Kafka is the Java-based ecosystem king. Redpanda is the C++ drop-in replacement that is 10x faster and simpler to operations.
I've run Apache Kafka in production for years and more recently switched a team to Redpanda, and the honest experience is this: Redpanda is what Kafka would look like if someone designed the broker today with modern hardware and a distaste for JVM babysitting. They speak the same protocol, so clients don't notice the difference. The operators absolutely notice.
At one infrastructure team we were running a self-managed 9-broker Kafka cluster with ZooKeeper, dedicated JVM tuning, and a rotating on-call that everyone hated. p99 producer latency was ~15ms but p99.9 spiked to 200ms+ during GC. We piloted Redpanda on identical hardware as a drop-in replacement — same clients, same topics, same throughput profile. Two outcomes: (1) p99.9 dropped to 18ms — the GC tail vanished, (2) cluster went from 9 nodes to 5 with the same throughput headroom because thread-per-core used CPU more efficiently. Migration took two sprints mostly for operational validation, not technical work. The infrastructure cost savings alone paid for the migration twice over.
On a different team, though, they were on Confluent Cloud and heavily using Confluent Schema Registry + ksqlDB + 40 Connect connectors. Migrating to Redpanda Cloud would have broken too many integrations and the cost savings didn't justify the engineering effort. They stayed on Kafka. When you're paying someone else to run the brokers, the operational argument for Redpanda dilutes.
Choose Apache Kafka if:
If you're self-managing brokers today and are tired of ZooKeeper/KRaft/JVM operational overhead, Redpanda is a genuine upgrade — pilot it on one workload, validate the Kafka ecosystem compat for your use case, and expect meaningful cost and operator-sanity wins. If you're on managed Kafka and happy, stay — the case for migration is weaker. For greenfield projects starting today where you plan to self-host, Redpanda is my default recommendation unless you have a specific Confluent feature dependency. Both are excellent; the decision is more about operational philosophy than raw capability.
Redpanda implements the Kafka wire protocol, so existing Kafka clients (Java, Python, Go, etc.), Kafka Connect connectors, and Kafka Streams applications work with Redpanda without code changes in nearly all cases. Schema Registry is compatible via Redpanda's built-in schema registry or Confluent's. Edge cases exist — some Kafka transactional features or newer KIPs may land on Kafka first. For common production workloads (produce, consume, transactional semantics, compacted topics), compatibility is excellent. For specialty Confluent Platform features (Control Center, specific Connect enterprise plugins), audit before migrating.
No — Redpanda was designed without ZooKeeper from day one. It uses an embedded Raft-based consensus protocol for metadata management, so the broker process itself handles everything. This is a big operational simplification: one binary, one process, no external coordination service to monitor, upgrade, or debug. Kafka is moving toward the same model via KRaft (Kafka Raft Metadata mode), but Redpanda had this from the start.
Stick with Kafka if: (1) you're on managed Kafka (Confluent Cloud, AWS MSK) — the operational advantages of Redpanda don't apply when someone else runs the brokers; (2) you have deep Confluent ecosystem dependencies (ksqlDB, Control Center, specific Connect plugins); (3) you need cutting-edge Kafka KIPs that haven't landed in Redpanda yet; (4) your team already has strong Kafka expertise and no pain points worth migrating for. For new self-hosted deployments, Redpanda usually wins; for existing managed deployments, the case for migration is much weaker.
Redpanda's benchmarks show 10x lower p99 latency and 6x better throughput per core vs Kafka on identical hardware — but real-world improvements vary by workload. In my experience, the most consistent wins are tail latency (p99.9 improvements of 10x+ are common because JVM GC spikes vanish) and hardware efficiency (you can run fewer brokers for the same throughput). Median throughput and p50 latency differences are smaller. If your concern is median performance, the gap is moderate; if your concern is tail latency or cluster size, Redpanda delivers significantly.
Yes — both Kafka (via KIP-405, available in 3.6+) and Redpanda support tiered storage where recent data lives on local disk and older data is offloaded to S3/GCS/Azure Blob. Redpanda's tiered storage has been available longer and is generally more mature in production deployments. Implementation details differ: both let you configure retention per-topic, but the specific settings and failure behaviors are tool-specific. For long-retention workloads (30+ days), both significantly reduce storage cost vs disk-only retention.
Redpanda added a feature called Data Transforms that let you run WebAssembly functions inside the broker to transform messages on produce or consume — effectively in-broker stream processing without a separate Kafka Streams or Flink cluster. This is useful for lightweight transformations (format conversions, field stripping, simple enrichment) and can reduce operational complexity compared to running a separate stream processor. Kafka doesn't have an equivalent in-broker feature; you'd use Kafka Streams or ksqlDB instead, which run as separate processes.
Lower risk than most "infrastructure migration" projects because of the protocol compatibility. The typical playbook: spin up a Redpanda cluster alongside Kafka, MirrorMaker topics across during a validation window, switch producers and consumers to the new brokers, retire the old cluster. Most of the work is operational validation (confirming throughput, latency, retention behavior match expectations), not application code changes. For a moderate-sized cluster, plan a couple of sprints; for large, high-stakes deployments, plan a quarter including a long validation window in production.