Flink is the right answer for stateful stream processing with event-time semantics and exactly-once guarantees, and an expensive answer for stateless transformation that a simpler consumer would handle. AceMQ assesses the workload against what Flink actually requires operationally and reports a recommendation with sizing.
Streaming projects get scoped on throughput numbers and skip the requirements that drive cost: whether end-to-end exactly-once is genuinely needed and whether the sinks can support it, how much keyed state the logic implies at production cardinality, what event-time lateness the upstream systems actually produce, and whether the team can operate checkpointing, savepoints, and rescaling. Those decisions determine whether the platform is sustainable.
Greenfield or expanding streaming platforms on Kubernetes with Kafka or Redpanda sources and mixed analytical and operational sinks.
AceMQ works from the actual processing requirements — semantics, state cardinality, lateness tolerance, and delivery guarantees — rather than from throughput alone. Where Flink fits, we size the cluster and state layer and define the operational model. Where it does not, we say so and identify the simpler component that does.
Customers get a defensible build-or-avoid decision per workload with sizing that reflects real state and semantics requirements. Several workloads typically move to a simpler consumer, which reduces the operational surface the team has to staff.
Designing state backend, key partitioning, and rescaling strategy for large-state Flink jobs that must restart without hours of downtime.
Migrating from Kafka to Redpanda with client compatibility testing, ACL and schema registry translation, and a staged cutover per topic.
Whether you need architecture advisory, 24/7 support, or full managed services, AceMQ has the expertise to help.