Azure Stream Analytics vs Redpanda comparison

Microsoft and Redpanda Data are both solutions in the Streaming Analytics category. Microsoft is ranked #4 with an average rating of 7.1, while Redpanda Data is ranked #18 with an average rating of 8.8. Microsoft holds a 7.6% mindshare in SA, compared to Redpanda Data’s 1.4% mindshare. Additionally, 90% of Microsoft users are willing to recommend the solution, compared to 100% of Redpanda Data users who would recommend it.

Azure Stream Analytics

Read 30 Azure Stream Analytics reviews

3,223 Views
2,933 Comparison Views

90% willing to recommend

Redpanda

Read 4 Redpanda reviews

397 Views
389 Comparison Views

100% willing to recommend

Azure Stream Analytics

Redpanda

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Azure Stream Analytics and Redpanda offer advanced data processing capabilities, competing within the real-time data analytics and event streaming category. Azure Stream Analytics is favored for its strong integration with Azure services, making it ideal for users within the Azure ecosystem. On the other hand, Redpanda gains an edge in performance, especially noted for its high-speed data processing and efficiency.

Features: Azure Stream Analytics deeply integrates with Azure services, providing real-time analytics, IoT hub support, and seamless scaling options. Its ease of provisioning and user-friendly interface makes it appealing for users already embedded in the Azure environment. Redpanda excels in supporting Kafka clients, ensuring high performance due to its C++ foundation, and offers a cost-effective alternative to traditional Kafka systems. Its simple setup and comprehensive documentation further enhance its attractiveness.

Room for Improvement: Azure Stream Analytics requires more transparent pricing and improved data handling capabilities. Its integration outside of Azure could be enhanced, alongside a smoother setup process for users not operating within the Azure ecosystem. Redpanda users would benefit from improved self-hosting documentation and enhanced command-line tools to optimize operations, catering better to diverse user needs.

Ease of Deployment and Customer Service: Azure Stream Analytics benefits from robust support within Azure’s environment and is widely adopted across public clouds, though users sometimes seek more direct technical assistance. Redpanda is noted for its easy on-premises deployment and streamlined community support, effectively addressing most customer requirements with detailed guidance provided by its documentation.

Pricing and ROI: Azure Stream Analytics adopts a "pay as you go" model, offering competitive pricing yet can incur high costs as scaling demands increase. Redpanda provides a more budget-friendly approach, with free versions available that attract cost-conscious enterprises. Both solutions report positive returns on investment through efficient solution delivery and high levels of customer satisfaction.

To learn more, read our detailed Azure Stream Analytics vs. Redpanda Report (Updated: September 2025).

Buyer's Guide

Azure Stream Analytics vs. Redpanda

September 2025

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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

4th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Redpanda

Ranking in Streaming Analytics

18th

Average Rating

8.8

Reviews Sentiment

7.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2025, in the Streaming Analytics category, the mindshare of Azure Stream Analytics is 7.6%, down from 12.2% compared to the previous year. The mindshare of Redpanda is 1.4%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Azure Stream Analytics	7.6%
Redpanda	1.4%
Other	91.0%

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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Vishal M Godi

Big Data Teaching Assistant at Center for Cloud Computing and Big Data, PES University

High-performance message brokering with excellent documentation and an easy setup

The industry standard for this kind of platform is Kafka. Confluent Kafka has acquired it. Kafka is an open-source platform built by Apache. Confluent is the commercial version of it. The major improvement of Redpanda over Kafka is firstly, good documentation. Redpanda's documentation is very easily understandable, and they have a lot of examples. In addition to that, most of the setups include using another technology called Docker, which I am very familiar with. Setting up technologies using Docker is very convenient to me, and Redpanda has provided many templates for that. Redpanda has its own built-in metrics exporter, making it easier to monitor and check performance. What makes Redpanda superior is its performance since it's written in C++. C++ is pretty much the standard for high-performance applications.

Read full review

Quotes from Members

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

Pros

"The solution's technical support is good."

"The solution has a lot of functionality that can be pushed out to companies."

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

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

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

"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."

"The way it organizes data into tables and dashboards is very helpful."

"The life cycle, report management and crash management features are great."

More Azure Stream Analytics pros

"What makes Redpanda superior is its performance since it's written in C++. C++ is pretty much the standard for high-performance applications."

"The cost savings have been significant."

"The UI is modern."

"I tested it with ten-plus nodes, and it's highly scalable."

Cons

"The UI should be a little bit better from a usability perspective."

"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."

"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."

"There is a lack of technical support from Microsoft's local office, particularly in Taiwan."

"There are too many products in the Azure landscape, which sometimes leads to overlap between them."

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

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

"There were challenges with Azure Stream Analytics. When I initially started, the learning curve was difficult because I didn't have knowledge of the service."

More Azure Stream Analytics cons

"Recently, for the documentation, they've built their own AI chatbot, which is focused on giving you answers based on their documentation. While using that, I did not find it to be very good."

"The command-line tools need to be improved. To quickly check the status of the topics and all."

"The version control mechanism must be improved."

"When it comes to self-hosting, their documentation could be improved."

Pricing and Cost Advice

"Azure Stream Analytics is a little bit expensive."

"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."

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

"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."

"The current price is substantial."

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

More Azure Stream Analytics pricing and cost advice

"It's free. Everybody can use it, only support is paid."

"Redpanda is cheaper than its competitors."

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

13%

Manufacturing Company

Insurance Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

No data available

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...

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

The solution does not need any license; it comes with your subscription.

See all answers

What needs improvement with Azure Stream Analytics?

With the deployment of Azure Stream Analytics, there are many challenges. I am working with a DevOps team that I'm part of as a counselor. I'm working with them because they are working in other pa...

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What is your experience regarding pricing and costs for Redpanda?

Redpanda is actually a commercial platform, but they do provide free versions as well. I've been working only with the free versions.

See all answers

What needs improvement with Redpanda?

Recently, for the documentation, they've built their own AI chatbot, which is focused on giving you answers based on their documentation. While using that, I did not find it to be very good. Maybe ...

See all answers

What is your primary use case for Redpanda?

I have worked with Redpanda for the past two to three months. Mainly in the tech industry or software industry, there's a huge rise of streaming data. Redpanda serves as a very reliable and fast me...

See all answers

Comparisons

Amazon MSK vs Azure Stream Analytics

Compared 23% of the time

Databricks vs Azure Stream Analytics

Compared 20% of the time

Apache Flink vs Azure Stream Analytics

Compared 18% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 16% of the time

Aiven Platform vs Azure Stream Analytics

Compared 5% of the time

More Azure Stream Analytics Competitors

Amazon Kinesis vs Redpanda

Compared 56% of the time

Apache Pulsar vs Redpanda

Compared 29% of the time

StreamNative vs Redpanda

Compared 15% of the time

More Redpanda Competitors

Product Reports

Buyer's Guide

Azure Stream Analytics

October 2025

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Buyer's Guide

Redpanda

September 2025

Download Redpanda product report

Also Known As

ASA

No data available

Overview

Azure Stream Analytics is a robust real-time analytics service that has been designed for critical business workloads. Users are able to build an end-to-end serverless streaming pipeline in minutes. Utilizing SQL, users are able to go from zero to production with a few clicks, all easily extensible with unique code and automatic machine learning abilities for the most advanced scenarios.

Azure Stream Analytics has the ability to analyze and accurately process exorbitant volumes of high-speed streaming data from numerous sources at the same time. Patterns and scenarios are quickly identified and information is gathered from various input sources, such as social media feeds, applications, clickstreams, sensors, and devices. These patterns can then be implemented to trigger actions and launch workflows, such as feeding data to a reporting tool, storing data for later use, or creating alerts. Azure Stream Analytics is also offered on Azure IoT Edge runtime, so the data can be processed on IoT devices.

Top Benefits

User friendly: Azure Stream Analytics is very straightforward and easy to use. Out of the box and with a few clicks, users are able to connect to numerous sources and sinks, and easily develop an end-to-end pipeline. Stream Analytics can easily connect to Azure IoT Hub and Azure Event Hub for streaming ingestion, in addition to connecting with Azure Blob storage for historical data ingestion.
Flexible deployment: For low-latency analytics, Azure Stream Analytics can run on Azure Stack or IoT edge. For large-scale analytics, the solution can run in the cloud. Azure Stream Analytics uses the same query language and tools for both the cloud and the edge, facilitating an easier process for developers to design exceptional hybrid architectures for streaming processes.
Cost-effective: With Azure Stream Analytics, users only pay for the streaming units they consume; there are no upfront costs. Users can easily scale up or down as needed; there is no commitment or cluster provisioning.
Trustworthy: Azure Stream Analytics guarantees event processing to be 99.99% available with a minute level of granularity. Azure Stream Analytics has embedded recovery capabilities and checkpoints to keep things running smoothly at all times. Events are never lost with Azure Stream Analytics at-least once delivery of events and exactly one event processing.

Reviews from Real Users

“Azure Stream Analytics is something that you can use to test out streaming scenarios very quickly in the general sense and it is useful for IoT scenarios. If I was to do a project with IoT and I needed a streaming solution, Azure Stream Analytics would be a top choice. The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex.” - Olubisi A., Team Lead at a tech services company.

“It's used primarily for data and mining - everything from the telemetry data side of things. It's great for streaming and makes everything easy to handle. The streaming from the IoT hub and the messaging are aspects I like a lot.” - Sudhendra U., Technical Architect at Infosys

Microsoft

Redpanda offers a modern, intuitive interface with efficient resource usage, seamlessly integrating with Kafka, and enhancing performance through fast operations and reliable support. Organizations benefit from its memory efficiency and high performance for demanding data workloads.

Built on a C++ foundation, Redpanda integrates easily with Kafka clients and stands out for fast operations, simplified Docker setup, and effective metrics monitoring. Performance is enhanced by memory efficiency and high throughput capabilities. The community provides robust support, and clear documentation aids the adoption process. However, improvements could be made in version control, command-line tools, and documentation, particularly in areas such as automation file management and chatbot documentation assistance. Redpanda is widely utilized in data streaming and normalization, efficiently handling large telemetry data volumes with minimal latency, essential for building asynchronous applications across microservices and monitoring systems.

What are the most important features of Redpanda?

Modern UI: Provides an intuitive interface for ease of use.
Efficient Resource Usage: Built on C++ for performance optimization.
Seamless Integration: Works well with Kafka clients for smooth operations.
Simplified Setup: Docker templates facilitate easy installation.
Built-in Metrics Exporters: Enhance monitoring and visibility.
High Performance: Ensures memory efficiency and quick data processing.

What benefits and ROI can users expect?

Fast Operations: Accelerates data processing for efficient workflows.
Reliable Support: Strong community aids troubleshooting and development.
Clear Documentation: Helps users quickly onboard and manage Redpanda.
Low Latency: Critical for applications requiring quick response times.
Sub-second Response: Supports demanding performance and latency needs.

Redpanda is commonly implemented in tech and software industries to streamline data streaming and normalization processes, handling high telemetry data volumes effectively. Its capacity for sub-second response times makes it crucial for companies developing asynchronous applications, especially in microservices and monitoring systems.

Redpanda Data

Sample Customers

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

Information Not Available

Buyer's Guide

Azure Stream Analytics vs. Redpanda

September 2025

Free Report: Azure Stream Analytics vs. Redpanda

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