Temporal
platformMaker of Temporal, an open-source durable-execution workflow platform.
Temporal Technologies is a U.S. software company headquartered in Seattle, Washington, founded in 2019 by Maxim Fateev and Samar Abbas. It builds Temporal, an open-source durable-execution platform for running long-lived, fault-tolerant workflows in code. The company also operates Temporal Cloud, a managed version of the platform, and has raised more than $100 million in venture funding.
Our Take
The Temporal cost mistake we see most is estimating Cloud spend from workflow count instead of Action count. Self-hosting is free in license only; for teams without a platform group the managed plan is usually cheaper. — Rafal Fila, ShadowGen
What Sets Temporal Apart
Temporal's durable-execution model persists complete workflow state and history, so application workflows survive crashes and resume precisely, a guarantee that code-first schedulers focused on data pipelines do not provide in the same form.
Key Achievements
About Temporal
Temporal Technologies develops Temporal, a durable-execution platform that lets engineers write long-running, reliable workflows as ordinary code. The platform persists the full state and history of every workflow execution, so a process can survive crashes, restarts, and infrastructure failures and resume exactly where it left off. The founders, Maxim Fateev and Samar Abbas, previously created related workflow systems at Uber and Amazon, and Temporal continues that lineage as an independent open-source project.
The Temporal server is open-source under the MIT license and free to self-host; the company monetises through Temporal Cloud, a managed service priced on consumption, measured in units called Actions. Temporal Cloud's plan structure was revised into Essentials, Business, Enterprise, and Mission Critical tiers. The company has raised more than $100 million across venture rounds and competes with code-first orchestrators such as Apache Airflow and Prefect, though Temporal targets durable application workflows rather than scheduled data pipelines specifically.