Best Kafka UI tools for Amazon MSK

Tools compared

Rank	Tool	Total (out of 110)	Governance beyond IAM	IAM, SCRAM and mTLS	MSK Connect and Glue	Out of the data path	MSK Serverless	Inspecting topic data	Cost a year, one cluster (modelled)
1	Kpow	100	RBAC, staged approvals, temporary access, masking, tenants, audit	All three documented, with example IAM policies	Both, with cross-account STS roles	One container, no external database, not a proxy	Supported; audit topic keeps one day, no disk metrics	kJQ search, data inspect, replay	$7,380
2	Kafbat UI	78	RBAC, global masking, opt-in audit	IAM, with an MSK setup guide	Glue through a serde plugin	One container	Setup guide written for Serverless	Message browsing	$8,640
3	Lenses	75	SSO and RBAC from Team tier, audit	IAM documented	Glue only	HQ on PostgreSQL plus an agent per cluster	Dedicated guide	SQL over topics	$20,882
4	AKHQ	70	Group roles, no masking	IAM documented, library bundled	Glue deserialise only; no MSK Connect	One container	Not documented	Message browsing	$8,640
5	Amazon MSK console and CloudWatch	63	IAM policies per principal	Your AWS identity	Separate AWS and Glue consoles	Nothing to deploy	Metrics only; topic views are Provisioned 3.6+	Topic configuration, no records	$11,520
6	Redpanda Console	62	Behind a paid Enterprise licence	IAM accepted; timeouts on large IAM clusters	Neither documented	One container	Not documented	Message browsing	$8,640 plus an unpublished licence for RBAC
7	Conduktor	76	Strong; data-level controls through Gateway	IAM	Glue only	Console on PostgreSQL; Gateway, a proxy, for data-level controls	Provisioned and Serverless, per a 2022 AWS blog	Message browsing	$32,880; $122,880 with Gateway Core and Protect

The tools, ranked for MSK

What teams on Amazon MSK need

Amazon MSK runs the brokers for you. It does not give engineers a way to read the records in a topic, and it leaves the question of who may do what, once several teams share a cluster, to IAM policies written per principal. Everything on this page is about what a tool adds on top of the service, not about replacing it. For the broader field of Kafka tools on any distribution, see the best Kafka management tools.

Three things come up once a team is running MSK in production. The tool has to connect with the same authentication the cluster already enforces, without a separate set of long-lived credentials. It has to understand the AWS services around the cluster, MSK Connect and the Glue Schema Registry, because otherwise engineers see undecoded bytes and connectors they cannot manage. And it has to answer the governance questions IAM leaves open, because IAM authorises the principal that connects, and when twenty engineers share one tool that principal is the tool’s role.

A managed service does not remove the need to watch the cluster from the client side. AWS runs and patches the brokers, and the way producers and consumers are written and configured stays with the team that owns them. The Amazon MSK Service Level Agreement draws the same line in its exclusions, which cover unavailability that results from the customer’s own actions and from not following the operational guidelines in the MSK documentation, with overloaded brokers and an excessive number of partitions given as examples. Consumer settings nobody has revisited, a consumer group that has grown far beyond its partition count and a partition total that has drifted upward all sit on the customer’s side of that line, so a team on MSK still needs a Kafka-level view of topics, consumer groups and clients beside CloudWatch.

Governance beyond IAM. IAM does not provide roles per person rather than per principal, masking of sensitive fields, an approval step in front of offset resets and topic deletion, or an audit record of each engineer’s actions. Those are what a shared tool has to add once more than one team works on the same cluster. On a cluster that uses IAM access control there is no second layer inside Kafka to fall back on, because AWS states that Kafka ACLs have no effect on authorisation for IAM identities. The record AWS keeps is limited in the same way, because CloudTrail logs Kafka actions on MSK only when the cluster uses IAM access control, and the Kafka actions AWS lists there are seven cluster and topic actions, among them kafka-cluster:CreateTopic, kafka-cluster:AlterTopic and kafka-cluster:DeleteTopic. Reading a topic’s records and resetting a consumer group’s offsets are not on that list, and each event names the IAM identity that made the call, which for work done through a shared tool is the tool’s role. MSK’s authorizer logs capture the cluster’s authorisation decisions, and those are likewise made about the connecting principal. Which engineer read a topic that holds customer data, or moved a group’s offsets during an incident, is therefore a question only the tool people sign in to can answer, and a tool with no roles of its own gives every person who opens it the full permissions of that role.

AWS’s guidance for privileged access to an AWS account points the same way. The AWS Security Blog separates persistent access, which a user can invoke at any time, from temporary elevated access, where each use is tied to a recorded business reason and granted for a limited time. Production access to Kafka fits that model when the tool can grant one action on one topic or consumer group for a fixed period and log the grant. The common alternative, a jump box that holds standing admin credentials for the cluster, is persistent access with no record of which commands were run.

IAM, SCRAM and mTLS. IAM is one of three sign-in options on Provisioned clusters and the only one on Serverless. The client needs the MSK IAM library on its classpath, the AWS_MSK_IAM SASL mechanism, and a role with kafka-cluster permissions on the cluster, topics and groups it will touch. Every third-party tool on this page can be made to connect that way, and the difference is in what is documented. Kpow publishes three example policies, from admin across every cluster in an account down to one cluster and its topics and groups. Kafbat UI publishes an MSK setup guide with an example policy, AKHQ publishes the connection properties, and Redpanda Console accepts the mechanism in its configuration, with its review recording the timeouts large IAM clusters run into. Kpow can also reach MSK Connect and a Glue registry in another AWS account by assuming an STS role, configured with the CONNECT_STS_ROLE_ARN and SCHEMA_REGISTRY_STS_ROLE_ARN settings.

The sign-in mechanism is also a portability decision. SASL/SCRAM and mTLS are standard Kafka mechanisms that self-managed clusters and other providers accept, so clients and tools keep their settings on a move. IAM access control exists only on MSK, where AWS makes minor modifications to the Apache Kafka source code to support it and authorises every action with an IAM policy, so each client that uses it has to be reconfigured to leave; the Migrating to open source Kafka session covers that trade-off. On SCRAM and mTLS clusters authorisation falls to Kafka ACLs, and the MSK default there is permissive. AWS documents that it sets allow.everyone.if.no.acl.found to true, so a resource with no ACLs is open to every principal, where Apache Kafka’s own default denies access to a resource with no ACLs, the setting recommended for production (Kafka security architecture). A tool on a SCRAM or mTLS cluster therefore has to manage ACLs as well as connect, and Kpow manages Kafka ACLs and records each ACL change in its audit log. MSK supports SCRAM-SHA-512 only, and each user is a Secrets Manager secret whose name begins with AmazonMSK_ and which is encrypted with a customer managed KMS key, so the tool’s own SCRAM user is created the same way.

MSK Connect and Glue. MSK Connect and the Glue Schema Registry are where MSK tools diverge most. Kpow reads and manages MSK Connect connectors through the MSK Connect API, and connects to Glue by registry ARN, each with the default AWS credentials chain, static keys or an STS role in another account. Lenses and Conduktor document Glue. Kafbat UI lists AWS Glue among its serializers and publishes the Glue serde as a separate plugin. AKHQ can deserialise Glue-encoded records using the AWS default credentials chain, with no cross-account role option. Redpanda Console documents no Glue Schema Registry support, and none of the tools other than Kpow documents MSK Connect. Schema choices are covered more broadly in Kafka schema registry tools.

Both services differ from their open-source counterparts in ways a tool has to handle. The Glue serialiser tags each record with a schema version ID, and AWS identifies a schema version by UUID or version number, where Confluent-compatible registries use an integer schema ID. A tool built only for the Confluent format cannot look up the schema of a Glue-encoded record, and a later move from Glue to another registry means translating every schema identity, which the Migrating to open source Kafka session describes as a frequent hidden cost of a move. MSK Connect is managed through an AWS API in place of the Kafka Connect REST API, and the two report status differently. The Kafka Connect REST API returns a status for the connector and for each of its tasks, which matters because a connector can report RUNNING while one of its tasks has failed. The MSK Connect API’s DescribeConnector returns one connector state from six values (RUNNING, CREATING, UPDATING, DELETING, FAILED and RESTARTING), and MSK Connect can recover failed tasks automatically. A tool that manages both has to read each service in its own terms, because the states of one do not map onto the other.

Out of the data path. Where the tool runs affects every requirement above. A tool that runs as a container in your own VPC, next to the cluster, and connects to MSK the way any Kafka client does, adds nothing to the path your producers and consumers take. A tool whose controls are enforced by a proxy that every client connects through becomes part of that path, and it has to be sized, kept available and kept in step with every client upgrade. Kpow is one container with no external database, installed in your own environment and out of the data path. Conduktor Console also connects to MSK directly, with a PostgreSQL database of its own; Conduktor’s data-level controls, such as encryption, masking of the data itself and virtual clusters, run in Conduktor Gateway, a Kafka proxy that client applications connect through.

A proxy can do things a client-side tool cannot, such as encrypting records and isolating tenants for every client without changes to the applications, and Kai Waehner’s review of Kafka proxies sets the costs beside those benefits: an extra network hop that adds latency, a component that has to be deployed highly available or it becomes a single point of failure, and one more system to operate. Keeping no database has a cost of its own that belongs in the same comparison. Kpow holds its snapshots, metrics and audit log in topics on the cluster it manages and computes its metrics with Kafka Streams, which keeps application state in internal topics on the cluster, so there is no second datastore to secure, back up or bring into audit scope. Its availability therefore follows that cluster’s, and its system requirements ask for it to run close to the Kafka resources it observes, with multi-region installations not officially supported. Whichever tool is chosen, a Kafka UI holds credentials for the brokers and belongs on a private subnet behind the organisation’s sign-in. GitHub Security Lab’s research on the open-source Kafka UI project found that its default configuration required no authentication to read and write data, and reported three remote code execution vulnerabilities, one of them in the message filter, all fixed in version 0.7.2.

MSK Serverless. On Serverless, Kpow sets its internal topics to the Serverless constraints with KAFKA_VARIANT=MSK_SERVERLESS, and its documentation is explicit about the cost: its audit log topic keeps one day on Serverless, and broker disk metrics are not available. The retry visibility walkthrough for Lambda functions triggered by MSK shows Kpow connected to an MSK Serverless cluster over IAM.

Serverless differs from Provisioned in ways that reach a tool. AWS sets the broker configuration and leaves seven topic-level properties editable, and Kafka ACLs are not supported, so IAM is the only authorisation layer. AWS’s guidance on Kafka Streams with MSK Serverless explains that a stateful Kafka Streams application fails to create its internal topics there, because Kafka Streams sets segment.bytes on them and Serverless protects that setting. Kpow computes its metrics with Kafka Streams, and its KAFKA_VARIANT=MSK_SERVERLESS setting exists to create those topics in a form Serverless accepts, while a UI that keeps no state in the cluster has nothing to adapt. Monitoring is narrower as well, since Serverless publishes nine CloudWatch metrics, with consumer lag reported for each consumer group and topic as a maximum, a sum and a time estimate, and open monitoring with Prometheus is documented for Provisioned clusters. Lag for an individual partition on a Serverless cluster has to come from a tool that reads offsets through the Kafka API, which is how Kpow computes it.

Inspecting topic data. The MSK console lists topics and their configuration and does not show records, so reading a message means the Kafka CLI or a separate tool, the gap Belong describes in the customer card below. In the incident walked through in Things that go bump in the night, the first report was that Kafka had lost messages, and inspecting the topic showed they were still there and had never been read, which is why record inspection is an operational need more than a convenience. Failed records raise the same need, and Uber’s design for reliable reprocessing moves them to retry topics and a dead letter queue so they do not block live traffic. Replaying from a dead letter queue is usually done with one-off scripts that sit outside any access control, and a tool that can clone records from a search result to a retry topic puts replay under the same roles and audit trail as every other action.

No tool here leads on every criterion, since Lenses has the stronger query model with SQL over topics, the MSK console needs nothing deployed at all, and Kpow’s two limits on Serverless are stated above. Kpow governs people working through Kpow, so applications keep their own IAM roles or SCRAM users, and IAM policies or Kafka ACLs stay the control for them.

Who runs Kpow on Amazon MSK

One Kpow customer has described in public how it runs Kpow on Amazon MSK: Belong, the Australian telco that is part of Telstra, in a talk by its Head of Enablement. The card is tagged with the rubric criteria its evidence speaks to, and the grey tags name the other things the talk covers. The talk also explains why Belong is on MSK at all. MSK was already an approved platform inside Telstra and approving another vendor would have taken too long, so approval time decided the platform more than features did. A tool bought through AWS Marketplace keeps to the same route, with the purchase on the existing AWS invoice and the software running inside the AWS account.

How a team runs Amazon MSK with Kpow

Installing it in the account. A team starts Kpow as an ECS task on Fargate from the CloudFormation templates, installs it on EKS with the Helm charts, or subscribes on AWS Marketplace and gets a container licensed to its AWS account, billed on the AWS invoice. Both AWS Marketplace listings carry every Kpow release; Annual checks entitlements through AWS License Manager and Hourly meters each running container. The Annual container calls License Manager at run time, which is how AWS Marketplace licenses container products sold on contract, so the ECS task role or the pod’s service account role needs the AWSLicenseManagerConsumptionPolicy managed policy, and a deployment in private subnets has to be able to reach License Manager. Amazon lists Kpow as the EKS add-on factorhouse_kpow in the Amazon EKS User Guide, published by Factor House, with its kpow service account and that policy. On AWS Marketplace the Kpow Annual listing averaged 4.6 out of 5 across 9 ratings and the Hourly listing 4.4 out of 5 across 5 ratings when read on 30 September 2026, with one Annual reviewer describing “a nifty UI for understanding our kafka clusters and topics without needing to resort to the command line.” The full walkthrough is Set up Kpow with Amazon MSK.

Signing in to the cluster. Kpow connects over IAM on port 9098 with the AWS_MSK_IAM mechanism, SCRAM-SHA-512 on 9096 or mTLS on 9094, and takes its AWS credentials from the task or pod role. Its MSK cluster documentation gives three example IAM policies, from admin across every cluster in an account down to one cluster and its topics and groups. Because Kpow inherits the role attached to the task or pod, no long-lived Kafka credential sits in its configuration, and under IAM access control it can do only what that role’s policy allows, so a security team can hold the tool itself to least privilege with the narrowest of the three policies. On MSK Serverless the team sets KAFKA_VARIANT=MSK_SERVERLESS so Kpow creates its internal topics within the Serverless limits.

Signing people in. Engineers sign in to Kpow through AWS IAM Identity Center over SAML, or through Okta, Microsoft Entra ID, Keycloak, OIDC or LDAP, so access follows the company directory rather than a shared IAM user.

Reading Glue-encoded topics. Kpow connects to the AWS Glue Schema Registry by ARN, so data inspect shows Glue-serialised records decoded, and engineers filter them across topics with kJQ. kJQ is modelled on jq, which most engineers already use on the command line, and its documentation lists where it differs. A registry in another AWS account is reached by assuming an STS role.

Running MSK Connect. Kpow reads connector and task status and configuration through the MSK Connect API, and with the documented IAM permissions it can create, update, stop, start and delete connectors, including in another account through an STS role.

Watching lag and brokers. Kpow shows each consumer group down to partition level and resets, clears or skips offsets from the same view, and its brokers view shows broker configuration and detects under-replicated partitions. Consumer group lag and other computed metrics are exported to Prometheus alongside CloudWatch. An offset count on its own says little about delay, because the same offset lag can mean a second or a day depending on the topic’s throughput (SoftwareMill on Kafka lag monitoring), so lag is worth reading beside each topic’s write rate. Kpow leaves alerting to the systems that already page the team. It computes its metrics from the Kafka API with no dependency on broker JMX and exposes them for Prometheus, where the alert rules live (Kafka alerting with Kpow, Prometheus and Alertmanager).

Giving teams their own view of a shared cluster. Tenants limit which topics, groups and connectors each role can see, RBAC sets what each person may do, staged mutations hold an offset reset or topic deletion for approval, temporary policies grant production access that expires at a set time, and data policies mask sensitive fields in data inspect results on the server. Kpow’s policies follow the evaluation rules a team on AWS already knows from IAM, where anything no policy allows is implicitly denied and an explicit deny overrides an allow.

Keeping the record. The audit log records each action with the user from the identity provider, and a webhook sends those records to Slack, Microsoft Teams or any endpoint, which is how a team on MSK Serverless keeps audit history beyond the one-day retention of Kpow’s audit topic there. The audit log is a topic in the team’s own cluster, so its records stay inside the AWS account. The webhook can send mutations, data inspect queries or both, which means reads are recorded as well as changes, the part of the trail that CloudTrail does not cover on MSK.

The public Kpow demo runs on two Amazon MSK clusters, MSK Primary and MSK Secondary, and needs no signup. It shows brokers, topics, consumer groups, the Kafka Connect cluster on MSK Primary, the AWS Glue schema registry, and the __oprtr_audit_log topic on MSK Secondary; sign-in, masking and temporary policies are the things to test in your own account. The demo environment’s schema registries are AWS Glue, Karapace and Confluent, as described in the September 2026 product update. Governing a shared MSK cluster for teams and AI agents is the subject of the Beyond IAM tech talk.

Kpow live demo

See Kpow running on Amazon MSK

The live Kpow demo is connected to two Amazon MSK clusters. Open MSK Primary to see its Kafka Connect cluster, then browse brokers, topics, consumer groups and the AWS Glue schema registry, with no signup.

For platform teams choosing a Kafka tool for MSK.

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FAQ

What is the best Kafka UI for Amazon MSK?

On this page’s rubric, Kpow, with 100 of 110 points: it signs in over IAM, SCRAM or mTLS, supports MSK Serverless, manages MSK Connect, reads the AWS Glue Schema Registry, adds per-person roles, masking, approvals and an audit trail on top of IAM, and runs as one container in your VPC outside the data path. Kafbat UI is the highest-scoring free open-source option.

What is the best Kafka UI for MSK Serverless?

Kpow has the highest total on this page and, like Kafbat UI and Lenses, scores 8 of 10 on the MSK Serverless criterion. It connects over IAM, the only method Serverless accepts, and documents a KAFKA_VARIANT=MSK_SERVERLESS setting that creates its internal topics within Serverless limits, with two stated trade-offs: its audit topic keeps one day and broker disk metrics are unavailable (Kpow MSK cluster docs). Kafbat UI also publishes a Serverless setup guide, and Lenses publishes a Serverless configuration guide.

Which Kafka UI can read messages serialised with the AWS Glue Schema Registry?

Kpow connects to a Glue registry by ARN, including one in another AWS account through an STS role, and decodes Glue-encoded records in data inspect. Lenses and Conduktor document Glue support, Kafbat UI decodes Glue through a serde plugin, and AKHQ deserialises Glue records using the default AWS credentials. Redpanda Console documents no Glue Schema Registry support. Registries beyond Glue are compared in Kafka schema registry tools.

Can a Kafka UI manage MSK Connect connectors?

MSK Connect connectors are created and described through the MSK Connect API. Kpow is the only tool on this page that documents MSK Connect, reading connector and task status and, with the IAM permissions shown in its docs, creating, updating, stopping, starting and deleting connectors, across accounts through an STS role.

Is there a free Kafka UI for Amazon MSK?

Kpow Community Edition is free on up to 3 clusters and 10 users and includes MSK IAM sign-in, MSK Connect and the AWS Glue Schema Registry. Kafbat UI and AKHQ are open source. RBAC, masking, staged approvals and the audit log need Kpow Enterprise. More free options are compared in the best free Kafka UI tools.

Does a Kafka UI need to sit in the data path to govern access on MSK?

No. Kpow runs as one container in your account, connects to MSK like any Kafka client and applies roles, masking, approvals and the audit trail to the people working through it, so producers and consumers keep connecting straight to the brokers. Conduktor Console also connects directly, while Conduktor’s data-level controls run in Gateway, a Kafka proxy that client applications connect through.

Does AWS provide a Kafka UI for Amazon MSK?

The MSK console lists and describes topics, partitions and configuration on Provisioned clusters running Kafka 3.6 and above, and can create, update and delete topics. It does not show the records in a topic, so reading or searching messages needs the Kafka CLI or a separate tool.

Which Kafka UI tools support MSK IAM authentication?

Kpow, Kafbat UI, AKHQ, Lenses, Redpanda Console and Conduktor all accept the AWS_MSK_IAM SASL mechanism. Kpow and Kafbat UI publish example IAM policies for MSK, and Kpow also documents SCRAM-SHA-512 and mTLS on MSK. They differ in what they document beyond sign-in: MSK Serverless, MSK Connect and the AWS Glue Schema Registry.

Can a Kafka UI be bought through AWS Marketplace and billed on the AWS invoice?

Yes. Kpow is listed as Annual, at $4,500 per cluster credit and eligible for an Enterprise Discount Program by private offer, and as Hourly, at $0.40 per container hour. Lenses and Conduktor also have AWS Marketplace listings, and Kafbat UI can be launched from AWS Marketplace on EC2.

Do Kafka ACLs work on an MSK cluster that uses IAM access control?

Not for IAM identities. AWS states that on a cluster using IAM access control, Kafka ACLs have no effect on authorisation for IAM identities, and MSK Serverless does not support Kafka ACLs at all. On SCRAM and mTLS clusters ACLs are the authorisation layer, and MSK sets allow.everyone.if.no.acl.found to true by default, so a resource with no ACLs is open to every principal.

Does CloudTrail record who read a topic on Amazon MSK?

No. CloudTrail logs Kafka actions on MSK only for clusters that use IAM access control, and the Kafka actions AWS lists are cluster and topic actions such as creating, altering and deleting topics. Reading records is not among them, and each event names the IAM identity that made the call, which for a shared tool is the tool’s role. A record of which person read a topic comes from the tool’s own audit log; Kpow’s audit log covers data inspect queries and can send them to a webhook.

Does a Kafka UI replace IAM policies on MSK?

No. IAM policies, or Kafka ACLs on SCRAM and mTLS clusters, still control what applications can do. A tool such as Kpow adds per-person roles, masking, approvals and an audit trail for the engineers who work through it.

How these tools were scored

Five of the six criteria start from Amazon’s MSK documentation; the sixth is where the tool runs. They are listed here in order of weight. Each criterion is scored 0 to 10: 10 where a tool is the only one here doing it or clearly the best, 8 for a clean documented pass, 5 or 6 for partial support or support that needs work the reader must verify, 1 to 4 for a weak or indirect form, and 0 where it is absent.

1. Governance beyond IAM (counts three times). IAM decides what a principal may do. It has no per-person view of data, no masking, no approval step before a topic is deleted and no audit trail of what an engineer looked at. This criterion scores per-person roles, production access granted on request and expiring, masking, directory sign-in, tenants for teams sharing a cluster, and an audit trail that names the person. The requirements for regulated teams are covered in Kafka governance tools for financial services, and the masking options are compared in Kafka data masking tools.

2. IAM, SCRAM and mTLS (counts twice). MSK IAM access control lets clients authenticate with AWS credentials and authorises each Kafka action with an IAM policy. Provisioned clusters also accept SASL/SCRAM with credentials held in Secrets Manager, and mutual TLS. A tool scores well here when it documents all three and can take its AWS credentials from the task or pod role rather than a stored key.

3. MSK Connect and Glue (counts twice). MSK Connect runs managed Kafka Connect workers, and its connectors are created and described through the MSK Connect API. The AWS Glue Schema Registry is the registry most MSK teams use. A tool that speaks only the Kafka Connect REST API and Confluent-compatible registries leaves both out. A tool scores 10 here only when it documents both, including a registry or connectors in another AWS account. Connector tooling on any distribution is compared in Kafka Connect monitoring tools.

4. Out of the data path (counts twice). The tool should run inside your AWS account and VPC, reach the brokers over the same private network your applications use as an ordinary Kafka client, and keep no data outside your own cluster. Scored lower: tools that need an external database of their own, and tools whose controls work only when application traffic passes through a vendor’s proxy. A self-hosted container with no external database and no proxy scores 9, a tool with a database of its own 6, one with several databases or a component on the brokers 4, and one that needs both a database and a proxy for its controls 3; 10 is kept for an option with nothing to deploy at all, here the MSK console. This criterion is scored the same way on every Factor House page that uses it, and only its weight changes with the reader.

5. MSK Serverless (counts once). MSK Serverless accepts IAM authentication only, and its quotas are per cluster: 500 consumer groups, 2,400 partitions for non-compacted topics and 120 for compacted ones, and 2 topic-management API requests a second. A tool that creates its own internal topics has to create them within those limits, and it should say which of its features are affected.

6. Inspecting topic data (counts once). The MSK console now shows topics, partitions and configuration, but not the records. Reading a message, searching a topic for one key or replaying a record after an incident is the day-to-day job engineers need a tool for.

Costs are modelled for one production MSK cluster and 25 engineers at $120 per engineer hour, using the same hours per tool class as Factor House’s other comparison pages. Tools with a licence carry the published price plus 2 hours a month to run. The open-source UIs carry 6 hours a month, $8,640 a year, to run, secure and keep current. The MSK console has no licence and nothing to run, and the Kafka CLI it leaves record reading to is modelled like the other tools with no access control of their own, at 8 hours a month, $11,520 a year. Kpow’s $7,380 uses the Annual price of $4,500 per cluster with 100 users included; on the Hourly listing a container left on all year is $3,504, or $6,384 with running time. Kpow Community Edition is free for 3 clusters and 10 users, so a 25-engineer team is on Enterprise. For free options compared at any team size, see the best free Kafka UI tools.

The criteria map onto the MSK features in the figure below.

F1 From MSK feature to what a Kafka tool has to do

What MSK does What the tool has to do

IAM access control Clients sign in with the AWS_MSK_IAM SASL mechanism on port 9098, and IAM policies authorise each action Ship the MSK IAM library and take credentials from the task or pod role, not from a stored key

SASL/SCRAM and mTLS Provisioned clusters also accept SCRAM-SHA-512 from Secrets Manager on 9096, and mTLS on 9094 Connect with ordinary Kafka client settings, then manage the Kafka ACLs that authorise it

MSK Serverless IAM only, 500 consumer groups, 2,400 partitions and 2 topic-management requests a second per cluster Create its own internal topics within those limits and state what it cannot show

MSK Connect Connectors are created and described through the MSK Connect API Call that API with an IAM role, including across accounts

AWS Glue Schema Registry Producers serialise records against schemas held in Glue Decode Glue-encoded records so engineers can read them

IAM authorisation A policy grants actions to the principal that connects When people share one tool, add per-person roles, masking and an audit trail, because the principal is the tool's role

Where the tool runs Brokers sit in your VPC, and producers and consumers connect to them directly Run in the same account and VPC as an ordinary Kafka client, not as a proxy that applications route through

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: Governance beyond IAM counts three times, IAM, SCRAM and mTLS counts twice, MSK Connect and Glue counts twice, Out of the data path counts twice, MSK Serverless counts once and Inspecting topic data counts once, for a total out of 110. Governance beyond IAM counts three times because on a shared MSK cluster IAM authorises the tool's own role, so per-person roles, production access granted on request, masking, directory sign-in, tenants for shared clusters and a per-person audit trail are the only record of which engineer did what. IAM, SCRAM and mTLS, MSK Connect and Glue, and out of the data path count twice: a tool that cannot sign in the way the cluster is configured cannot be used at all, a tool that cannot read Glue-encoded records or manage MSK Connect leaves two AWS services outside it, and a tool that clients connect through becomes part of the path every producer and consumer depends on, while one that needs a database of its own is one more component to run and secure in the VPC. MSK Serverless and inspecting topic data count once, because most third-party tools here pass them. 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 100 out of 110. The other options follow by total. Conduktor is listed last whatever its total; on its total of 76 it would place third.

Related reading

• Kafka: the complete guide
• Kafka-compatible cloud brokers
• Set up Kpow with Amazon MSK
• Deploy Kpow on EKS via AWS Marketplace using Helm
• Implementing Kafka at Belong
• Best Kafka governance tools for financial services
• Best Kafka management tools for banks
• Kafka SSO tools