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: August 2026).

Azure Stream Analytics vs. Confluent

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Helped 911,602 peers since 2012

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 September 2026, in the Streaming Analytics category, the mindshare of Azure Stream Analytics is 6.2%, down from 8.0% compared to the previous year. The mindshare of Confluent is 6.3%, down from 8.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Azure Stream Analytics	6.2%
Confluent	6.3%
Other	87.5%

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

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

"For IoT use cases, if you want to do real-time dashboarding with Power BI, it's great."

"If you're not doing terribly complex scenarios, this is a quick and fast way to have your streaming pipeline set up."

"The biggest improvement for us has been that it now takes much less time for us to receive valuable information."

"It has features and functionality to integrate with all the tools that are available in the market, not only Azure solutions."

"Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time."

"Azure Stream Analytics reads from any real-time stream; it's designed for processing millions of records every millisecond."

"It provides the capability to streamline multiple output components."

More Azure Stream Analytics pros

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

"Implementing Confluent's schema registry has significantly enhanced our organization's data quality assurance."

"Confluence's greatest asset is its user-friendly interface, coupled with its remarkable ability to seamlessly integrate with a vast range of other solutions."

"The solution can handle a high volume of data because it works and scales well."

"The most valuable feature of Confluent is the wide range of features provided. They're leading the market in this category."

"Kafka Connect framework is valuable for connecting to the various source systems where code doesn't need to be written."

"The monitoring module is impressive."

"The documentation process is fast with the tool."

More Confluent pros

Cons

"It is not complex, but it requires some development skills. When the data is sent from Azure Stream Analytics to Power BI, I don't have the access to modify the data. I can't customize or edit the data or do some queries. All queries need to be done in the Azure Stream Analytics."

"There may be some issues when connecting with Microsoft Power BI because we are providing the input and output commands, and there's a chance of it being delayed while connecting."

"Early in the process, we had some issues with stability."

"The current price is substantial."

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

"If something goes wrong, it's very hard to investigate what caused it and why."

"Its features for event imports and architecture could be enhanced."

"The initial setup is complex."

More Azure Stream Analytics cons

"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."

"It could have more themes. The themes in the version I'm using are very limited; they offer two to three themes."

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

"From the control center perspective, there is a lot of room for improvement in the visualization."

"They should remove Zookeeper because of security issues."

"The solution could have an extra plugin or upgrading feature. In addition, it could have more integration with different platforms and be more compatible."

"Areas for improvement include implementing multi-storage support to differentiate between database stores based on data age and optimizing storage costs."

"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."

More Confluent cons

Pricing and Cost Advice

"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."

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

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

"The current price is substantial."

"Azure Stream Analytics is a little bit expensive."

"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."

"The product's price is at par with the other solutions provided by the other cloud service providers in the market."

More Azure Stream Analytics pricing and cost advice

"You have to pay additional for one or two features."

"The solution is cheaper than other products."

"The pricing model of Confluent could improve because if you have a classic use case where you're going to use all the features there is no plan to reduce the features. You should be able to pick and choose basic services at a reduced price. The pricing was high for our needs. We should not have to pay for features we do not use."

"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 highly priced."

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

"Confluent has a yearly license, which is a bit high because it's on a per-user basis."

"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."

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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911,602 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

11%

Manufacturing Company

10%

University

Computer Software Company

Financial Services Firm

15%

Retailer

12%

Computer Software Company

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	3
Large Enterprise	17

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

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Compared 10% of the time

Databricks vs Confluent

Compared 7% of the time

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Compared 6% of the time

PubSub+ Platform vs Confluent

Compared 5% of the time

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Compared 5% of the time

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Product Reports

Azure Stream Analytics

Download Azure Stream Analytics product report

Download Confluent product report

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

Azure Stream Analytics vs. Confluent