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Top 3 Stream Analytics Software SaaS Companies in May 2026

As of May 2026, there are 3 SaaS companies in Stream Analytics Software. They have combined revenues of $21.1M and employ 57 people. They have raised - and serve - customers combined.

Stream analytics software enables organizations to process and analyze large volumes of streaming data in real-time. This technology allows businesses to gain insights from data as it flows in, providing the ability to respond to critical information without delays that come with traditional batch processing. Common use cases include monitoring IoT devices, detecting fraud, and analyzing sales data as it is generated.

Companies
3
Revenue
$21.1M
Funding
-
Employees
57

Filters

Sorting: Highest -> Lowest

Filters
Revenue
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Funding (Locked)Growth (Locked)Team-Size (Locked)

Top Stream Analytics Software Companies

Showing 10 of 3 companies ranked by annual revenue.

1
Wallaroo Labs

New York, New York, United States

Provider of a cloud-based data processing software intended to simplify the deployment of real-time applications. The company's software eliminates infrastructure problems, facilitates rapid iteration and testing and is used to run real-time applications to process large volumes of data while being agnostic to infrastructure and scale, enabling data engineers, architects and providers of data infrastructure to improve outpatient monitoring, real-time bidding and risk profiling.

Revenue
$12.4M
Customers
-
Year founded
2014
Funding
-
Team size
47
Growth
197.85%
2
Arroyo

Berkeley, California, United States

Cloud-native stream processing

Revenue
$5M
Customers
-
Year founded
2022
Funding
-
Team size
2
Growth
-
3
Artie

San Francisco, California, United States

Software that streams data from databases to warehouses in real-time

Revenue
$3.7M
Customers
-
Year founded
2023
Funding
-
Team size
8
Growth
-

Inclusion Criteria

- Must enable real-time data ingestion and processing - Should support analytics on continuous data streams - Must allow for integration with various data sources (e.g., IoT devices, applications) - Should provide capabilities for alerting and live dashboards for immediate insights - Not just focused on historical data analysis; must also support real-time decision making - Should include tools for data visualization and reporting based on live streams