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

"It's a product that can scale."

"The most valuable features are the IoT hub and the Blob storage."

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

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

"Technical support is pretty helpful."

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

"The product is very user-friendly."

"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."

More Azure Stream Analytics pros

"Overall, it's a great company and they have excellent software."

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

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

"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."

"Confluent facilitates the messaging tasks with Kafka, streamlining our processes effectively."

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

"The most valuable feature that we are using is the data replication between the data centers allowing us to configure a disaster recovery or software. However, is it's not mandatory to use and because most of the features that we use are from Apache Kafka, such as end-to-end encryption. Internally, we can develop our own kind of product or service from Apache Kafka."

"The design of the product is extremely well built and it is highly configurable."

More Confluent pros

Cons

"We would like to have centralized platform altogether since we have different kind of options for data ingestion. Sometimes it gets difficult to manage different platforms."

"More flexibility in terms of writing queries and accommodating additional facilities would be beneficial."

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

"Azure Stream Analytics could improve by having clearer metrics as to the scale, more metrics around the data set size that is flowing through it, and performance tuning recommendations."

"The initial setup is complex."

"The initial setup is complex. It should be easier for new users who may not have too much Azure experience."

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

"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."

More Azure Stream Analytics cons

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

"It would be great if the knowledge based documents in the support portal could be available for public use as well."

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

"They should remove Zookeeper because of security issues."

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

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

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

"I am not very impressed by Confluent. We continuously face issues, such as Kafka being down and slow responses from the support team."

More Confluent cons

Pricing and Cost Advice

"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 current price is substantial."

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

"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."

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

"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."

"Azure Stream Analytics is a little bit expensive."

More Azure Stream Analytics pricing and cost advice

"Confluent is an expensive solution."

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

"Confluent is highly priced."

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

"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."

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

More Confluent pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

11%

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

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Compared 8% of the time

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Compared 11% of the time

Databricks vs Confluent

Compared 7% 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

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Product Reports

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Azure Stream Analytics

July 2026

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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