Best Kafka management tools for technology and SaaS companies

Tools compared

Rank	Tool	Total (out of 100)	Out of the data path	Multi-cluster reach	Monitoring scope	Deployment footprint	Inspecting topic data	Many teams, shared clusters	Cost a year (modelled)
1	Kpow	90	One container, state in your Kafka, not a proxy	Up to 12 clusters per instance, any distribution	Lag, Connect, registries, ksqlDB, Kafka Streams; Prometheus endpoints	One container or JAR, no database	kJQ search across topics on the server	Tenants and per-action RBAC	$20,880
2	Kafbat UI	81	One stateless container	No cap	Metrics dashboard, Connect, registry, KSQL; no Streams view	Stateless container, Helm chart	Message browsing	Per-resource roles per cluster	$11,520
3	AKHQ	80	One stateless container	No cap	Groups, Connect, registry, KSQL; slow at high partition counts	One JVM container in YAML	Topic data browsing	Groups by resource and cluster pattern	$16,320
4	Redpanda Console	62	One stateless container	One broker cluster per deployment	Strong message viewer, little lag or Connect detail	Stateless container	Push filters	RBAC licence-gated; one cluster per deployment	$8,640 plus quoted licence
5	Lenses	58	HQ on PostgreSQL, agent and database per cluster	One agent per cluster	SQL studio, topology view, catalogue	HQ, agents and databases on Kubernetes	SQL over topics	Roles on groups only	$2,880 plus quoted licence
6	Conduktor	57	Gateway proxy for data-level controls, Console on PostgreSQL	Unlimited clusters on Team	Monitoring and operations, little lag detail	Console, PostgreSQL and a Gateway tier	Browse and filter	Per user or group; Virtual Clusters need Gateway	$50,880, or $140,880 with Gateway Core and Protect

No tool meets every column, so a management tool for people is usually paired with the metrics and alerting stack a company already runs.

The tools, ranked for technology and SaaS companies

What technology and SaaS companies need from a Kafka management tool

This page is about companies whose own product runs on Kafka: enterprise software and SaaS platforms, internet and adtech companies, and makers of hardware and connected devices whose products report to their own cloud services. Governance matters less here than on the pages for banks or financial services, where regulators set the bar. What decides the choice is whether the tool keeps up with the scale, the number of clusters and the kinds of consumers the company runs, and whether it fits the metrics stack the company already has.

Scale comes first. In a 2020 guest post on High Scalability, a TripleLift data engineer described an event log taking in 30 billion events a day. At that volume a management tool that puts a proxy in front of the brokers carries every one of those events, so the tool’s own capacity and availability become part of the product’s. A tool that connects as an ordinary Kafka client, like the applications do, adds nothing to that path, and the only other network calls it makes are to the REST APIs of the services it manages, such as the Kafka Connect REST API, which keeps a firewall review short.

Scale also changes what goes wrong. Most Kafka production problems are not Kafka bugs but configuration that made sense once, a signal nobody was watching and a decision made without context, as the incident review Things that go bump in the night sets out. In one of its incidents a consumer group with tens of thousands of idle members overwhelmed the group coordinator, and scaling Kafka made it worse, because the usual dashboards showed neither group membership size nor coordinator load. The consumer group protocol in Apache Kafka 4.0, described in KIP-848, moved rebalancing to the broker for this kind of reason. A management tool earns its place at a technology company by showing group membership, lag by partition and coordinator state before somebody scales the wrong component.

Not every consumer is a consumer group member, and technology companies run many of the ones that are not. Spark, Flink and any client that assigns its own partitions read without coordinator-managed membership, and Kafka’s admin API lists them as simple consumer groups (ConsumerGroupListing.isSimpleConsumerGroup). A lag view that shows only classic groups misses them, and that is often where the streaming jobs are. Kafka Streams apps add another gap: their topology and task health do not appear in broker metrics at all, because Kafka’s own documentation lists Streams metrics as metrics reported by the application, so a tool needs something inside the app to see them.

Topics outlive the features that created them. A product team retires a feature, its producers stop, and the topic stays, with consumer groups still committing to it or long gone. Lag figures make this harder to see than it should be, because tools count lag differently: lag computed from a group’s active assignments ignores topics the group used to read, while a tool that counts every committed offset shows lag on those stale topics. Burrow, LinkedIn’s open-source lag checker, is one example of a tool with its own definition, so before two dashboards are compared it is worth checking which definition each uses. Finding the stale topics needs per-partition write activity, not just a topic list.

Many technology companies already run Prometheus and Grafana, or a metrics service like them: the TripleLift engineer’s post on High Scalability describes Prometheus storing application metrics with Grafana dashboards on top. A Kafka tool fits that stack when it exports lag and throughput computed from observed offset changes, ready to alert on, rather than leaving the team to rebuild them in PromQL from raw counters collected by the Prometheus JMX exporter. Partition-level series need care: the Prometheus instrumentation guidance asks for low label cardinality, and a series per partition per group grows fast on large clusters, so an exporter that offers group and topic offsets on their own endpoints lets each team decide whether to scrape them. A metrics endpoint also serves cluster-wide data, so access to it is controlled at the network and scrape level, which is what the Prometheus security model expects of any exporter. Hand-built dashboards tend to improve only after an incident: in the group-membership incident described in Things that go bump in the night, no dashboard showed consumer group membership size until the incident had happened; a console that already shows lag by partition, group state and Streams topologies closes that gap without replacing the metrics stack.

Governance still has a place, at a smaller weight. Product teams share clusters, and some security teams answer that by giving every team its own Kafka UI instance. At a hundred or more teams that becomes a fleet of deployments pointed at the same few clusters, each with its own technical Kafka user and ACLs, each polling the admin API on its own schedule. Apache Kafka’s own multi-tenancy guidance describes tenants sharing a cluster through naming conventions and prefixed ACLs, and a management tool that scopes each team to its own prefix on one deployment follows the same model.

Technology and SaaS companies that run Kpow

Five technology companies that run Kpow are named here: Hewlett Packard Enterprise, ServiceNow, Garmin, TripleLift and JobNimbus. None has described its Kpow use in public, so the cards below give public context about each company and how it uses Kafka where that is published, rather than claims about what each does with Kpow. Garmin also appears on the page for manufacturers.

How technology and SaaS companies run their Kafka with Kpow

Each workflow below uses documented Kpow features, linked to the Factor House docs, the way a platform team at a technology or SaaS company would run them.

Install it once, close to the clusters. Kpow runs as one Docker container or Java JAR, with its state in internal topics on your own Kafka cluster and no external database. One instance manages up to twelve clusters and their Connect clusters, schema registries and ksqlDB servers; the documentation’s own warning is that multi-cluster does not mean multi-region, so a company with clusters in several regions installs an instance in each.

Keep working when a dependent service is down. Start-up validation of the schema registry, Kafka Connect and ksqlDB connections can be switched off, so Kpow starts even when one of them is unreachable, shows an error for that service in the UI and keeps trying to reconnect. The brokers, topics and consumer groups stay visible while the registry or Connect cluster is being fixed.

See every consumer, including Kafka Streams apps. The Kpow Streams Agent is a dependency added to each Kafka Streams application. Its source is open under Apache 2.0, it never opens a connection to Kpow, and once a minute it writes a snapshot of the app’s topology and metrics to a Kafka topic that Kpow reads. Kpow then shows the topology, thread, task and state store metrics beside the consumer groups and lag of the same app.

Export lag and group state to the metrics stack. With Prometheus egress switched on, Kpow serves cluster, Connect, schema and ksqlDB metrics on one endpoint, Kafka Streams metrics on another, and group offsets and topic offsets on their own endpoints, so the high-cardinality partition series are scraped only where they are wanted. The metrics glossary lists what each exports, and alert rules live in the company’s own alerting service.

Find topics nobody writes to. Kpow’s Signals topic view flags inactive partitions with no writes in the last seven days, empty partitions and tiny partitions, and its consumer view flags idle consumer members. Signals is an early access feature, off by default and switched on with an environment variable, so a team that turns it on should expect its checks to change between releases. Deleting a stale topic is still a person’s decision: Kpow’s topic management covers configuration edits, truncation and replica changes, and RBAC decides who may take those actions.

Search records when a customer reports a problem. Data Inspect searches several topics in one query and runs kJQ filters on the server against keys and values, with headers included when a headers deserializer is chosen, so a support engineer can find one account’s events without writing a consumer. The Data Inspect documentation says the filters are compiled and executed on the server rather than in the browser, which matters for 64-bit identifiers: JavaScript cannot represent every integer above Number.MAX_SAFE_INTEGER, so a filter evaluated in a browser can miss large IDs.

Scope each product team to its own topics. Multi-tenancy restricts each team to the topics, consumer groups, schema registries and connectors it owns, matched by name patterns, on one shared deployment. It is the lighter-weight criterion on this page, and it is there when a company that started without authentication reaches the point where every engineer seeing every topic is no longer acceptable.

To see these screens before installing anything, the live Kpow demo needs no signup and runs on two MSK clusters.

Kpow live demo

Follow consumer lag across two clusters in the live Kpow demo

The live Kpow demo needs no signup. It runs on two Amazon MSK clusters: switch between them, follow consumer groups and lag by partition, browse topics and their schemas, and search messages with kJQ.

For platform teams running Kafka under software products, SaaS platforms and connected devices.

Try the Kpow demo→

FAQ

What is the best Kafka tool for a technology or SaaS company?

On this page’s rubric, Kpow: it runs as one container with no external database and nothing in the data path, manages up to twelve clusters per instance, shows Kafka Streams topologies and lag for every consumer, and exports lag, group state and Streams metrics to Prometheus. It scores 90 out of 100, ahead of Kafbat UI at 81 and AKHQ at 80.

Does Kpow replace Prometheus and Grafana?

No. Kpow shows the current state of brokers, topics, consumers and Streams apps and what to do about it, while the metrics stack keeps the history and does the alerting. Kpow has no alerting engine of its own; it feeds the metrics stack through its Prometheus endpoints, so lag and group state arrive ready to alert on.

Can a Kafka UI show Kafka Streams applications?

Kpow can, through its open-source Streams agent, which shows each app’s topology and its thread, task and state store metrics. Kafbat UI, AKHQ and Redpanda Console show a Streams app’s consumer group and internal topics but not its topology.

Does the ranking change if governance does not matter at all?

No. Without many teams on shared clusters, the other five criteria total 90, and Kpow still ranks first with 81, ahead of Kafbat UI at 75 and AKHQ at 75.

Is a free Kafka UI enough for a technology startup?

For browsing topics and consumer groups on one or two clusters, often yes. Kafbat UI and AKHQ are free and run as one container each, and Kpow Community Edition is free for up to 3 clusters and 10 users. The paid features matter once the company runs Kafka Streams apps it needs to see inside, exports Kafka metrics to Prometheus, or needs to scope teams on shared clusters.

How these tools were scored

Six criteria, each taken from how technology and SaaS companies run Kafka, score every option from 0 to 10. They are listed here in order of weight, and each total is the weight times the score, summed, out of 100.

1. Out of the data path (weight 3). A management tool that runs in the company’s own environment, connects as an ordinary Kafka client and keeps no data outside the company’s clusters adds nothing between producers, consumers and brokers. A tool whose data-level controls need a proxy carries every event the product produces, and a tool that keeps its own state in a database adds a system to run and secure. Lenses and Conduktor lose most points here.

2. Multi-cluster reach (weight 2). Technology companies run development, staging, production and often regional clusters, so one deployment that reaches many clusters saves a fleet of consoles. Kpow’s cap of twelve clusters per instance keeps it level with the uncapped Kafbat UI, AKHQ and Conduktor rather than above them, and Redpanda Console reaches one broker cluster per deployment.

3. Monitoring scope (weight 2). How much of the cluster the tool shows: lag by partition, consumer groups of every kind, Connect, schema registries, ksqlDB and Kafka Streams, and whether it exports those figures to a metrics stack. The scores reuse the best Kafka monitoring tools page where it scores the same option; Kafbat UI is not scored there and was scored on this page from its own documentation.

4. Deployment footprint (weight 1). How much infrastructure the tool adds: one stateless container scores high, a central server with a database and an agent per cluster scores low.

5. Inspecting topic data (weight 1). Searching and reading records, by key, value or header, across topics, without writing a consumer.

6. Many teams, shared clusters (weight 1). Whether each product team can be scoped to its own topics, consumer groups and connectors on a shared cluster. It counts once here, against twice or more on regulated industry pages.

The scores on shared criteria match the same options’ scores on the site’s other ranking pages, including the multi-cluster tools page and the industry pages.

The cost figures model a technology or SaaS company running 4 clusters (development, test, staging and production) for 40 engineers at $120 an engineer-hour, and each card prints its own assumptions. The general listicle view, without this weighting, is in best Kafka management tools.

F1 What a technology or SaaS company runs into, and what the Kafka tool has to do about it

What happens at a technology or SaaS company What the Kafka tool has to do

Event volume Product, ad and device events run into billions a day, and every one of them crosses the brokers Connect as an ordinary Kafka client, with no proxy for that traffic to pass through and no database of its own

Many clusters Development, staging, production and regional clusters multiply as the product grows Reach many clusters from one deployment, installed close to them

Kafka Streams and other consumers Much of the processing runs in Kafka Streams apps, Spark or Flink jobs, not only in plain consumer groups Show Streams topologies and the lag of every kind of consumer, including ones that assign their own partitions

Metrics The company already runs Prometheus and Grafana, or another metrics service, for alerting Export lag, group state and Streams metrics ready-made, with high-cardinality series kept apart

Old topics Topics outlive the features that created them, and nobody is sure which ones are still read Show which topics have stopped receiving writes and which consumer groups still read them

Partial outages A schema registry or Connect cluster is unreachable while the brokers are fine Keep working, show the error for the unreachable service and reconnect when it returns

Every option is scored from 0 to 10 on each criterion, from the evidence and sources this page cites, and the reason for each score is on its card. The criteria are weighted: Out of the data path counts three times, Multi-cluster reach counts twice, Monitoring scope counts twice, Deployment footprint counts once, Inspecting topic data counts once and Many teams, shared clusters counts once, for a total out of 100. Out of the data path counts three times because a technology or SaaS company pushes very large, continuous event volumes through Kafka: a tool that puts a proxy between producers, consumers and brokers handles every one of those events, and a tool that keeps its state in a database outside Kafka is one more system to run. Multi-cluster reach and monitoring scope count twice, because these companies run many clusters and buy a Kafka tool mostly to see lag, consumers, Kafka Streams apps and metrics across them. Deployment footprint, inspecting topic data and many teams on shared clusters count once: governance matters less here than on a bank's page, but product teams still share clusters. This page is published by Factor House, which makes Kpow. Every option is scored on the same rubric and the same sources: Kpow's per-criterion scores are set the same way as every other option's and are not adjusted, and the weights apply to every option alike. Kpow ranks first on its total of 90 out of 100. The other options follow by total.

Related reading

• Kafka: the complete guide
• Best Kafka governance tools for financial services
• Best tools to manage multiple Kafka clusters from one place
• Best tools to monitor Kafka consumer lag
• Best tools to monitor Kafka broker health
• Best Kafka management tools for manufacturers
• Best Kafka UI tools for Redpanda
• Best Kafka management tools for banks