Best Kafka management tools for banks

Tools compared

Rank	Tool	Total (out of 100)	Out of the data path	Production access on request	Audit trail per person	Directory and Kafka sign-in	Many teams, shared clusters	On-prem and cloud together	Cost a year (modelled)
1	Kpow	89	One container, state in your Kafka, not a proxy	Time-boxed temporary policies via API, staged approvals, masking per resource	Every action with the IdP user, data queries included	SAML, OIDC, LDAP; Kerberos, SCRAM or mTLS to brokers	Tenants and per-action RBAC	Any distribution, 12 clusters per instance	$20,880
2	Kafbat UI	66	One stateless container	Per-resource RBAC, no approvals, masking for all viewers	Optional, reads at level ALL, no view	OAuth2, OIDC, LDAP; no SAML	Per-resource roles per cluster	Confluent Cloud broke in v1.4.x and v1.5.0	$11,520
3	Klaw	60	Self-hosted with its own database	Approval on every request, no data access	Requester and approver, no reads	Active Directory, OAuth2 SSO	Teams, tenants, 35+ permissions	Any distribution, writes Kafka ACLs	$8,640
4	AKHQ	59	One stateless container	Regex groups, UI-only if JWT secret unset	Opt-in, no reads, no view	LDAP, OIDC; no SAML	Groups by resource and cluster pattern	Named connections	$16,320
5	Lenses	54	HQ on PostgreSQL, agent and database per cluster	Strict global masking, no approvals	In-product audit log from Team tier	SSO incl. Entra ID and Okta	Roles on groups only	Any Kafka API, agent per cluster	$2,880 plus quoted licence
6	Confluent Control Center	35	Dedicated host, broker reporter JAR	Confluent RBAC, no DENY, no approvals	Broker principal, not always the person	OIDC on self-managed	Admin access only, per TD	Confluent Platform only	$2,880 plus quoted subscription
7	Conduktor	59	Console on PostgreSQL; Gateway proxy in the data path	Per-viewer masking, cross-team access requests	70+ event types with user, in the UI	LDAP, OIDC	Per user or group, most permissive grant wins	Confluent Cloud, Aiven, MSK, Cloudera	$122,880; $212,880 with Gateway Core and Protect

No tool meets every column, and banks commonly pair a management tool for people with broker ACLs or an authorizer for services.

The tools, ranked for banks

What banks need from a Kafka management tool

This page is about banks specifically: retail and commercial banks that run Kafka as shared infrastructure for payments, fraud and business banking teams. For the regulation-by-regulation view across banks, payments and insurance, with DORA, PCI DSS, GDPR and SOX mapped to Kafka controls, see best Kafka governance tools for financial services.

A bank’s Kafka platform team is usually small and its client base is not. TD’s Event Streaming Platform, described by its staff engineer Sandy Yang in a talk hosted by Factor House, runs more than 20 clusters in four forms (on-prem virtual machines, on-prem physical servers for very low-latency workloads, and two kinds of Confluent Cloud cluster) and has onboarded more than 300 projects. The platform team hand-held its early clients through Control Center and the command line until it could not keep up, and the job of a management tool at that point is to let every line of business serve itself without anyone holding admin rights. The same talk says the team burned out creating every client’s artefacts by hand before self-service existed, and still judges one enterprise platform the right call, because the alternative was many small clusters that nobody could maintain after two or three years.

Regulated firms sometimes put a sensitive workload on a physically separate cluster, and the reason is usually that segregation is easier to prove to an auditor that way, not that a regulation asks for a separate cluster, as the webinar Reduce Kafka spend and operational risk sets out. What the bank has to show is the segregation between teams on a shared cluster, and the Apache Kafka multi-tenancy guide documents how one cluster provides it, with a topic namespace per tenant, authentication and authorization, and quotas. A management tool on a shared cluster has to carry the same boundaries through to what each person can see and do.

Banks differ most from other Kafka users in production. Engineers need to look at production data when something breaks, but standing read access to payment and customer topics is exactly what a bank’s controls exist to prevent. Writes are stricter again, and at TD anyone who wants to publish to a production topic has to ask for an exception and justify it. So the tool has to support access that is granted for a task, expires on its own, and leaves a record. The usual workaround is a VPN, a jump box and Kafka’s command-line tools, which hands the engineer everything the jump box’s credentials allow and keeps no record of the commands that were run. The AWS Security Blog’s guide to temporary elevated access names the two models, persistent access that can be invoked at any time and time-bound access granted for a stated business reason, and describes the second as the supplement for higher-risk human access. For banks in the EU the regulation says the same thing: DORA Article 9 requires policies that limit access to information and ICT assets to what is required for legitimate and approved functions and activities only.

Naming the person who did something is harder in Kafka than in most systems. A Kafka ACL is written against a principal, and that principal is usually a service identity, so engineers who share a technical user look identical to the broker. NORD/LB’s case study describes this as the bank’s compliance problem: it had no separation between natural and technical users until people signed in to a tool as themselves and the tool acted through technical users with scoped permissions. Only the tool people sign in to can attribute an action to a person, which is why the audit trail on this page is scored per person.

Around that sit the constraints every bank shares. People are managed in Active Directory or LDAP, clusters are often secured with Kerberos or mutual TLS, on-prem clusters run beside a managed cloud service, and every component that can see customer data goes through a third-party risk review. Two of those are separate sign-in layers that tool evaluations often merge. Kerberos, SCRAM and mutual TLS are how a client authenticates to the brokers, and the Apache Kafka security overview lists them as Kafka’s own SASL and SSL mechanisms. SAML, OpenID Connect and LDAP are how a person signs in to the tool. A tool can pass one check and fail the other, so a bank tests both.

The rules behind a bank’s third-party risk review, DORA Article 28 in the EU, OSFI Guideline B-10 in Canada and the interagency guidance in OCC Bulletin 2023-17 in the United States, are about providers that run services or hold data for the bank. Software that runs inside the bank’s own network, connects as an ordinary Kafka client and keeps its state on the bank’s clusters leaves that review less to cover than a hosted service, a tool with a database of its own, or a component that application traffic passes through.

The component that application traffic passes through is a Kafka proxy, and it brings benefits as well as costs. Kai Waehner’s Kafka Proxy Demystified lists what a proxy buys, encryption, tenant isolation, audit logging and routing applied without changing applications, and what it costs: an extra network hop, a component that has to be highly available or it becomes a single point of failure, and more code in the critical path. Conduktor Console connects to clusters directly and masks data in its own UI, but it needs an external PostgreSQL database, and Conduktor’s field encryption, masking of the data itself, policy enforcement on client traffic and Virtual Cluster multi-tenancy all run through Conduktor Gateway, which Conduktor’s own documentation describes as a Kafka proxy between client applications and brokers. Gateway can enforce policy on applications, which Kpow does not attempt. Kpow gives people RBAC, masking in inspection, temporary access, tenants and an audit log without putting anything in front of the brokers, and a bank keeps broker ACLs or an authorizer as the control for its services.

Banks also change Kafka distributions and vendors over time. TD runs on-prem clusters beside Confluent Cloud, and NORD/LB kept Confluent as its Kafka platform and added Kpow beside it, which treats the distribution and the management tool as separate decisions. IBM completed its acquisition of Confluent on 17 March 2026, so a bank whose console comes from its Kafka vendor now has one supplier behind both. DORA Article 29 asks financial entities to consider whether a provider is easily substitutable and whether several arrangements sit with the same provider, and a management tool that works the same on every distribution keeps the two decisions apart.

What banks use Kpow for

Five banks that run Kpow are named here. How each one uses it is described only where the bank has said so in public; for the others, the card carries public information about the bank itself. What these banks have in common is a platform team serving many teams on shared clusters, under a regulator, with a security function that has to approve every tool that touches customer data, and the rubric below the rankings is built from that. Each card is tagged with the rubric criteria its evidence speaks to.

How a bank runs its Kafka with Kpow

The workflows below are how a bank’s platform team puts Kpow to work across shared Kafka clusters. Each one is built from documented Kpow features, and TD’s talk describes several of them running in production.

Install it beside the clusters. Kpow runs as one Docker container or Java JAR, or on Kubernetes with the Helm charts, inside the bank’s own network. It needs no external database: its system requirements state that snapshots, metrics and the audit log are held in topics on the bank’s own cluster and that Kpow has no dependencies beyond Kafka. The mechanism is Kafka’s own, since Kafka Streams keeps application state in internal topics on the cluster, so there is no second store of topic metadata or audit records to secure and back up. Kpow connects to brokers as an ordinary Kafka client, with the same cluster security settings as any other client, including SASL GSSAPI for Kerberos, so applications keep talking to the brokers directly. Because it is a client, the brokers authorize it like one: Kpow’s documented minimum ACL permissions let the security team grant its principal least privilege, and the broker stays the final authority on every action. One instance connects to self-managed Apache Kafka, Confluent Platform, Confluent Cloud and Amazon MSK together, up to 12 clusters, so on-prem and cloud clusters sit in the same view.

Review where it sits. A Kafka UI server holds broker credentials, so it goes on an internal network behind the bank’s sign-in and never on the public internet; GitHub Security Lab’s write-up of three remote code execution flaws in an open-source Kafka UI found many instances deployed with no authentication, some of them exposed to the internet. Kpow’s state lives in Kafka, so Kpow is available only while that cluster is, and its system requirements add that it has to run close to the clusters it manages because network latency affects what it can observe. The UI also sends product analytics unless ALLOW_UI_ANALYTICS is set to false, an option the data collection page limits to paid licences.

Onboard a team with its own tenant. When a new team is approved, the platform team adds a tenant scoped to that team’s topics, consumer groups and connectors, and maps the team’s Active Directory group to a role through LDAP or SAML with Microsoft Entra ID. With LDAP the roles are read from directory groups by Jetty’s JAAS LDAP login module, so group membership in the directory drives access and Kpow keeps no user store of its own. RBAC then sets Allow, Deny or Stage per action and resource, with Deny winning where policies overlap, so production can stay read-only for everyone outside the operations team. That is the evaluation order bank security teams already know from AWS IAM, where an explicit deny overrides any allow. Inside a tenant, the multi-tenancy documentation describes a consistent synthetic view of the cluster, so the totals a team sees are computed over its own resources, a user can only create resources that are valid inside the tenant, and the documented default “Kpow Hidden” tenant excludes Kpow’s internal topics, whose names begin with oprtr or __oprtr, the audit topic among them. At TD the whole sequence is a change inside the bank: the client opens the firewall, creates an Active Directory group and raises a ServiceNow change order, and the platform team sets up the tenant and access policy.

Grant production access for one task. An engineer who needs to inspect a production topic raises a request in the bank’s change system. Once it is approved, that system calls the Kpow API to create a temporary policy that grants inspect access on the named topic for an hour or two. The policy expires on its own, is capped at seven days by default, and is recorded in the audit log. TD runs this pattern from a ServiceNow form, and the grant arrives within a minute. Because Kpow is self-hosted, a cloud ITSM reaches its API through a ServiceNow MID Server or a reverse proxy, as the Kpow and ServiceNow guide shows, and Kafka itself is never exposed. The pattern is the one identity platforms use for privileged roles: Microsoft Entra Privileged Identity Management offers time-bound access with start and end dates and approval before a role is activated, and a temporary policy applies it to one action on one Kafka resource.

Hold risky changes for a second person. With staged mutations, a role can be set to Stage on actions such as creating or deleting a topic, so the request waits in Kpow until an administrator approves or denies it, and the full history lands in the audit log. A webhook can tell the approvers that a request is waiting. The Apache Kafka operations guide says to make sure the consumer instances are inactive before resetting a group’s offsets, which on the command line leaves the engineer to confirm that for themselves. In Kpow an offset change is also a mutation, scheduled and applied when the group is empty, and it shows as a pending mutation until then.

Show an examiner who did what. The audit log records each action, data inspect queries included, with the user from the bank’s directory and the policy that allowed it. The question an examiner asks about customer data is who looked at it, and a log of changes alone cannot answer that. Kpow shows the last seven days in the product and writes the record to the __oprtr_audit_log topic on the bank’s own cluster, where Kpow’s topics default to one week of retention, so for a longer record webhooks send mutations, queries or both to Slack, Microsoft Teams or any endpoint the bank chooses, such as its SIEM. The record never leaves the bank’s environment unless the bank sends it somewhere.

Mask customer fields. Data policies redact card and customer fields in Data Inspect and ksqlDB results on the server, across the key, value and headers of a record, so an engineer with access to a payments topic sees the structure of each message without the sensitive values. Masking that ran in the browser would leave the clear value in the response for anyone who reads the traffic, which OWASP’s API security guidance lists as a top risk under relying on the client to filter sensitive data. Kpow also removes the plain String SerDes from Data Inspect when data policies are configured, since reading a structured message as raw text would skip the redaction, and when a schema changes so that a masked string becomes a nested object the policy falls back to full redaction, the behaviour OWASP calls failing securely. For card numbers, a policy that shows only the last four digits matches the PCI Security Standards Council’s guidance that masking should display only the digits the business need requires, with a customer service agent who needs the last four as its example.

Read encrypted payloads. A bank that encrypts messages at the payload level can load its own custom SerDes into Kpow, as TD does, so authorised users read the decrypted data in Data Inspect while it stays encrypted on the brokers. TD’s talk gives the reason banks end up here: its managed Kafka service was at the time reached over the internet, the bank’s network could not expose card data to it directly, and the answer was a shared library that encrypts every message before it leaves the application. Payload encryption makes the brokers blind to the data and every generic tool with them, so the test for a management tool is whether the bank’s own decryption code can run inside the bank’s own deployment of it.

Watch consumer lag and alert the owning team. Kpow publishes consumer group offsets and lag, with broker, topic and connector metrics, on Prometheus endpoints for Grafana, AlertManager or the bank’s own monitoring, and through the Kpow API. TD’s lag job on Kpow runs every five minutes and lets each team raise its own lag alerts.

Govern Flink and trace lineage. Banks that run Apache Flink beside Kafka can put the same directory sign-in and Allow, Deny or Stage policies over their Flink jobs with Flex, and Factor Platform adds data lineage across Kafka and Flink, the need NORD/LB describes under BCBS 239 and DORA. BCBS 239 is the Basel Committee’s principles for effective risk data aggregation and risk reporting, and for a bank built on events it comes down to three questions about every topic: where the data comes from, who owns it, and whether it contains regulated information.

To see these screens before installing anything, the live Kpow demo needs no signup and shows what a bank’s engineers do every day, data inspect, consumer groups and topic management, on two MSK clusters, with the audit trail in the __oprtr_audit_log topic on MSK Secondary. The demo has no SSO and no data policies configured, so sign-in and masking are the two things to test in the bank’s own environment. To run the workflows against the bank’s own clusters, install Kpow from its container image or JAR; tenants, RBAC, temporary policies, staged mutations, masking and the audit log are Kpow Enterprise features, and Community Edition is free for 3 clusters and 10 users.

Kpow live demo

Open Kpow the way a bank's engineers would

The live Kpow demo needs no signup. Browse clusters, consumer groups and topic data, then read the audit trail on the __oprtr_audit_log topic of the MSK Secondary cluster.

For platform teams running Kafka for many teams inside a bank.

Try the Kpow demo→

FAQ

What is the best Kafka UI for a bank?

On this page’s rubric, Kpow: it grants time-boxed production access through its API, records every action with the person from the bank’s directory, scopes teams with tenants, manages on-prem and cloud clusters from one deployment, and runs as one container with no external database and nothing in the data path. Klaw is the strongest open-source option for request and approval workflows, but it does not inspect data.

How should a bank give engineers access to production Kafka data?

Grant it per task rather than permanently: a request, an approval, access to a named resource for an hour or two, and a record of the grant and of what was read. In Kpow that is a temporary policy, which a change system such as ServiceNow can create through the API, and which expires on its own.

Does a Kafka management tool need to be a proxy to govern access?

No. Governing what people can see and do in a tool, with RBAC, masking in inspection and an audit log, works from a tool that connects as an ordinary Kafka client. A proxy is only needed to enforce policy on application traffic, and it puts a component in front of every producer and consumer.

Can one Kafka tool manage on-prem and Confluent Cloud clusters?

Yes, if the tool is vendor-agnostic. Kpow manages self-managed Apache Kafka, Confluent Platform, Confluent Cloud and Amazon MSK from one deployment, up to 12 clusters per instance. Confluent Control Center documents Confluent Platform clusters only.

Does Kpow work with Kerberos-secured Kafka clusters?

Yes. Kpow takes the standard Kafka client security settings, and its cluster configuration uses SASL with GSSAPI as the default mechanism, with the Kerberos service name and login settings available as environment variables.

How these tools were scored

Six criteria, each taken from a situation bank platform teams have described in public, score every option from 0 to 10. They are listed here in order of weight.

1. Out of the data path. A management tool that runs in the bank’s own environment, connects as an ordinary Kafka client and keeps no data outside the bank’s clusters gives the shortest answer when the bank asks which third parties can see its data. Scored lower: tools that need an external database, 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. This criterion is scored the same way on every Factor House page that uses it, and only its weight changes with the reader.

2. Production access on request. Can an engineer be given read access to a production topic for a task, approved and time-boxed, without a standing grant? Can production writes be held for a second person’s approval? And is sensitive data masked for the people who do get in? TD’s answer is a ServiceNow form that calls the Kpow API and grants inspect access within a minute, scoped for an hour or two, and in Sandy Yang’s words, “This gives TD an audit trail.” The wider set of controls over deletes and offset resets is compared in Kafka destructive operations tools.

3. Audit trail per person. When people work through a shared tool, the broker only sees the tool’s service account, so only the tool’s own log can name the person. A bank needs that log to include data reads as well as changes, and to be readable without building a consumer first. Kafka audit logging tools compares the layers in detail.

4. Directory and Kafka sign-in. People should sign in through the bank’s directory, whether that is LDAP directly or SAML and OIDC in front of Active Directory, with directory groups mapped to roles. TD grants Kpow access by AD group, with production limited to the operations team. On the cluster side the tool has to connect the way the bank’s clusters already authenticate clients, which in many banks means SASL GSSAPI (Kerberos). Kafka SSO tools covers the protocol detail.

5. Many teams, shared clusters. Hundreds of projects on a handful of clusters need each team scoped to its own topics, consumer groups and connectors, so the platform team can onboard a team with a policy rather than a cluster. At TD, a ServiceNow change order ends with the platform team setting up a Kpow tenant and access policy for the requesting team.

6. On-prem and cloud together. Banks rarely run one distribution. TD runs on-prem clusters beside Confluent Cloud, and NORD/LB runs Confluent’s Kubernetes operator in its own environment. One deployment of the tool should reach all of them; the general comparison is best tools to manage multiple Kafka clusters from one place.

The cost figures model a bank running 4 clusters (development, test, pre-production and production) for 100 engineers at $120 an engineer-hour, and each card prints its own assumptions. The general listicle view, without the banking weighting, is in best Kafka management tools.

F1 What a bank runs into, and what the Kafka tool has to do about it

What happens at a bank What the Kafka tool has to do

Reading production data An engineer needs to see what is in a production topic for an hour to diagnose a problem Grant read access on request, time-boxed, through an API a change system can call, and record the grant

Writing to production Producing to a production topic needs a justified exception Deny produce in production by role, and hold any exception for a second person's approval

Directory sign-in People and groups live in Active Directory or LDAP, often behind SAML or OIDC Sign people in through that directory and map its groups to roles

Cluster security Clusters authenticate clients with Kerberos (SASL GSSAPI), SCRAM or mutual TLS Connect with the same client settings any Kafka client uses

Many teams Hundreds of projects share a small number of clusters Scope each team to its own topics, consumer groups and connectors

Mixed topology On-prem clusters run beside Confluent Cloud or another managed service Manage every cluster from one deployment, whatever the distribution

Third-party review Risk and procurement review every component that can see payment or customer data Run inside the bank's environment as a Kafka client, not in front of the brokers as a proxy

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, Production access on request counts twice, Audit trail per person counts twice, Directory and Kafka sign-in counts once, Many teams, shared clusters counts once and On-prem and cloud together counts once, for a total out of 100. Out of the data path counts three times because every component that can see payment or customer data goes through a bank's third-party risk review, and a tool that sits between applications and brokers, or keeps its state in a database of its own, is more to review and one more thing that can fail on the payment path. Production access and the per-person audit trail count twice, because they are the two controls a bank's change process and its examiners ask about first: who could read or change production, and who actually did. Directory sign-in, shared clusters and mixed topology count once. 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 89 out of 100. The other options follow by total. Conduktor is listed last whatever its total; on its total of 59 it would place fourth.

Related reading

• Kafka: the complete guide
• Best Kafka governance tools for financial services
• Running Kafka at bank scale: TD Bank
• NORD/LB case study
• Data lineage support in Factor Platform
• Best tools to manage multiple Kafka clusters from one place