Solace vs. Kafka

Enterprises of all shapes and sizes are striving to implement agentic AI and real-time data, and two of the most common technologies they use to do so are Solace Platform and Apache Kafka.

At first glance, the technologies can seem similar, but they differ significantly in many ways, and not just concepts. This piece explains the real-world differences between Kafka and Solace, and the ways Solace is superior, both for greenfield deployments and organizations that have already implemented Kafka.

Introducing Solace and Kafka

Apache Kafka is an open-source log-based broker. Several vendors have used Kafka as the basis of real-time data solutions by augmenting it with services, applications, and abstraction layers that address shortcomings in areas such as efficiency, reliability, scalability and security.

Solace Platform is based on the principles of event-driven architecture (EDA), specifically around a queue-based event broker that delivers lower latency and native messaging patterns designed for operational workflows where order, persistence, and guaranteed delivery are required.

For a detailed technical examination of the architecture and functional differences between Solace and Apache Kafka, read this comparison.

Organizations choose Solace Platform because it includes not just an enterprise-grade event broker (aka message broker), but a full toolset that facilitates the deployment, management, observability, governance, and security, and because Solace supports capabilities like publish/subscribe, guaranteed delivery, event replay, hierarchical topics and loosely-coupled architecture that make it fundamentally better suited to many real-time data projects. This makes it easier for architects, developers and middleware teams to implement and operate the system.

For Kafka users facing challenges with complexity, Solace works very well alongside logs when they’re the right tool for a specific job. Like many technologies it is not an either/or situation – in fact Solace has a number of connectivity options to help bring logs to operational use cases.

Applicability to Agentic AI

Many are calling artificial intelligence (AI) agents “the new microservices”— autonomous, decentralized components that are responsible for specific decisions or actions. Microservices benefited from a shift away from monolithic architectures, requiring enterprise architects to find ways to decouple endpoints and enable asynchronous, real-time communications between them. The solution was event-driven architecture (EDA).

Agentic applications, especially those that need real-time context from across the organization, similarly need EDA. Importantly, this architectural requirement shouldn’t slow down development and proofs of concept, but serve them and help them to scale. Because, by nature, they are operational applications for which queue-based communications has long been recognized as the superior architecture.

An event mesh built with Solace Event Brokers helps solve the asynchronous nature of applications, microservices, and agentic AI where agents operate at different, and unpredictable, rates.
An event mesh decouples services so it’s easier to add, modify or replace applications, LLMs, data sources, clouds, and more.
An event mesh makes it easier to manage new, or less mature, applications because they can be managed independently for recovery, upgrade, or replacement.
An event mesh takes the burden off the applications to know where and how to move data between them, and the need to perform transformations.
In your rapidly changing landscape, traditional tools built for streaming and analytics, like Apache Kafka, can introduce unnecessary complexity, cost, and latency for the real-time, bidirectional, and highly agile operational workflows required by applications that are enabled by the RAG and the latest agentic AI.

Solace Platform enables organizations to deploy an event mesh that provides the purpose-built, agile data foundation to scale their latest (AI) initiatives up and out, from pilot to production, without the operational pitfalls of Kafka. It also provides all the tools to help liberate the data, to stream and filter it, to react to it in the consuming application, and to democratize access to the events so anyone in the organization can leverage them for their own use cases.

Solace Platform makes it easy to connect to Kafka as a source or target using native connectors, micro-integrations at the edge, and a wireline proxy.

AI Agents are the New Microservices

AI agents are a lot like microservices – lightweight purpose-specific applications that perform a specific task and work with other microservices to complete processes – except agents they trained for tasks and are orchestrated to complete a goal, making it less structured but more powerful. Your organizational plans, which rely on AI agents for scaling up and out, need a data infrastructure that can adapt and grow with the speed (and sometimes messy pace) of AI innovation—Kafka just makes it hard.

Real-time context for LLMs and autonomous agentic AI applications require asynchronous, real-time integrations to work properly in production. The interactions between agents, traditional applications, and enterprise data sources must be loosely coupled, enabling independent development, deployment, and scaling.

Kafka, as a durable distributed log for batch-centric streaming and analytics, introduces friction here. It is great for storing events for event replay and new training, but vector databases are a more efficient and appropriate tool for providing real-time context.

An event mesh built with Solace Platform is inherently asynchronous and decoupled, and supports multiple messaging patterns, so agents, microservices, and applications can publish data and deliver it wherever it’s needed; i.e. when consumers subscribe to that topic, whether it’s the entire topic or a match thanks to wildcard support, This enables your teams to use publish/subscribe to adapt event-driven systems to the fast-changing AI landscape without being slowed down by the rigid, log aggregation-based infrastructure common to stream processors like Kafka.

Operation and Scaling

A major challenge in using Kafka for operational workflows is the explosion of topics and partitions. To achieve the fine-grained event filtering and workload orchestration that AI agents require, developers need a way to add an ever-increasing number of topics and partitions.

Topic Sprawl: Kafka’s flat topic structure often leads to the creation of duplicate topics for different consumers and increasing management overhead. This can be partially due to poor design, but how do you design perfectly for unknowns in this period of rapid AI-fueled change.
Partition Management: Partitions are manually configured for scalability and ordering. Over-partitioning increases broker load and licensing costs without performance benefit, while under-partitioning creates bottlenecks. Rebalancing partitions is complex, costly, and resource-intensive.
Cost of Replication: Cluster and log replication across regions or clouds, essential for resilience and multi-cloud strategy, adds management complexity, often requiring external tools.

Organizations address some of this challenge by offloading the work to 3^rd parties. However, due to its complexity, it often costs more as a commercial product, which touches on the adage that “open source isn’t free” when the enterprise needs features and support. Solace Platform solves these problems with smart topics and dynamic message routing (DMR). Its hierarchical topics eliminate topic sprawl, and DMR automatically routes data wherever a subscriber exists (with no unneeded replication or routing where there are no subscribers), removing the need for partition and cluster management, significantly reducing TCO and operational risk.

The other thing worth mentioning is that Kafka does support parallel processing by nature. This works well for blackboard architectures where there is one source for multiple consumers, but it quickly breaks with hierarchical agentic patterns. Solace addresses both scenarios with dynamic routing – which solves the problem and results in higher end-to-end performance at a lower cost.

Performance

Operational workflows, particularly those powering real-time microservices and agents, benefit from shock absorption and low latency. Moreover, things change. Things scale. Things get better. Even today, organizations are deploying low-cost edge LLMs that are purpose-built to do what microservices used to handle and don’t have the same latency as cloud services but do introduce rate mismatch. In short, AI isn’t just a chatbot in a modern enterprise and so latency and performance should always matter, all things being equal.

In hierarchical agentic workflows, orchestration may call on many different services and be responding to many different inbound triggers and so performance matters because they add latency and compute costs across the organization. Failing to maintain this high-performance mindset may not seem to matter at first…until your AI is flinging data and processing to dozens of places and applications you never imagined. We have seen this before with microservice explosion, so we believe taking a “be the best” approach out of the gate is warranted and costless to implement.

This becomes especially important as:

Workflows fan out: Orchestration spans many services while responding to multiple inbound triggers.
Latency compounds: Each hop adds delay and compute cost across the organization.
Systems evolve: Edge LLMs reduce round trips to the cloud but introduce new rate mismatches.

Kafka’s pull-based consumption model—where consumers must poll the brokers for new messages—is fundamentally inefficient for these use cases. The poll does provide the backpressure mitigation, but a queue delivers the lower latency solution and backpressure; and with things like dead message queues, and HA/DR built into Solace Platform you get the same results with Solace.

When agents require immediate data to act on a real-time event, continuous polling is necessary, but this burns compute cycles and adds unnecessary latency. When you cannot leverage Kafka’s inherent batch-based messaging, its performance for single-message, operational transactions drops significantly.

Solace, built around an event broker, uses a push-based publish/subscribe model where producers write messages, publish messages to the event broker, which delivers messages to the subscriber the moment they arrive, and they consume messages when they are able — high-throughput event streaming with guaranteed delivery to consuming applications that have a matching topic subscription. That loosely coupled architecture and pub/sub approach means the same message can be simultaneously delivered to any number of subscribers. This high-throughput low latency messaging and event streaming is crucial for AI agents that require fresh, contextual data for guaranteed real-time retrieval-augmented generation (RAG) and proactive decision-making:

Immediate delivery: Messages are published the moment they arrive via pub sub, eliminating polling delays.
Built-in shock absorption: Queues provide natural backpressure without continuous polling.
Production readiness: Dead message queues plus HA/DR across your event mesh support resilient operational pipelines.

This event broker-managed pub/sub model ensures lower latency and superior efficiency for high-velocity, operational AI pipelines.

Guaranteed Delivery

Reliability and message order are non-negotiable for production-grade operational applications. Solace Event Broker provides native, persistent queue-based messaging as a core function. Messages are delivered to a queue in FIFO (first-in, first-out) order and are only removed upon consumer acknowledgment, guaranteeing delivery and order with minimal complexity.

In contrast, Kafka is not natively queue-based. While it can emulate queue behavior using consumer groups, implementing guaranteed, persistent messaging, and preserving order across topics requires developer-managed logic related to partition keys and offsets—plus the new Kafka queues in development address some of it, but it isn’t native. We want to be completely fair here: Kafka’s partitioning for workload balancing and scaling while maintaining order is a strength, but not unique to Kafka. Solace released partitioned queues in 2023 to deliver on the same needs, but in a more sophisticated architecture.

This difference matters because it directly impacts development effort and operational performance:

Guaranteed delivery and order: FIFO queues with acknowledgment-based removal ensure reliability without custom logic.
Reduced application complexity: No need for developer-managed partition keys, offsets, or consumer coordination to emulate queues.
Built-in scalability: Partitioned queues provide workload balancing while preserving order, natively within Solace.

Because this complexity adds development cost and reduces performance compared to Solace’s built-in queue functionality, Kafka is not the ideal solution for operational AI pipelines. Solace’s inherent shock-absorbing capabilities also simplify high availability (HA) and disaster recovery (DR) for AI application stacks like LLMs and vector databases.

Advanced Filtering and Decoupling

In an agentic AI ecosystem, agents must receive only the specific data events they need to operate on. Generic topic structures lead to massive event overload (inefficient AI data fan-out), forcing services to receive and discard irrelevant data—wasting network bandwidth and compute resources.

Unlike Kafka topics, Solace topics are metadata-rich thanks to Solace’s hierarchical topic structure, enabling developers and architects to use fine-grained filtering with wildcards directly on the event broker. Hierarchical topics let applications that publish messages in such a way that Solace brokers can precisely targets events, delivering them only to the specific AI agents and applications that have subscribed to those Solace topics, which is critical for:

Precision: Preventing information overload for autonomous agents.
Orchestration: Enabling sophisticated logic to route tasks to the most appropriate or lower-cost agents on the fly.
Simplicity: Making it easier to connect new endpoints (publishers/subscribers) without needing to know which partitions or brokers to poll.

This broker-side filtering and routing maximizes decoupling, ensuring that agents can evolve and be updated without impacting the entire data flow.

Some may argue that putting the processing for routing on the broker makes it the bottleneck versus KSQL and streams (i.e. the application layer). Processing has to happen somewhere, and having the event mesh handle it is an approach proven by the most demanding organizations in the world. Solace uses edge based micro-integrations that operate independently and scale independently and yet still be managed inside the event driven integration stack. It delivers the best of both worlds.

Orchestration and Governance

AI Agents require more than just data delivery; they need a dedicated fabric for real-time coordination, lifecycle management, and enterprise-grade governance. Solace’s Agent Mesh is a comprehensive, resilient solution that fills this gap.

Agent Mesh, leveraging Solace Platform for communication, provides:

Agent Orchestration: A highly capable service for coordinating sophisticated multi-agent workflows, ensuring tasks flow efficiently between specialized agents.
Agent Governance: Centralized controls for security, compliance, and auditing of autonomous agent interactions, addressing the autonomous agent governance and orchestration risk faced by CAIOs.
Agile Architecture: The inherent decoupling allows teams to implement upgrades and deploy new agents without interruption or downtime, a critical advantage in a quickly changing industry.

While Kafka provides low-level streaming building blocks, Solace Agent Mesh is a complete, application-focused service for the agentic future, eliminating the need to combine and support multiple external tools.

Hybrid and Multi-Cloud

Modern enterprises run applications in a variety of clouds, datacenters and other locations, requiring efficient and resilient information distribution across environments and geographies.

Kafka’s approach to multi-site connectivity is asynchronous, log-based replication (e.g., MirrorMaker 2), which can result in higher cluster replication costs than you need in production. These costs will be borne by the organization directly or through fees paid to external vendors. As stated before, nothing is actually free once you are burning CPUs, moving data, or storing it somewhere.

Solace Platform is designed from the ground up for fast, efficient distribution across hybrid environments consisting of all kinds of clouds, datacenters and more.

Dynamic Routing: Automatically and efficiently routes data between broker clusters across regions and clouds, delivering data only where there are subscribers for optimal efficiency and lower networking costs.
Simplified Resilience: Provides easy, built-in management of HA within a cluster and DR across clusters, essential for connecting on-prem operational systems with cloud-based AI/LLM services. For disaster recovery, the event brokers work together to ensure that queues are recovered in both the local cluster and across regions.

This intelligent distribution ensures that your applications (AI-driven or otherwise) receive fresh context regardless of where the source agent, data source, or RAG pipeline is deployed.

Observability, Management and Governance

Middleware managers have to combine and support multiple toolsets to observe, manage and govern Kafka estates. This can include the logs from Kafka brokers, then via API the event telemetry (which has a heavy instrumenting cost), and finally the different backend tools used for visualizing different parts of the cobbled together applications. Finally, many organizations have an operations center where some of these metrics are also shared.

Solace Platform is a comprehensive and fully-integrated toolset:

Design & Governance: Tools for event API management and design.
Deployment & Management: Integrated, automated management of HA and DR.
Observability: Built-in, broker-native observability tools for monitoring the entire data flow, unlike Kafka which often relies solely on third-party tools or non-native OTel implementations.

This fully integrated set of tools reduces vendor sprawl and the operational risk associated with complex, fragmented infrastructure integration.

Comparison at a Glance

Feature	Solace Platform	Kafka
Operational Delivery Model	Push-based to consumers, maximizing real-time responsiveness.	Pull-based (polling) by consumers, leading to latency and inefficiency for single operational events.
Topic Complexity	Smart Topics: hierarchical, fine-grained filtering built into the broker. Lets system scale to millions of topics easily.	Flat topics/partitions: requires manual topic/partition scaling for filtering and load balancing, leading to explosion, complexity, and high cost.
Queue-based Messaging	Native, efficient FIFO queues with delivery guarantees built in.	Not native; complexity added via consumer groups/KIPs; polling inefficient for operational workloads.
Multi-Cloud/Hybrid	Dynamic Message Routing (DMR) for automatic, efficient event distribution across clouds/regions.	Requires management, external tools (MirrorMaker 2), and cluster replication costs.
Agent Orchestration	Solace Agent Mesh is a framework for building and orchestrating AI agents, and feeding them real-time context from across distributed environments, including clouds, enterprise applications and edge devices.	Requires custom glue code and external systems (e.g., Spring Cloud Stream, Quarkus) to manage agent coordination and governance. Adding tools increases complexity and cost.
Integrated Platform	Single, complete platform for design, deploy, manage, observe, and govern.	Requires combining and supporting multiple separate tools and third-party solutions.

Start with the Right Foundation

The move to real-time data and agentic AI requires a more capable, agile, and efficient data foundation. Kafka is an architectural mismatch for the real-time, operational workflows of AI agents, ballooning complexity and cost through its fundamental design choices around partitioning, polling, and topic structure.

To effectively implement real-time data and accelerate your AI strategy, you must leverage the right tool for the job. Solace Platform is:

Agile and Decoupled: Enabling rapid iteration and deployment of new agents without system-wide disruption.
Efficient and Performant: Utilizing a push model and broker-side filtering for low-latency, real-time context delivery.
Scalable and Governed: Automatically handling multi-cloud distribution and providing an integrated Agent Mesh for orchestration and governance.

Don’t repeat past mistakes; start your AI journey with Solace to get your pilot up faster and go to production without scaling complexity.