In the world of streaming, ingestion and event-driven architectures, you’ll often come across two major managed services: Azure Event Hubs (Event Hubs) and Confluent Cloud. Though they overlap, they serve different organisational needs, technical maturity levels and architectural strategies. This article helps you decide which to adopt (or when to switch) based on your scenario.
Table of Contents
Overview of Each Service
Azure Event Hubs
- Fully managed streaming ingestion service from Microsoft Azure, designed for high-throughput event ingestion (telemetry/logs/IOT) and built for the Azure ecosystem.
- Supports Kafka-protocol endpoint so existing Kafka producers/consumers can connect.
- Tight integration with Azure services (Stream Analytics, Azure Functions, ADLS, Synapse, etc).
- Simpler to start if you’re wholly in Azure and your use case is focused on ingestion. As one article puts it: “shines for users already invested in the Azure ecosystem”.
Confluent Cloud
- A fully managed data-streaming platform based on Apache Kafka by Confluent. It includes Kafka brokers, schema registry, connectors, stream processing, multi-cloud/hybrid support.
- Multi-cloud, hybrid and edge support , offers flexibility if you aren’t locked into one cloud.
- Richer ecosystem: connectors, ksqlDB, governance features, enterprise features built on top of Kafka.
Get enterprise-grade data engineering solutions to unlock advanced analytics and AI readiness
Strengths & Limitations
Here’s a comparison of the strengths and limitations of each.
| Service | Strengths | Limitations |
|---|---|---|
| Event Hubs | • Easy Azure integration • Fully managed with minimal overhead • Kafka protocol support for simpler migration (Microsoft Learn) |
• Partial Kafka compatibility (not a full Kafka ecosystem) (Kai Waehner) • May lack advanced streaming features such as rich connectors and governance • More tightly coupled to the Azure ecosystem |
| Confluent Cloud | • Full-featured Kafka-based streaming platform • Multi-cloud and hybrid support • Enterprise tooling such as Schema Registry, connectors and governance (XenonStack) |
• Higher cost and operational complexity • Steeper learning curve for teams new to Kafka • Requires more upfront architecture and design effort |
Decision Criteria – When to Use Which
Use Event Hubs when:
- Your primary cloud is Azure and you want tight integration with other Azure PaaS services.
- The use case is event ingestion or telemetry/log streaming into Azure Data Lake, Synapse, etc.
- You want a simpler setup, fast time-to-value, and can accept fewer “enterprise streaming platform” features.
- You don’t need full Kafka ecosystem features (or your Kafka usage is moderate/simple).
Use Confluent Cloud when:
- You require a full streaming platform, not just ingestion: multiple teams, many producers/consumers, governance, schema management, connectors.
- You need multi-cloud or hybrid deployment (e.g., on-prem + cloud), or portability across clouds.
- You are building mission-critical, high-throughput streaming workloads, where you want leverage of Kafka ecosystem.
- You expect many downstream consumers, diverse data use cases, data-product mindset rather than just event ingestion.
Case Study : Nearly $40K AWS Cloud Cost Reduction in Just 6 Weeks
Example Scenarios & Recommendations
- Scenario A: A mobility IoT company streaming sensor data in Azure → storing into ADLS + Azure Synapse + Power BI. They mostly want ingestion and analytics.→ Go with Event Hubs — minimal friction, fast setup.
- Scenario B: A global financial services firm with multiple business lines, microservices producing events, many consumers (fraud detection, analytics, operational workflows), hybrid on-prem + cloud, need schema registry and governance.→ Confluent Cloud is better suited.
- Scenario C: A startup using Azure, but wants future flexibility (maybe multi-cloud) and some advanced features (connectors). Might start with Confluent small cluster now to “future-proof”.
Other Considerations
- Protocol compatibility & migration: Event Hubs supports Kafka protocol endpoints , easier to migrate Kafka workloads.
- Cost & operational overhead: Fully managed services reduce ops cost, but total cost depends on throughput, retention, connectors, multi-region.
- Governance & Data Platform Maturity: If you are building a “data streaming platform” (many teams, many use cases) rather than a point ingestion pipeline, choose beyond basic ingestion. One article says:
“As soon as you need to onboard more use cases . you will struggle with topics such as governance .” for Event Hubs.
- Vendor & cloud lock-in: Event Hubs is Azure-native; Confluent offers multi-cloud. If avoiding lock-in is a priority, that matters.
- Feature set and ecosystem: Confluent has more advanced streaming features (ksqlDB, connectors, tiered storage etc).
- Team skills: If your team is new to streaming / Kafka, Event Hubs might lower the barrier; if you already have Kafka expertise, Confluent gives full power.
- Latency, scale, retention: For ultra-large scale, enterprise SLAs, advanced retention patterns, Confluent may have edge.
Summary
In short:
- If you’re in Azure, need ingestion and integration quickly, your use cases are fairly standard → Event Hubs wins.
- If you are building a strategic streaming platform, need advanced Kafka ecosystem, multi-cloud or hybrid, many teams and use cases → Confluent Cloud is the better investment.