ksqlDB licensing and open-source alternatives: what to settle before you build
ksqlDB ships under the Confluent Community License, which the Confluent FAQ lists among its components and describes as source-available and not OSI approved. Apache Kafka itself is released under the Apache License 2.0 (Apache-2.0), and the Apache Software Foundation project offers no paid edition. That means a team that adopts ksqlDB as its SQL layer over self-hosted Kafka is adopting a different license, with different terms, from the broker it runs. Read the Confluent Community License FAQ before the architecture review, not after the first production dependency is in place.
The decision matters in three situations. The first is building an internal application or pipeline on ksqlDB and running it for your own staff or systems. The second is offering ksqlDB-backed functionality to customers, because the managed-service restriction in the license is the term most likely to affect a hosted product. The third is self-hosting ksqlDB yourself, where you carry the license obligations directly and must confirm that your intended use falls within them. Ask counsel to review the license against your exact deployment model, and document the answer. Also confirm the license version applied to the release you intend to deploy, since terms can change between releases.
If the license terms do not fit, the open-source alternatives are the Kafka Streams library, which is part of Apache Kafka and therefore Apache-2.0, and Apache Flink SQL, which provides SQL over streams under its own Apache license. Kafka Streams requires writing Java or Scala applications rather than SQL statements, so the trade-off is developer effort against licensing exposure. Before choosing, list the queries you need, the state they hold, the team skills available, and the operational owner for each option. Then test one representative query on each candidate, and confirm the connector, state and deployment support you require with the project documentation.
How self-hosting ksqlDB changes the picture by use case
When ksqlDB runs only for your own employees and internal systems, the question is mostly about whether your use fits the license terms as written and whether anything you do with the software triggers a restriction. Teams usually run it to query and transform Kafka topics for analysts, operations staff or internal dashboards. Legal should read the license text against your actual deployment rather than relying on a general summary.
- Internal purpose: Confirm that the cluster serves your own business and is not offered to outside parties.
- Version and source: Record which ksqlDB build you run and where you obtained it, so the license version applied is documented.
- Modifications: Note any patches or custom builds, since changes can carry their own obligations.
- Access by contractors: Check whether outside consultants operating the cluster for you raise questions under the terms.
- Evidence: Keep a written license review in the change record for the platform.
Ask legal to confirm the position in writing and ask the vendor to clarify any term that is unclear for your setup.
When ksqlDB ships inside a product your customers install, run or access, the license question moves from internal use to distribution and redistribution. Whether you bundle the component, call it through an API your product exposes, or only use it behind the scenes can lead to different answers. Your product team and legal counsel should map each of these paths before the design is fixed.
- Bundling: Determine whether you distribute ksqlDB binaries or images to customers or to on-premises installs.
- Hosted or delivered access: Check whether customers reach ksqlDB features indirectly through your application.
- Notices and attribution: Identify what the license requires you to include when you redistribute.
- Replacement cost: Estimate the engineering effort to swap in an alternative if the terms do not fit, using the Kafka Streams option described on this page as a reference point.
- Customer contracts: Review whether your customer agreements promise capabilities that depend on a component you may need to change.
Before release, get a written opinion from counsel and confirm the distribution model with the vendor.
Offering ksqlDB as a managed service to other organizations is the situation the Confluent Community License restricts most directly. The license FAQ addresses providing the software to third parties as a managed service, so a hosted offering needs a specific legal answer rather than an assumption. This is the scenario where the choice of open-source alternative is most likely to be decided by legal review rather than engineering preference.
- Managed service definition: Ask counsel how the restriction applies to your exact service model, including multi-tenant and dedicated deployments.
- Commercial license: Ask the vendor whether a commercial agreement is available for your intended offering.
- Customer-facing SQL: Consider whether customers writing their own queries changes the answer.
- Exit path: Plan a migration option, such as Kafka Streams or Apache Flink SQL, in case the terms cannot be met.
- Timing: Resolve the question before onboarding customers, because changing components later affects contracts and operations.
Do not launch a hosted offering on ksqlDB until counsel and the vendor have confirmed the position in writing.
Some organizations run one Kafka platform for many internal teams, and ksqlDB is offered as a shared service. This sits between internal use and a hosted offering because the users are inside the company, but the platform team acts like a provider. The license question depends on whether your teams are separate legal entities, whether they are affiliates, and how access is granted. Platform owners should document the answer before offering ksqlDB as a catalog option.
- Legal entities: Confirm whether the teams using the service belong to one company or to affiliated companies.
- Self-service provisioning: Check whether teams can create their own ksqlDB applications without central review.
- Chargeback: Note that charging internal teams for usage may change how a counsel reads the arrangement.
- Governance: Record which teams may use the platform and the approval path for new use.
- Documentation: Keep the answer in the platform's service catalog so future teams do not assume it is settled.
Ask legal to review the service model before the platform is announced to other business units.
If the answer for your situation is unclear or unfavorable, the practical next step is to compare alternatives that carry the Apache License 2.0. Apache Kafka itself is released under that license and has no paid edition from the project. Kafka Streams is a Java library in the same project, and Apache Flink SQL is another open-source option. Each path changes the build, skills and operating model, so the comparison should start with the use case rather than the license alone.
- Kafka Streams: Embeds stream processing in Java applications and avoids a separate query server, which changes deployment and ownership.
- Apache Flink SQL: Provides SQL over streams with its own cluster and state management to run and monitor.
- Application code: Producers and consumers written with a Kafka client can cover simple transformations.
- Skills and tooling: Compare the team's Java, SQL and operations experience with each option.
- Migration cost: Estimate the rewrite of existing queries and the testing needed to confirm equivalent output.
Confirm the license of any alternative you choose with your own legal review, since the Apache License 2.0 applies to Apache Kafka but each dependency and connector needs its own check.
Open-source options for stream SQL and processing on Kafka
Each route below replaces some part of what ksqlDB provides as a server-side SQL layer with a different split between application code, SQL and operated infrastructure. Compare them on who writes the logic, where state lives and how much platform work your team takes on before choosing one.
Kafka Streams for Java logic
Kafka Streams is a client library, so your processing runs as ordinary Java applications inside your own deployment, with no separate query server to license or operate. It suits teams whose stream logic is code-heavy, stateful and owned by engineers. The team builds what ksqlDB handles for you: a SQL-style interface for analysts, a deployment pipeline for each application, and its own monitoring and state recovery. Confirm the current license of the Kafka Streams library in the repository before you adopt it.
Apache Flink SQL for analysts
Flink SQL gives SQL-first teams a declarative way to define streaming jobs over Kafka topics, closer to ksqlDB's model, while running on a separate Flink cluster. The operational footprint is larger: you run JobManagers, TaskManagers, checkpoint storage and job lifecycle tooling. Your team must build the catalog of tables, the job deployment process and the alerting on checkpoint failures. Confirm the license and the supported connector versions for your Kafka release before committing.
Kafka Streams state and recovery
Stateful operators such as aggregations and joins keep local state that is backed by changelog topics on Kafka. Recovery time depends on how much state each instance must rebuild, so sizing and storage choices matter as much as the SQL or Java you write. With ksqlDB, the server manages this; with Kafka Streams, your team designs it. Plan persistent volumes, standby replicas and restore testing before production traffic arrives.
Exactly-once settings on your own stack
Whichever processor you choose, the processing guarantee is only as strong as the client and broker settings around it. Idempotent producers, transactional writes and read-committed consumers must be configured deliberately, and some settings silently disable protection. Your team owns this verification in a self-built stack, including tests that inject failures. Confirm the Kafka version you run supports the guarantee you need.
Access control across processors
Each processing layer authenticates to Kafka with its own identity, and each needs ACLs for the topics it reads, writes and creates for internal state. In KRaft clusters, ACLs live in the metadata log, and resources without an ACL are restricted to super users unless you change that default. Your team must map service identities, internal topic prefixes and consumer groups to ACLs for every processing application, not only for one server-side component.
Build, release and version discipline
Open-source processors move independently of your Kafka brokers, so each application needs a tested version pairing with the broker and client libraries. Java applications require at least Java 11 for Kafka Streams clients, and brokers require Java 17 from Kafka 4.0. Your team must set a release calendar, a rollback path and a test environment that matches production topics. Ask whichever partner or vendor you use to write this policy into the scope.
Which option fits which team, compared on the criteria that matter
This table compares ksqlDB, Kafka Streams and Apache Flink SQL on licence, interface, state handling, deployment model and Kafka or Java requirements. Read each row against your own legal review and platform standards, because the rows show where each option changes your obligations. The main takeaway is that the licence question is decided first, and the engineering fit follows from it.
| Criterion | ksqlDB | Kafka Streams | Apache Flink SQL |
|---|---|---|---|
| Licence | Confluent Community License, which is source-available and not OSI approved | Apache License 2.0, as part of the Apache Kafka project | Confirm the licence of the Flink release you plan to run |
| Open-source status under OSI definition | No | Yes | Confirm against the release you select |
| Language and interface | SQL statements submitted to a server, plus a REST interface | Java library embedded in your own application | SQL client and SQL API, with Java and Python APIs available |
| State handling | Managed on the ksqlDB servers, backed by Kafka topics. Confirm the recovery steps | Local state stores backed by changelog topics in Kafka | Checkpointed state in external storage. Confirm the storage and recovery setup |
| Deployment model | Separate server cluster that you install, upgrade and operate | Part of each application, deployed with your existing services | Separate Flink cluster or Kubernetes deployment that you operate |
| Kafka and Java requirements | Confirm the supported Kafka and Java versions for your ksqlDB release | Java 11 or later for Kafka Streams applications, per the Kafka upgrade notes | Confirm the Kafka connector version and Java version for your Flink release |
| Fits teams that | Already run the licence terms with legal approval and want SQL over Kafka topics | Write Java services and want an open-source stream library with no separate server tier | Need SQL analytics and a separate processing cluster under an open-source licence |
| What to confirm first | Whether your intended use falls within the Community License terms, including any managed-service restriction | Your Java and Kafka version alignment and state recovery targets | Licence of your Flink release, connector support and operating model for the cluster |
How to validate your licensing position before writing code
This sequence turns a licensing question into a documented decision, so the team knows which components it can use, how it can use them, and which Kafka release it will prototype on before any build work starts.
Inventory every Kafka component in the stack
List each component the design touches: brokers, clients, Kafka Streams, Kafka Connect connectors, ksqlDB, schema tooling, and any managed or vendor-packaged distribution. Record the exact package, image or repository each one comes from and who maintains it. The step is done when the inventory has one row per component with its source, version and owner, and nothing is marked as "probably open source" without a check.
Confirm each license from its own source
For every row, read the license file or the vendor's license page in the repository or distribution you will actually use, not a secondhand summary. Apache Kafka is released under the Apache License 2.0, but ksqlDB is listed among components under the Confluent Community License, which Confluent describes as source-available and not OSI approved, so it must be checked separately. The step closes when each component has a recorded license name, a link to the governing text and a note on any terms that restrict use.
Document the intended use of each component
Write down how the platform will use each component: internal processing only, embedded in a product shipped to customers, offered as a hosted or managed service to third parties, or some combination. Restrictions in source-available licenses often depend on exactly this distinction, so the same component can be acceptable in one use and not in another. The step is complete when each component has a one-paragraph use statement that a lawyer or engineer could review without asking follow-up questions.
Obtain counsel review of the license position
Send the inventory, the license texts and the use statements to qualified counsel for a written opinion on each component and use case. DeployOpen does not provide legal advice, so this review sits with your legal team or outside counsel. Ask counsel to confirm the managed-service restriction, any obligations on modifications and redistribution, and whether the chosen alternative changes the position. The step closes when you hold a signed or dated record of the review and any conditions attached to it.
Select the open-source option and its replacement path
Based on the reviewed position, decide whether ksqlDB stays in scope or whether the design uses open-source alternatives such as Kafka Streams or Apache Flink SQL. Map each existing ksqlDB query or feature to its replacement and note what changes in deployment, state handling and operations. The step produces a decision record listing the chosen components, the rejected options and the reason for each, so the choice can be audited later.
Prototype on a supported Kafka release
Build a small prototype of the chosen option on a Kafka release listed as supported on the downloads page, such as 4.3.1, 4.2.2 or 4.1.2, and confirm the Java runtime your applications require. Current guidance is Java 17 for brokers, Connect and tools, and at least Java 11 for clients and Kafka Streams applications. Run the prototype against a test topology that exercises the real topics, consumer groups and failure cases. The step is done when the test topology passes, the runtime is agreed in writing, and the team has a record of the release and Java version used.
Record the position and reopen it on change
Store the inventory, license evidence, use statements, counsel record, decision record and prototype results together in one place the team can find. Set a trigger to revisit the position when a component changes license, when the intended use changes, or when a new Kafka release changes the supported set. The step is complete when the record has an owner and a named review trigger, so the licensing position stays valid after the prototype becomes production code.
Security and operations to plan for each option
Whichever processing layer you choose, the Kafka cluster underneath carries the same authentication, encryption and authorization decisions. Plan these once at the cluster level, then give each processing component its own identity so access can be granted and revoked without affecting the others.
Choose SASL or SSL authentication
Kafka authenticates clients, brokers and tools with SSL or SASL. The documented SASL mechanisms are GSSAPI (Kerberos), PLAIN, SCRAM-SHA-256, SCRAM-SHA-512 and OAUTHBEARER. Pick one mechanism per listener based on your identity provider, and confirm which mechanisms each client library and processing framework supports before you commit, because a licensed component may only support some of them.
Encrypt traffic with TLS
Kafka encrypts traffic between clients, brokers and tools with SSL/TLS, but security is optional. A cluster can run unsecured or mix secured and unsecured listeners, which is convenient during testing and risky in production. Confirm that every processing component connects over a secured listener, and plan certificate issuance, rotation and trust distribution before the first application goes live.
Plan ACLs before the first client connects
In KRaft clusters, the StandardAuthorizer stores ACLs in the cluster metadata log. Resources without ACLs are restricted to super users unless allow.everyone.if.no.acl.found is set to true. Leave that setting at its default so that a new topic or consumer group does not open to everyone by accident, and write explicit ACLs for each topic, group and transactional ID your processing layer uses.
Give each component its own identity
Each processing option needs its own service identity and credentials, such as a separate SCRAM user or Kerberos principal for ksqlDB, Kafka Streams applications, Flink jobs and any Spark pipeline. Separate identities let you scope ACLs to the topics each component reads and writes, rotate one set of credentials without an outage elsewhere, and show auditors which component touched which data.
Review the authorization integration
Kafka authorization is pluggable and supports integration with external authorization services. If your organization already manages access centrally, confirm whether the external service can express the per-topic and per-group rules your processing jobs need, and test how it behaves when the external service is unavailable. Do not assume that a processing framework enforces access on its own.
Confirm licensed component access controls
If a processing component is under a source-available license, check its own authentication and access features against the Kafka controls above. Confirm which options are in the community edition and which require a commercial license, and ask the vendor in writing. Test each component against your chosen SASL mechanism and TLS setup in a non-production cluster before you rely on it.
Questions teams ask about ksqlDB licensing and alternatives
Can we self-host ksqlDB inside our own company?
Self-hosting ksqlDB for your own internal use is generally a question for your legal team to read against the license terms, because ksqlDB is distributed under the Confluent Community License rather than an open-source license. Confluent's own license FAQ lists ksqlDB among the components under that license and describes it as source-available and not OSI approved. The restriction most teams need to check is the one on offering ksqlDB to third parties as a managed service. Confirm the exact wording against the current license text and FAQ before you commit, and record the reading your counsel agreed to.
What does a ksqlDB license review cost, and who pays for it?
A license review is usually paid for by the organization that owns the decision, either through your in-house legal function or an outside counsel engaged directly. DeployOpen does not publish a price list, and any review of license terms is a legal matter that DeployOpen does not provide as legal advice. The cost depends on how many products, customer-facing services and jurisdictions are involved, and whether your use is internal or offered to others. Ask counsel for a written scope that names the license versions and the exact use case, so the review is repeatable when the product changes.
Is Apache Kafka itself free to use under a permissive license?
Apache Kafka is an Apache Software Foundation project released under the Apache License 2.0 (SPDX: Apache-2.0), and the project itself offers no paid edition. That is a different license from ksqlDB, so a platform built on Kafka does not automatically inherit the same terms for every component around it. Check the license of each add-on you plan to run, including stream processors, connectors and schema tools. The Kafka downloads page lists the supported releases you should build against.
How long does migrating a ksqlDB workload to Kafka Streams or Flink SQL take?
No duration is given here because the answer depends on the workload, and no researched figure covers it. The main drivers are the number of persistent queries, how much state each one holds, the number of downstream consumers that rely on the output topics, and whether the team already runs Java services. Kafka Streams is a library you build into an application, while Flink SQL runs on a separate Flink deployment, so each path adds different platform work. Ask any provider for a written inventory of queries and a phased cutover plan before accepting a timeline.
What does a migration cost depend on?
The cost is driven by the work your own team and a partner would perform: inventorying queries, rewriting logic, testing output parity, sizing new infrastructure, and cutting over consumers. One-time costs cover the rewrite and testing, while recurring costs cover running the new runtime, its monitoring and upgrades. Costs rise with stateful queries that need state migration, strict exactly-once requirements, and regulated data that needs extra evidence. Ask for a quote that lists each query, the target technology, the test approach and who operates the result afterward.
What must our team provide before an engagement starts?
Your team should provide a complete list of ksqlDB statements, the topics and schemas they read and write, the consumers that depend on the output, and the current throughput and retention settings. You also need access to the existing cluster, a test environment that mirrors production security settings, and a named owner for business sign-off on output parity. Identify who approves license conclusions and who will run the platform after handover. Missing inventory data is the most common reason a scope grows after it is agreed.
Can our own engineers evaluate the options before hiring help?
Yes, a capable internal team can run a first evaluation without outside help. Start by reading the Kafka security overview and the upgrade notes for the release you plan to use, then run a small proof of concept of each candidate with one representative query. Measure state size, restart behavior, and how the output compares with the ksqlDB result. Bring in a partner when the inventory is large, the output feeds regulated decisions, or the team lacks Java or Flink operations experience.
Which Kafka release should a new platform target?
Target a release listed as supported on the Kafka downloads page, which currently includes 4.3.1, 4.2.2 and 4.1.2. The project aims for three releases a year and supports rolling upgrades from each of the last three releases to the latest version. Kafka 4.0 supports only KRaft mode, so any ZooKeeper-based cluster must migrate first. Confirm the Java version your clients and Streams applications need, since clients and Kafka Streams applications require at least Java 11.
How DeployOpen scopes stream processing decisions on Kafka
DeployOpen plans, implements, hardens and documents self-hosted open-source platforms inside your own infrastructure, including the stream processing layer built on Apache Kafka. For ksqlDB licensing and open-source alternatives, we start by confirming which license terms apply to your planned use before any engineering begins.
How we scope
- Confirm which components you plan to run and whether any fall under the Confluent Community License.
- Record how you intend to offer the service internally or to customers, including any managed-service use.
- Agree a fixed-scope written document covering deliverables, timelines and out-of-scope items before work starts.
How we build
- Compare ksqlDB, Kafka Streams and Apache Flink SQL against your queries, state needs and operating model.
- Build the chosen processing layer on your Kafka cluster with versions matched to supported Kafka releases.
- Test processing logic, failure recovery and restart behavior in a non-production environment you control.
How we hand over
- Document the license decision, its basis and the review points for future changes in terms.
- Deliver runbooks for deployment, configuration, upgrades and rollback of the stream processing components.
- Walk your engineers through the code, configuration and operations so your team can run the system alone.
How we support
- Offer ongoing support, upgrades and continuous testing for the processing layer as a retainer.
- Review license and release notes when Kafka or the processing components change, and report what affects your setup.
- Recommend that your legal or compliance advisers confirm license interpretation for your specific use before you commit.
