolution1–3 hours
Exactly-once pipeline stalls, consumers block on LSO
What we check firstHanging transactions — an aborted or never-committed producer transaction pins the last stable offset, so read_committed consumers stop. We identify the stuck producer ID and transactional.id.expiration.ms behavior before intervening.
Typical resolution2–4 hours
KRaft controller quorum loses leadership, metadata writes fail
What we check firstController log directory latency and the quorum voter set. In KRaft mode a slow metadata log disk on any voter causes election churn; on ZooKeeper-based clusters the same symptom traces to ZK session expiry from GC pauses.
Typical resolution2–4 hours
Amazon MSK cluster degraded, but CloudWatch metrics look normal
What we check firstBroker-level partition count against instance class limits, MSK storage autoscaling state, and whether the client is pinned to a broker that failed over. MSK hides the broker logs you need — we pull them via the log delivery config first.
Typical resolution2–4 hours

Resolution times reflect typical Apache Kafka engagements under an active AceMQ support contract. Every P1 closes with a written root-cause analysis.

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Support In Practice

Apache Kafka problems we've already solved

Representative engagements showing how these incidents get diagnosed and closed under an AceMQ support contract.

Assessment

Middleware Architecture Assessment for Financial Trading

European Online Trading Platform

Independent architecture and performance review of RabbitMQ, Kafka, and Redis for an online trading platform.

RabbitMQKafkaRedis
Read case study
CVE Patching

Kafka CVE Patching and Compliance Strategy for Global Enterprise

Global Technology Enterprise

AceMQ developed a multi-technology compliance strategy covering CVE patching for Kafka alongside RabbitMQ and IBM MQ deployments, creating a unified vulnerability management approach across the entire enterprise messaging stack.

KafkaRabbitMQIBM MQ
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Support

High-Throughput Kafka and RabbitMQ Support for Global Payments

PagoNXT (Financial Technology)

AceMQ provided ongoing support for PagoNXT's high-throughput payments infrastructure running Kafka and RabbitMQ at 2,500 transactions per second, including load testing validation and architecture optimization.

KafkaRabbitMQ
Read case study
Assessment

Kafka Architecture Assessment and Migration Advisory

Enterprise Organizations

AceMQ's Kafka assessment service evaluates streaming architecture health, identifies operational risks, and provides a structured migration or modernization roadmap for enterprises running or evaluating Kafka.

Kafka
Read case study
Support

PostgreSQL WAL and Replication Slot Support

Digital Banking Platform

Ongoing support for a cluster where an abandoned logical replication slot repeatedly filled the WAL volume and threatened to halt the primary.

PostgreSQLDebeziumKafka
Read case study
Remediation

ClickHouse Too Many Parts Remediation

Adtech Measurement Platform

Resolving ingestion failures where frequent small inserts produced parts faster than background merges could retire them, tripping the parts limit.

ClickHouseKafkaKubernetes
Read case study
Remediation

Apache Druid Streaming Ingestion Lag Remediation

Digital Advertising Exchange

Resolving Kafka supervisor lag caused by task slot exhaustion, oversized ingestion tasks, and handoff failures to deep storage.

Apache DruidKafkaKubernetes
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Support

Apache Druid Query Performance Support

Retail Analytics Provider

Ongoing support for broker timeouts and unpredictable query latency driven by segment sizing, cache behavior, and processing thread contention.

Apache DruidKafkaKubernetes
Read case study
What's Included

Everything in your Apache Kafka support contract

No add-on pricing for incidents. No per-ticket charges. One contract covers the whole surface.

Emergency Incident Response

Brokers down, a consumer group that won't stabilize, or a partition with no leader. A senior Kafka engineer joins a live bridge within 15 minutes with access to diagnose — not a ticket acknowledgement.

Root Cause Analysis

Every P1 closes with a written RCA: what failed, why, the fix applied, and the specific broker setting, client config, or topic design change that prevents recurrence.

Throughput & Latency Tuning

Partition count, batch size and linger, compression choice, fetch sizing, and replication settings tuned against your actual message sizes and consumer processing time — not against a generic benchmark.

Security & CVE Advisory

mTLS and SASL/OAUTHBEARER configuration review, ACL auditing, and proactive alerts for CVEs affecting your exact Kafka, Connect, or client library version with tested upgrade paths.

Connect & Schema Registry

Kafka Connect worker failures, dead-letter queue design, sink connector backpressure, and Schema Registry compatibility breaks during rolling producer deploys — all inside the same contract.

Upgrades & Cluster Migration

Rolling upgrades with inter.broker.protocol.version staging, ZooKeeper-to-KRaft migration, and cluster moves between self-managed, MSK, and Confluent with mirrored offsets.

Anywhere You Run It

We support Apache Kafka wherever it's deployed

Cloud, Kubernetes, bare metal, hybrid, and air-gapped — including environments where you can't give us outbound network access.

AWS (EC2, ECS, EKS)Amazon MSK & MSK ServerlessConfluent Platform & Confluent CloudMicrosoft Azure (AKS, HDInsight)Google Cloud (GKE)Strimzi on KubernetesRed Hat OpenShiftBare metal & on-premiseHybrid cloud & multi-regionAir-gapped / no outbound accessKRaft-mode clustersZooKeeper-based clusters
Why AceMQ

What you get that you don't get elsewhere

Named Engineers, Zero Cold Start

The same senior engineers stay on your account. They know your partition layout, your retention policy, and your consumer topology — so a P1 call starts with diagnosis instead of twenty minutes of you describing your cluster.

No Tier-1 Triage Layer

You reach a senior Kafka engineer directly by phone, email, or Slack. Nobody collects information to pass along, and no escalation approval sits between you and someone who can read a controller log.

Clients and Brokers, Both Sides

Most Kafka incidents are client-side: prefetch, poll loops, commit strategy, or a producer with the wrong acks setting. We debug your Java, Go, Python, or .NET consumers alongside the brokers, because that's where the fault usually is.

Genuine Follow-the-Sun Coverage

Engineers across 26+ countries and every time zone. Your 3am incident is someone's mid-afternoon — no overnight skeleton crew, no waiting for a region to wake up.

Proactive, Not Just Reactive

Quarterly cluster health checks plus shared intelligence across our support base. When a version-specific broker bug or client library regression surfaces on one cluster, every affected customer hears about it before it reaches their production.

Managed Kafka, Fully Covered

Amazon MSK and Confluent Cloud remove some operational surface but not the ones that cause incidents. We support managed clusters as first-class environments, including the parts the provider's own support scope stops short of.

FAQ

Apache Kafka support questions

Your Kafka Cluster Shouldn't Wake You Up at 3am

Whether you need emergency response tonight or a support contract that prevents the next rebalance storm, AceMQ staffs every engagement with a named senior Kafka engineer. Support quotes returned within 24 hours.

Get in Touch

Talk to a Support Expert

Send us a message and we'll follow up within one business day — or book a free 30-min consultation directly.

305-204-2607
info@acemq.com
66 W. Flagler St. 9th Floor
Miami, FL 33130

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