Solace vs Kafka: Event Messaging, Event Streaming and Using Both

Solace vs Kafka is a comparison between two different ideas of what an event platform is for. Apache Kafka is a distributed log: it stores events in order, keeps them for as long as you configure, and lets each consumer read and re-read at its own pace. Solace Event Broker is an event broker: it matches each event against topic subscriptions and pushes it to whoever needs it, across many protocols and many sites. Kafka is built around retention, Solace around routing. Many enterprises run both, and the honest answer to “which one?” is usually “which job?”.

This page sits under Solace alternatives in the Solace cluster. For the plant-level version of the same question, see Kafka vs MQTT.

Solace vs Kafka: the two models

Kafka keeps the data; consumers keep their place. Producers append records to partitioned topics. The brokers retain them by time or size, whether or not anyone has read them. Each consumer group pulls records and commits an offset, its bookmark in the log. A new analytics job can start from the beginning of the log and read a month of history. The Kafka design documentation describes this partitioned, replicated commit log, and Solace’s own comparison calls Kafka “a distributed log built for high-throughput, durable event storage, and replay”.

Solace routes each message; the broker tracks delivery. Publishers send to hierarchical topics such as acme/plant2/line4/filler/state. The broker matches the topic against subscriptions, including wildcards (* for one level, > for the rest), and pushes the message to subscribers or into queues. Guaranteed messages stay on a queue until a consumer acknowledges them, then they are gone. Solace describes itself as “a routing engine that dynamically pushes events from publishers to subscribers over hierarchical, metadata-rich topics”. A replay log exists, but it is a recovery and onboarding feature rather than the centre of the design.

The consequence is where the intelligence sits. In Kafka, filtering and fan-out happen largely in the consumer: a consumer reads a partition and discards what it does not need. In Solace, filtering happens in the broker, so a consumer receives only the topics it subscribed to. That difference drives network use across a WAN, consumer design and how many topics an architecture can afford.

Solace vs Kafka compared on the same dimensions

Public facts as of October 2026. “Kafka” means Apache Kafka; Confluent adds Flink, governance and managed services on top.

Dimension	Solace Event Broker	Apache Kafka
Core model	Event broker: topic routing, queues	Distributed, partitioned commit log
Delivery	Broker pushes to subscribers	Consumers pull
Who tracks position	Broker, by acknowledgement	Consumer, by offset
Retention	Until acknowledged; optional replay log per Message VPN	By time or size, independent of reads
Filtering	Hierarchical topics, * and > wildcards, at the broker	By topic and partition; finer filtering in the consumer or stream processor
Queue semantics	Native: exclusive, non-exclusive, partitioned and last value queues	Share groups (KIP-932), production-ready since 4.2.0
Ordering	Per queue; key-based in partitioned queues	Per partition
Protocols	SMF, MQTT 3.1.1/5, AMQP 1.0, JMS, REST, WebSocket	Kafka protocol; others via connectors or proxies
Multi-site	Dynamic Message Routing between brokers	MirrorMaker 2; Confluent Cluster Linking
Stream processing	Not a stream processor	Kafka Streams; Flink in the ecosystem
Licence and owner	Commercial, Solace; free Standard Edition not production deployable	Apache 2.0, Apache Software Foundation; Confluent owned by IBM
Form factors	Software, appliance, Solace Cloud	Self-managed, plus many managed services

What changed in 2025 and 2026

Kafka gained queues. KIP-932 introduces share groups, where several consumers work through the same partition with per-record acknowledgement and delivery counting. The Kafka 4.2.0 announcement of 17 February 2026 calls them production-ready. That narrows the “Kafka cannot do work queues” argument, although the log and pull model underneath are unchanged. Kafka 4.0 had already removed ZooKeeper in March 2025.

IBM bought Confluent. IBM agreed to acquire Confluent for $31 per share, about $11 billion in enterprise value, and completed the deal on 17 March 2026. For a buyer, “Kafka” and “Confluent” are now different procurement conversations: an open-source project with many vendors, and an IBM product. How IBM positions Confluent against its own IBM MQ is covered on Solace vs IBM MQ.

Solace moved closer to Kafka clients. In September 2025 Solace introduced an open-source Kafka Wireline Proxy that lets Kafka clients connect to Solace with configuration changes.

Using Solace and Kafka together

This is the common answer, and Solace recommends it. Its comparison page suggests “using Kafka where log-based processing is essential, and Solace Platform to orchestrate and distribute real-time events across hybrid, multi-cloud, and edge environments”. That is a vendor’s framing, but the pattern stands on its own merits. The mechanisms, covered in depth on Solace Kafka integration:

1. In-broker Kafka bridging. Kafka Receivers consume Kafka topics and publish them as Solace messages; Kafka Senders take messages from Solace queues and produce them to Kafka. The docs state it is supported on software brokers, not appliances.
2. Micro-Integrations for Apache Kafka, Confluent, Redpanda, MSK and Event Hubs.
3. The Kafka Wireline Proxy for Kafka clients that should talk to Solace directly.
4. Event Portal discovery, which catalogues Kafka topics, consumer groups and schemas alongside Solace events.

An illustrative plant case. Six plants publish line state and quality events to local brokers. Solace links the plants to two data centres, filtering so that the MES in each region receives only its own sites, and a quality system receives every reject event on a guaranteed queue. A single bridge copies the full event stream into the corporate Kafka cluster, where it is retained for a year and replayed into the data lake whenever a model is retrained. Each platform does the job it was designed for.

Where each fits

Kafka fits when events must be retained and replayed, when stream processing (Kafka Streams, Flink) is the point, when the main consumers are data platforms and lakehouses, and when the team already has Kafka skills. It is the default for analytics pipelines.

Solace fits when events must reach many different consumers across sites and clouds with filtering at the broker, when clients speak MQTT, JMS, AMQP or REST, when guaranteed delivery to operational systems matters more than long retention, and when a WAN link may drop and the broker must buffer.

Both fit when the enterprise needs operational distribution and an analytics log. That is the majority case in large manufacturers, and it is why the comparison is rarely a straight replacement.

Where it gets hard

• Running two platforms costs two teams’ worth of skills. Bridging is easy to draw and harder to operate: schemas, topic-to-topic mapping and back-pressure all need an owner.
• Topic models do not translate one to one. Solace encourages thousands of fine-grained topics; Kafka works best with fewer, partitioned topics. Mapping one to the other needs a deliberate design, ideally in Event Portal or an AsyncAPI catalogue.
• Bridge placement. Kafka bridging runs on software brokers only, which constrains appliance-centric estates.
• Benchmarks are not neutral. Both camps publish favourable figures. Test your message sizes, fan-out and retention in a pilot.
• The IBM factor. Confluent pricing and roadmap under IBM are not yet visible. Keep the Kafka layer portable where you can.

For a structured selection between the two, an owner’s engineer review sets the requirements before the vendors do. The other comparisons are listed on Solace alternatives.