Why Managed Kafka Isn’t Enough: The Case for Full Streaming Platforms

When enterprises start adopting real-time data streaming, the natural place to begin is Kafka. It’s fast, scalable, and durable. Managed Kafka services make that start easier, taking care of broker provisioning, cluster health, and basic metrics. But that’s exactly the issue: they only solve for Kafka the infrastructure, not Kafka in production. 

Here’s what’s often missed: Kafka is not a streaming platform. It’s the backbone of one. And stopping there leads to an incomplete, brittle architecture that slows down every team that touches data. 

Let’s get precise. 

What Managed Kafka Actually Offers 

Managed Kafka services, whether from Confluent Cloud, MSK, or Aiven, essentially focus on operating the Kafka cluster itself: 

• Provisioning brokers 

• Upgrading versions 

• Scaling partitions 

• Handling replication 

• Offering a UI and API for topic management 

• Limited integrations (e.g., schema registry, private link) 

This simplifies Kafka-as-a-service, not streaming-as-a-service. What’s missing is everything between ingest and outcome, where your business logic actually lives. 

And that’s where friction begins. 

Real-Time Streaming Is an Application Problem, Not a Broker Problem 

Kafka does an excellent job at moving events. But streaming is not about moving data, it’s about reacting to it. 

Once events are in a topic, here’s what your application still needs to handle: 

• Join event streams to reference tables or time windows 

• Correlate user behavior across sessions 

• Detect anomalies in sensor data 

• Convert raw JSON into structured, validated formats 

• Push alerts to APIs, dashboards, or mobile devices 

• Write enriched outputs to Postgres, S3, or Elasticsearch 

None of these responsibilities are handled by Kafka brokers. Managed Kafka offloads cluster administration, not stream application complexity. 

What Teams End Up Building Anyway 

Despite using a managed Kafka service, most engineering teams are forced to build and operate a second layer of infrastructure to make the system usable: 

• Stream Processors: Flink, Spark Structured Streaming, Kafka Streams 

• Orchestration: Airflow, Argo, Prefect 

• State Management: Redis, RocksDB, custom joins 

• Observability: Prometheus, Grafana, OpenTelemetry 

• Connector Runtime: Kafka Connect clusters or custom scripts 

• CI/CD for logic: Build pipelines for deployable transforms 

• Glue Code: Everything that ties the above together 

And this is where the cost shifts. Not financial cost, operational complexity. It becomes harder to debug, harder to onboard new developers, and nearly impossible to replicate across environments. 

The Limits of Kafka Connect + ksqlDB 

Many managed services offer Kafka Connect and ksqlDB as add-ons. While useful in simple pipelines, they fall short at scale: 

• Kafka Connect requires careful scaling, fault-tolerant config, and constant tuning. 

• ksqlDB has limited support for joins, lacks GitOps-native CI/CD, and isn’t always cloud-native 

• Custom transforms? Still need to be written and deployed using external systems like Flink or microservices. 

These tools extend Kafka’s usability but do not constitute a true streaming platform. They don't unify the control plane, data plane, and application logic into a deployable, observable system. 

What Full Streaming Platforms Actually Provide 

A streaming platform offers a coherent, opinionated way to do Real-Time Data Streaming end-to-end. Not just storage and ingestion, but processing, enrichment, deployment, governance, and delivery. 

Specifically: 

With full streaming platforms, your team doesn’t have to glue together 10 tools to deliver one feature. They build logic, deploy it, and observe it natively, within the platform. 

Why Condense Was Designed This Way 

Condense is not just Kafka hosting with a UI. It’s a Kafka Native Streaming Platform designed to make production real-time pipelines fast to build, safe to operate, and easy to evolve. 

Here’s how it goes beyond managed Kafka: 

• Kafka: Fully deployed in your cloud (BYOC), with support for VPC peering, IAM, logging, scaling 

• Transforms: Run as containerized logic inside the platform, version-controlled via Git 

• Built-in IDE: Developers write and test logic without managing Flink jobs or services 

• Utilities: Prebuilt operators like alert(), join(), window(), route(), split() 

• Stream App Deployment: Each app is a full DAG: data in, logic applied, outputs routed 

• Observability: You see errors, retries, output stats, and per-event lineage 

• Connectors: Ingest and output from MQTT, HTTP, JDBC, Kinesis, S3, Postgres, etc. 

• Marketplace: Import ready-to-use domain logic: trip segmentation, SLA scoring, etc. 

It's Kafka under the hood. But it's more than Kafka it's the platform that Kafka alone can’t become. 

Final Thoughts: Choosing Infrastructure vs Choosing Outcomes 

If you’re evaluating a managed Kafka service, ask yourself: 

• Will it let me deploy and monitor stream logic natively? 

• Will I need to hire a team just to manage the rest of the pipeline? 

• Can my developers iterate without setting up stream engines separately? 

• Can I control where Kafka and logic run (BYOC), or am I locked in? 

• What’s the time from raw event to business insight? 

Managed Kafka solves one layer. A full streaming platform solves the problem. 

If Real-Time Data Streaming is more than just ingestion in your business, it’s time to look beyond brokers. What Condense adds is the missing platform layer, so teams stop wiring systems and start delivering outcomes.