Azure Stream Analytics vs Confluent comparison

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Azure Stream Analytics

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Executive SummaryUpdated on Dec 17, 2024

Confluent and Azure Stream Analytics compete in the streaming analytics space. Azure Stream Analytics holds a technical advantage due to its in-depth features, despite Confluent's favorable pricing and support.

Features: Confluent offers robust integrations, high throughput, and flexibility to adapt to various data environments. Azure Stream Analytics provides complex event processing, native integration with Azure services, and seamless data flow and analysis.

Room for Improvement: Confluent's customer support could improve and its numerous configurations might overwhelm new users. Scalability for very large deployments can be complex, and documentation could be clearer. Azure Stream Analytics has high initial complexity, limited multi-cloud capabilities, and costs can escalate with feature expansions.

Ease of Deployment and Customer Service: Confluent is known for straightforward deployment and flexible configurations suitable for diverse IT ecosystems, but its customer support needs enhancement. Azure Stream Analytics offers simplified deployment within Azure, with strategic guidance for efficient implementation and reliable customer service for medium to large enterprises.

Pricing and ROI: Confluent presents a competitive pricing structure with good ROI, attracting businesses using open-source and streaming infrastructure. Azure Stream Analytics has higher initial setup costs with scalable pricing options, offering a balanced ROI through high-value data insights and efficiencies in Azure services.

To learn more, read our detailed Azure Stream Analytics vs. Confluent Report (Updated: June 2026).

Azure Stream Analytics vs. Confluent

June 2026

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Azure Stream Analytics

Ranking in Streaming Analytics

2nd

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Confluent

Ranking in Streaming Analytics

7th

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2026, in the Streaming Analytics category, the mindshare of Azure Stream Analytics is 7.0%, down from 9.1% compared to the previous year. The mindshare of Confluent is 6.6%, down from 8.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Azure Stream Analytics	7.0%
Confluent	6.6%
Other	86.4%

Streaming Analytics

Featured Reviews

Chandra Mani

Technical architect at Tech Mahindra

Has supported real-time data validation and processing across multiple use cases but can improve consumer-side integration and streamlined customization

I widely use AKS, Azure Kubernetes Service, Azure App Service, and there are APM Gateway kinds of things. I also utilize API Management and Front Door to expose any multi-region application I have, including Web Application Firewalls, and many more—around 20 to 60 services. I use Key Vault for managing secrets and monitoring Azure App Insights for tracing and monitoring. Additionally, I employ AI search for indexer purposes, processing chatbot data or any GenAI integration. I widely use OpenAI for GenAI, integrating various models with our platform. I extensively use hybrid cloud solutions to connect on-premise cloud or cloud to another network, employing public private endpoints or private link service endpoints. Azure DevOps is also on my list, and I leverage many security concepts for end-to-end design. I consider how end users access applications to data storage and secure the entire platform for authenticated users across various use cases, including B2C, B2B, or employee scenarios. I also widely design multi-tenant applications, utilizing Azure AD or Azure AD B2C for consumers. Azure Stream Analytics reads from any real-time stream; it's designed for processing millions of records every millisecond. They utilize Event Hubs for this purpose, as it allows for event processing. After receiving data from various sources, we validate and store it in a data store. Azure Stream Analytics can consume data from Event Hubs, applying basic validation rules to determine the validity of each record before processing.

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PavanManepalli

AVP - Sr Middleware Messaging Integration Engineer at Wells Fargo

Has supported streaming use cases across data centers and simplifies fraud analytics with SQL-based processing

I recommend that Confluent should improve its solution to keep up with competitors in the market, such as Solace and other upcoming tools such as NATS. Recently, there has been a lot of buzz about Confluent charging high fees while not offering features that match those of other tools. They need to improve in that direction by not only reducing costs but also providing better solutions for the problems customers face to avoid frustrations, whether through future enhancement requests or ensuring product stability. The cost should be worked on, and they should provide better solutions for customers. Solutions should focus on hierarchical topics; if a customer has different types of data and sources, they should be able to send them to the same place for analytics. Currently, Confluent requires everything to send to the same topic, which becomes very large and makes running analytics difficult. The hierarchy of topics should be improved. This part is available in MQ and other products such as Solace, but it is missing in Confluent, leading many in capital markets and trading to switch to Solace. In terms of stability, it is not the stability itself that needs improvement but rather the delivery semantics. Other products offer exactly-once delivery out of the box, whereas Confluent states it will offer this but lacks the knobs or levers for tuning configurations effectively. Confluent has hundreds of configurations that application teams must understand, which creates a gap. Users are often unaware of what values to set for better performance or to achieve exactly-once semantics, making it difficult to navigate through them. Delivery semantics also need to be worked on.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."

"The best features of Azure Stream Analytics are that it's easy to set up and configure."

"It was easy for me to use from the beginning. I am accustomed to working with Microsoft."

"This solution is easy to use and has good fault tolerance, so I would recommend it to other organizations."

"Azure Stream Analytics helps with processing fast-moving data in my IoT applications because it's quicker to escalate."

"Any time I needed assistance, they were helpful."

"It's scalable as a cloud product."

"From the end user's perspective, Stream Analytics forms the main backbone for the entire pipeline and all the technologies."

More Azure Stream Analytics pros

"Our main goal is to validate whether we can build a scalable and cost-efficient way to replicate data from these various sources."

"Confluent is an amazing tool that is highly configurable, integrates very well with Jira, and lets you create nice documentation for various products while also supporting reporting and online content hosting."

"A person with a good IT background and HTML will not have any trouble with Confluent."

"One of the best features of Confluent is that it's very easy to search and have a live status with Jira."

"To date, we have seen improvements in performance and scalability, so we recommend this solution."

"I find Confluent's Kafka Connectors and Kafka Streams invaluable for my use cases because they simplify real-time data processing and ETL tasks by providing reliable, pre-packaged connectors and tools."

"As an enterprise organization, data availability is critical and Confluent provides that SLA support."

"I would rate the scalability of the solution at eight out of ten. We have 20 people who use Confluent in our organization now, and we hope to increase usage in the future."

More Confluent pros

Cons

"There is a need for improvement in reprocessing or validation without custom code. Azure Stream Analytics currently allows some degree of code writing, which could be simplified with low-code or no-code platforms to enhance performance."

"The solution doesn't handle large data packets very efficiently, which could be improved upon."

"The product could be improved by providing more detailed analytics; for example, a graph to identify the past, present and current users, and UI and UX testing could be supported on this solution."

"The solution offers a free trial, however, it is too short."

"The collection and analysis of historical data could be better."

"The only challenge was that the streaming analytics area in Azure Stream Analytics could not meet our company's expectations, making it a component where improvements are required."

"One area that could use improvement is the handling of data validation. Currently, there is a review process, but sometimes the validation fails even before the job is executed. This results in wasted time as we have to rerun the job to identify the failure."

"The solution’s customer support could be improved."

More Azure Stream Analytics cons

"The Schema Registry service could be improved. I would like a bigger knowledge base of other use cases and more technical forums. It would be good to have more flexible monitoring features added to the next release as well."

"The pricing was high for our needs. We should not have to pay for features we do not use."

"Confluence could improve the server version of the solution. However, most companies are going to the cloud."

"The product should integrate tools for incorporating diagrams like Lucidchart. It also needs to improve its formatting features. We also faced issues while granting permissions."

"It requires some application specific connectors which are lacking. This needs to be added."

"The formatting aspect within the page can be improved and more powerful."

"Recently, there has been a lot of buzz about Confluent charging high fees while not offering features that match those of other tools."

"Currently, in the early stages, I see a gap on the security side. If you are using the SaaS version, we would like to get a fuller, more secure solution that can be adopted right out of the box. Confluence could do a better job sharing best practices or a reusable pattern that others have used, especially for companies that can not afford to hire professional services from Confluent."

More Confluent cons

Pricing and Cost Advice

"The cost of this solution is less than competitors such as Amazon or Google Cloud."

"There are different tiers based on retention policies. There are four tiers. The pricing varies based on steaming units and tiers. The standard pricing is $10/hour."

"We pay approximately $500,000 a year. It's approximately $10,000 a year per license."

"The current price is substantial."

"I rate the price of Azure Stream Analytics a four out of five."

"When scaling up, the pricing for Azure Stream Analytics can get relatively high. Considering its capabilities compared to other solutions, I would rate it a seven out of ten for cost. However, we've found ways to optimize costs using tools like Databricks for specific tasks."

"The licensing for this product is payable on a 'pay as you go' basis. This means that the cost is only based on data volume, and the frequency that the solution is used."

"Azure Stream Analytics is a little bit expensive."

More Azure Stream Analytics pricing and cost advice

"It comes with a high cost."

"Confluence's pricing is quite reasonable, with a cost of around $10 per user that decreases as the number of users increases. Additionally, it's worth noting that for teams of up to 10 users, the solution is completely free."

"Confluent is expensive, I would prefer, Apache Kafka over Confluent because of the high cost of maintenance."

"The solution is cheaper than other products."

"On a scale from one to ten, where one is low pricing and ten is high pricing, I would rate Confluent's pricing at five. I have not encountered any additional costs."

"Confluent is an expensive solution as we went for a three contract and it was very costly for us."

"Regarding pricing, I think Confluent is a premium product, but it's hard for me to say definitively if it's overly expensive. We're still trying to understand if the features and reduced maintenance complexity justify the cost, especially as we scale our platform use."

"Confluent is an expensive solution."

More Confluent pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

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903,257 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

12%

Computer Software Company

Manufacturing Company

University

Financial Services Firm

15%

Retailer

11%

Computer Software Company

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	3
Large Enterprise	18

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	4
Large Enterprise	17

Questions from the Community

Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

What is your experience regarding pricing and costs for Azure Stream Analytics?

Azure charges in various ways based on incoming and outgoing data processing activities. Choosing between pay-as-you-go or enterprise models can affect pricing, and depending on data volume, charge...

What needs improvement with Azure Stream Analytics?

There is a need for improvement in reprocessing or validation without custom code. Azure Stream Analytics currently allows some degree of code writing, which could be simplified with low-code or no...

What is your experience regarding pricing and costs for Confluent?

They charge a lot for scaling, which makes it expensive.

What needs improvement with Confluent?

What is your primary use case for Confluent?

The main use cases for Confluent are log aggregation and streaming. I'm familiar with Confluent stream processing with KSQL. KSQL helps in terms of data analytics strategies because if we are the d...

Databricks vs Azure Stream Analytics

Comparisons

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Compared 9% of the time

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Compared 8% of the time

Amazon MSK vs Azure Stream Analytics

Compared 8% of the time

PubSub+ Platform vs Azure Stream Analytics

Compared 8% of the time

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Palantir Foundry vs Confluent

Compared 11% of the time

Databricks vs Confluent

Compared 8% of the time

Amazon Kinesis vs Confluent

Compared 6% of the time

PubSub+ Platform vs Confluent

Compared 5% of the time

Apache Flink vs Confluent

Compared 5% of the time

More Confluent Competitors

Product Reports

Download Azure Stream Analytics product report

Azure Stream Analytics

July 2026

Download Confluent product report

July 2026

Also Known As

ASA

No data available

Overview

Azure Stream Analytics offers real-time data processing with seamless IoT hub integration and user-friendly setup. It efficiently manages data streams and supports Azure services, SQL Server, and Cosmos DB.

Azure Stream Analytics specializes in real-time data analytics, easily integrating with Microsoft technologies. It enables swift deployment, monitoring, and high-performance data streaming. Though praised for its powerful SQL language and machine learning capabilities, users face challenges with historical analysis, pricing clarity, debugging, and data connection outside Azure. Limited real-time data joining, query customization, and complex data handling are noted alongside needs for improved technical support, job monitoring, and trial periods.

What are the key features of Azure Stream Analytics?

IoT Hub Integration: Seamless connection with IoT devices for efficient data stream management.
Real-Time Analytics: Capable of processing and analyzing vast data volumes instantly.
SQL-Based Language: Powerful language for simple and flexible query creation.
Azure Service Integration: Compatible with Azure Storage, SQL Server, Cosmos DB.
Interface: User-friendly setup and swift deployment for efficient monitoring.
Machine Learning: Enhanced capabilities support predictive and preventive data analysis.

What benefits or ROI should users consider?

High-Performance Streaming: Efficient throughput for real-time data applications.
Data Partitioning: Supports scalability and flexibility in processing large data sets.
Minimal Setup Requirements: Quick and easy deployment, reducing initial setup time.
Integration Ease: Compatibility with Microsoft technologies simplifies implementation.

Azure Stream Analytics is leveraged in industries for real-time IoT data processing, predictive analytics, and accident prevention in logistics. It supports telemetry data processing for applications like predictive maintenance and integrates with Power BI for enhanced data visualization, aligning with Azure's IoT infrastructure.

Microsoft

Confluent offers scalable, open-source flexibility and seamless data replication, supported by strong cloud integration. Key features like Kafka Connect and real-time processing make it valuable for data streaming projects while ensuring high availability with a Multi-Region Cluster.

Confluent is a robust data streaming platform that enables efficient management and integration of real-time data pipelines. Its message-driven architecture and fault tolerance provide reliability, while a user-friendly dashboard and connectors support diverse data sources. Cloud integration reduces costs, and extensive documentation, plugins, and monitoring capabilities enhance collaboration and revision management. Despite some areas needing improvement, including security in the SaaS version and integration flexibility, Confluent remains a staple in industries requiring vast data processing and task automation.

What are Confluent's key features?

Scalability: Handles large volumes of data, ensuring continuous performance.
Kafka Connect: Enables seamless data integration across multiple systems.
Real-time Processing: Facilitates instant data insights for rapid decision-making.
Multi-Region Cluster: Maintains data availability across different geographical locations.
Cloud Integration: Reduces operational costs and enhances deployment flexibility.
User Dashboard: Simplifies management with an intuitive interface.

What benefits and ROI should users consider in reviews?

Cost-Efficiency: Improved processes reduce operational expenses.
Data Reliability: Ensures consistent and accurate data handling.
Increased Collaboration: Boosts team efficiency with comprehensive documentation and management tools.
Scalability: Supports business growth without compromising performance.
Integration Flexibility: Seamlessly connects with diverse data sources and systems.

Confluent is commonly implemented in finance, insurance, and software industries for applications like fraud detection, ETL tasks, and enterprise communication. It supports real-time data processing, project management, and task automation, often integrating with project management tools like Jira, providing valuable solutions for business processes.

Sample Customers

Rockwell Automation, Milliman, Honeywell Building Solutions, Arcoflex Automation Solutions, Real Madrid C.F., Aerocrine, Ziosk, Tacoma Public Schools, P97 Networks

ING, Priceline.com, Nordea, Target, RBC, Tivo, Capital One, Chartboost