Choosing how to run Apache Kafka is one of the most consequential infrastructure decisions for any organization building a streaming data platform. The options range from fully managed services like Confluent Cloud to self-hosted deployments on your own infrastructure. Each approach carries distinct trade-offs in cost, operational complexity, control, and scalability. The right choice depends on your team's Kafka expertise, your infrastructure strategy, your compliance requirements, and how much operational responsibility you are willing to take on. This guide breaks down both options and helps you determine which fits your needs.
Managed streaming for Kafka refers to cloud services that operate Apache Kafka clusters on your behalf. The provider handles infrastructure provisioning, broker management, patching, upgrades, scaling, and monitoring. You interact with Kafka through APIs and configuration, without managing the underlying servers, storage, or networking.
Confluent Cloud is the most prominent managed Kafka offering, built by the original creators of Apache Kafka. Other options include AWS Managed Streaming for Apache Kafka (MSK) and cloud marketplace deployments. The core value proposition is the same across all managed services: reduce the operational burden of running Kafka so your team can focus on building applications and processing data rather than managing infrastructure.
Confluent Cloud is not simply Kafka hosted on cloud VMs. It is a cloud-native reimplementation built with elastic compute and infinite storage architecture. Clusters deploy across AWS, Azure, and Google Cloud, with billing available through cloud provider marketplaces. Storage is unlimited at the cluster level, eliminating the per-broker storage limits that force over-provisioning in self-hosted setups.
Clusters are self-balancing: when you add capacity, data rebalancing happens automatically without manual intervention. Upgrades are automatic and non-disruptive, with rolling updates applied by the Confluent engineering team. Proactive vulnerability patching removes the security maintenance burden from your operations team.
Beyond core Kafka, Confluent Cloud includes a fully managed ecosystem. Schema Registry enforces data contracts between producers and consumers, preventing schema evolution from breaking downstream applications. ksqlDB provides SQL-based stream processing directly on Kafka topics, enabling transformations and aggregations without deploying a separate processing framework.
The platform offers over 120 fully managed source and sink connectors, covering databases, cloud services, data warehouses, and SaaS applications. In a self-hosted or basic managed setup, these connectors require separate deployment and management. Confluent Cloud handles connector infrastructure, scaling, and monitoring as part of the service.
Self-hosted Kafka means you deploy, configure, and operate Kafka clusters on infrastructure you control. This can be on-premise servers in your own data center or cloud VMs (EC2, GCE, Azure VMs) that you manage. In both cases, your team is responsible for broker sizing, storage provisioning, network configuration, security setup, monitoring, upgrades, and capacity planning.
A typical self-hosted production cluster requires at least three broker nodes, a ZooKeeper ensemble (or KRaft controllers), and separate infrastructure for Schema Registry, Kafka Connect, and monitoring. A realistic cloud deployment might involve six or more instances plus dedicated storage volumes.
KRaft (Kafka Raft) is the newer consensus mechanism that replaces ZooKeeper for cluster metadata management. It simplifies the architecture by eliminating the separate ZooKeeper ensemble, reducing the number of components to deploy and monitor. KRaft also improves partition scalability and speeds up controller failover.
For self-hosted deployments, KRaft reduces operational complexity by removing ZooKeeper from the stack. However, you still manage the KRaft controllers as part of your Kafka deployment. The migration from ZooKeeper to KRaft requires planning and testing, particularly for clusters with large partition counts.
Confluent Cloud uses consumption-based pricing. The Basic tier starts at $0.0096 per partition-hour plus data transfer fees. The Dedicated tier, designed for production workloads with private networking, starts at $1.50 per hour per Confluent Kafka Unit (CKU). A moderate workload typically costs around $200 per month on Confluent Cloud.
Self-hosted Kafka on cloud VMs involves fixed infrastructure costs regardless of utilization. A production cluster with six EC2 instances costs approximately $1,100 per month for compute, plus around $300 per month for 3TB of EBS storage. Total infrastructure costs range from $850 to $1,500 per month before accounting for the engineering time required to manage the cluster.
The critical cost factor for self-hosted Kafka is personnel. Operating a production Kafka cluster requires dedicated expertise in capacity planning, performance tuning, security configuration, and incident response. This operational overhead often exceeds the infrastructure costs and is the primary driver behind managed service adoption.
| Factor | Confluent Cloud | Self-hosted Kafka |
|---|---|---|
| Pricing model | Pay-per-use (partitions, data, CKUs) | Fixed infrastructure + personnel costs |
| Infrastructure | Fully managed, elastic | You provision and manage servers |
| Storage | Unlimited, scale-to-zero | Per-broker limits, manual expansion |
| Upgrades | Automatic, non-disruptive rolling updates | Manual, you ensure availability |
| Rebalancing | Self-balancing clusters | Manual rebalancing after expansion |
| Connectors | 120+ fully managed | Community connectors, self-managed |
| Schema Registry | Included, fully managed | Separate deployment required |
| Stream processing | ksqlDB and Flink included | Separate Flink/Spark deployment |
| Monitoring | Pre-aggregated metrics included | Custom setup (Prometheus, Grafana) |
| Security | Encryption, RBAC, SSO, PrivateLink | Configurable SSL/TLS, SASL, your responsibility |
| Multi-cloud | AWS, Azure, GCP natively | Requires separate clusters per cloud |
| Control | Limited to platform capabilities | Full control over configuration |
Confluent Cloud eliminates most operational tasks. Broker provisioning, scaling, patching, upgrades, and monitoring are handled by the platform. Your team configures topics, manages access policies, and builds applications.
Self-hosted Kafka requires your team to handle every operational aspect: broker sizing based on performance testing, storage capacity planning, security patching, version upgrades (with availability management during the process), partition rebalancing after expansion, and continuous monitoring setup. Each of these tasks requires Kafka-specific expertise that many organizations struggle to hire and retain.
Confluent Cloud scales elastically. You increase capacity by adjusting CKUs, and the platform handles rebalancing automatically. Storage scales without limit. The consumption-based model means you pay only for what you use, including scaling to zero during periods of inactivity.
Self-hosted Kafka scales by adding brokers, which requires performance testing to select the right instance types, followed by manual partition rebalancing across the expanded cluster. Storage is limited per broker, so retention requirements may force you to add brokers even when compute capacity is sufficient. Scaling down is operationally complex and rarely done in practice.
Confluent Cloud provides encryption at rest and in transit, role-based access control (RBAC), single sign-on (SSO) integration, and PrivateLink for network isolation. Security patches and vulnerability fixes are applied proactively by the Confluent team.
Self-hosted Kafka offers full control over security configuration: SSL/TLS, SASL authentication mechanisms, and network isolation through your own VPC design. This control is necessary for organizations with specific compliance mandates that require on-premise deployment or custom encryption configurations. The trade-off is that every security measure must be implemented, tested, and maintained by your team.
Confluent Cloud bundles the full Confluent ecosystem: Schema Registry, ksqlDB, managed connectors, and Flink-based stream processing. These components are integrated and managed as a single platform.
Self-hosted Kafka provides core Kafka functionality. Schema Registry, Kafka Connect, and stream processing frameworks must be deployed and managed as separate infrastructure components. Community-built connectors are available but require self-management, including version upgrades, scaling, and troubleshooting.
Confluent Cloud is the stronger choice when your team has limited Kafka operational expertise, when speed to production matters, or when you need the full ecosystem (Schema Registry, connectors, stream processing) without managing additional infrastructure. It also fits well for multi-cloud strategies, since it deploys natively across AWS, Azure, and Google Cloud from a single platform.
Organizations that want consumption-based pricing with elastic scaling and prefer to allocate engineering time to application development rather than infrastructure management will benefit most from Confluent Cloud.
Self-hosted Kafka makes sense when you need full control over every configuration parameter, when compliance mandates require on-premise deployment, or when your team has deep Kafka expertise and the capacity to manage production clusters. It can also be more cost-effective for very high-throughput workloads where fixed infrastructure costs are lower than consumption-based pricing at scale.
Organizations with strict data sovereignty requirements that prevent cloud deployment, or those with specialized hardware or network configurations that managed services cannot accommodate, should consider self-hosted Kafka.
Many organizations adopt a hybrid model. Development and staging environments run on Confluent Cloud for speed and simplicity, while production workloads with specific compliance or latency requirements run on self-hosted clusters. Some teams use Confluent Platform (the enterprise distribution of Kafka for self-managed deployment) alongside Confluent Cloud, maintaining consistent tooling across both managed and self-hosted environments.
Another common pattern is running self-hosted Kafka on-premise for data sovereignty reasons while replicating a subset of topics to Confluent Cloud for analytics and cross-region distribution. Kafka's MirrorMaker 2 and Confluent's Cluster Linking enable this type of hybrid replication.
Mimacom is a certified Confluent and Apache Kafka partner with deep expertise in both managed and self-hosted streaming architectures. Mimacom helps organizations evaluate their requirements, choose the right deployment model, and implement production-grade Kafka platforms. Services include architecture reviews, Kafka cluster design and deployment, migration from self-hosted to Confluent Cloud (or the reverse), performance tuning, and ongoing managed operations.
Let our streaming experts guide you through the architectural and operational trade-offs so you can make the right decision for your organization.
No. Confluent Cloud is a cloud-native reimplementation of Kafka, not a hosted version of open-source Kafka on virtual machines. It uses an elastic compute and infinite storage architecture that is fundamentally different from traditional Kafka broker deployments. Features like self-balancing clusters, automatic non-disruptive upgrades, and scale-to-zero pricing are not available in standard Kafka. The platform also includes fully managed Schema Registry, ksqlDB, Flink-based stream processing, and over 120 connectors as integrated services.
A self-hosted production Kafka cluster on cloud VMs typically costs between $850 and $1,500 per month for infrastructure alone (compute instances plus storage). This does not include the personnel costs for managing the cluster, which often exceed the infrastructure spend. A moderate workload on Confluent Cloud costs approximately $200 per month with consumption-based pricing. However, at very high throughput levels, self-hosted infrastructure can become more cost-effective per gigabyte processed, since fixed costs do not scale linearly with usage the way consumption-based pricing does.
KRaft (Kafka Raft) is the consensus mechanism that replaces Apache ZooKeeper for Kafka cluster metadata management. It eliminates the need for a separate ZooKeeper ensemble, reducing the number of components to deploy and monitor. KRaft improves partition scalability and speeds up controller failover. For new self-hosted deployments, KRaft is the recommended approach. For existing clusters running ZooKeeper, migration to KRaft requires careful planning and testing, particularly for clusters with large partition counts. Confluent Cloud abstracts this entirely, as the underlying consensus mechanism is managed by the platform.