Striveworks, a startup specializing in MLOps (DevOps for machine learning models), has secured $33 million in its first-ever funding round. The investment, led by Centana Growth Partners, highlights the growing interest in artificial intelligence and Striveworks’ own success in the field, with its annual recurring revenue (ARR) growing by 300% over the past two years.
Based in Austin, Texas, Striveworks plans to utilize the funds for hiring, product development, and business expansion. The company, which has been in operation for five years, has been focused on efficient capital management, reinvesting profits to fuel growth.
Although specific customer names were not disclosed, Striveworks serves clients across various industries, including government, finance, and highly regulated sectors. The company addresses challenges related to building appropriate machine learning models and ensuring their performance meets expectations, particularly in production environments. Striveworks emphasizes the need for automated and thoughtful strategies to handle errors and maintain responsible AI practices.
The company’s flagship platform, Chariot, facilitates data preparation, model building, and model deployment. Chariot offers features such as low-code collaboration, model-in-the-loop annotation, integration with third-party tools, custom workflows, and provenance tracking of data sets.
Striveworks operates in a market with several other MLOps-focused startups, including Seldon, Galileo, Aries, and Tecton. Notably, the demand for MLOps solutions has attracted the attention of larger players, such as McKinsey, which recently acquired Iguazio.
Centana partner Ben Cukier commended Striveworks for its efficient use of capital and its ability to achieve significant growth without external funding. The company has secured substantial contracts with high net retention numbers.
While the exact valuation was not disclosed, Cukier stated that the current market environment is returning to normalcy after a period of heightened activity in recent years.