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Top 2 Real-time Analytic Database Software Companies With $1M–$5M Revenue (September 2026)

As of September 2026, Latka tracks 2 real-time analytic database software companies with $1M–$5M in annual revenue. They have combined revenues of $4.3M and employ 34 people. They have raised $3M.

Every company below sells real-time analytic database software to other businesses and is ranked by its most recent annual revenue. Revenue, funding, headcount and customer figures come from CEO interviews on the Latka podcast, public company filings, and Latka estimates where a company has not disclosed a number.

What Real-time Analytic Database Software Companies do

Real-time analytic database software enables organizations to analyze and process data as it is generated, providing instant insights and facilitating immediate decision-making. This software is particularly useful in scenarios that require the continuous ingestion of streaming data, often seen in applications such as customer behavior tracking, smart device responses, and operational performance monitoring. Typical features of real-time analytic databases include low-latency query capabilities, high throughput for concurrent queries, and support for complex event processing. Common user personas include data analysts, business intelligence professionals, and IT operations teams, who leverage these tools to drive analytics processes within industries such as finance, e-commerce, and telecommunications, where timely access to data is critical for strategic advantage.

Companies
2
Revenue
$4.3M
Funding
$3M
Employees
34

Filters

Sorting: Highest -> Lowest

Filters

Top Real-time Analytic Database Software Companies With $1M–$5M Revenue

Showing 2 of 2 companies ranked by annual revenue.

1LeanXcale logo
LeanXcale

Madrid, Spain

Developer of an ultra-scalable operational database designed to provide full SQL and ACID transactions to cloud applications with standard interfaces. The company's ultra-scalable operational database offers real-time analytics blending the capabilities of an operational database and the ones of a data warehouse in a single platform, empowering its customers to implement professional results without the need to copy their data in time and resource-consuming projects.

Revenue
$2.6M
Year founded
2015
Funding
$3M
Team size
18
2Tacnode logo
Tacnode

United States

Tacnode innovates at the cutting edge of Database, Data Lakehouse and AI space. Tacnode is a unified cloud native and cloud agnostic data platform built for massive scale. The first database that unifies real-time analytics, online retrieval, and PostgreSQL compatibility in one platform — handling large-scale data and complex workloads with unprecedented performance, at real-time.

Revenue
$1.8M
Team size
16

Frequently asked questions about Real-time Analytic Database Software Companies With $1M–$5M Revenue

How many real-time analytic database software companies with $1M–$5M in annual revenue are there?

Latka tracks 2 real-time analytic database software companies with $1M–$5M in annual revenue. Together they generate $4.3M in annual revenue and employ 34 people.

Which real-time analytic database software company with $1M–$5M in annual revenue is the largest?

LeanXcale is the largest, with $2.6M in annual revenue, founded in 2015.

How much revenue does a typical real-time analytic database software company with $1M–$5M in annual revenue make?

The average real-time analytic database software company in this list makes $2.2M a year, across 2 companies with reported revenue.

Who are the leading Real-time Analytic Database software vendors with $1M–$5M in annual revenue?

Ranked by annual revenue, the leaders are LeanXcale and Tacnode.

How much funding have real-time analytic database software companies with $1M–$5M in annual revenue raised?

The 2 real-time analytic database software companies with $1M–$5M in annual revenue tracked here have raised $3M in disclosed funding between them.

Related IT Infrastructure Software categories

Inclusion Criteria

- The software must support real-time data ingestion and processing. - It should provide low-latency query response for immediate insights. - It must be capable of handling high volumes of concurrent queries. - The product should allow for complex event processing and analytics. - It should offer integration capabilities with various data sources. - Not just focused on historical data analysis; must also provide real-time analytical functionalities.