OctoML Raises $85M for Machine Learning Acceleration Platform

OctoML Secures $85 Million in Series C Funding

OctoML, a Seattle-based company specializing in machine learning model optimization and deployment for enterprises, has announced the successful completion of an $85 million Series C funding round. This investment was spearheaded by Tiger Global Management.

Existing investors, including Addition, Madrona Venture Group, and Amplify Partners, also contributed to this funding round. This brings the total capital raised by the company to $132 million, building upon a $28 million Series B round announced earlier in the year.

Founding and Core Technology

The company’s origins lie in the work of its co-founders: CEO Luis Ceze, CTO Tianqi Chen, CPO Jason Knight, Chief Architect Jared Roesch, and VP of Technology Partnerships Thierry Moreau. These individuals were also the creators of the Apache TVM open-source machine learning compiler framework.

Currently, Apache TVM is utilized by major technology companies such as Amazon, Microsoft, and Facebook. OctoML leverages TVM’s capabilities to automatically optimize machine learning models, enabling their execution on a wide range of hardware platforms.

Strategic Partnerships and Performance Gains

Since securing its Series A funding, OctoML has established partnerships with key hardware manufacturers. These include Qualcomm, AMD, and Arm.

Recently, the company collaborated with Microsoft on a project focused on large-scale video content moderation. OctoML reports that its users, encompassing numerous Global 100 companies like Toyota, are experiencing performance improvements ranging from 2x to 10x in their machine learning models after implementing the service.

SaaS Platform Development

The company began onboarding early adopters to its Software-as-a-Service (SaaS) platform around the time of its Series B round. While not yet generally available, OctoML is now focused on expanding its customer base and ensuring their success on the platform.

As machine learning models become more complex, cloud deployment costs are increasing. A system capable of optimizing these models directly translates to cost savings for customers.

Sustainability and Hardware Flexibility

Ceze emphasized that optimization isn’t solely about cost. It also addresses sustainability concerns. Improving model speed by a factor of two on the same hardware reduces energy consumption by half, which has a significant impact at scale.

Furthermore, the limited availability of high-end GPUs due to ongoing chip shortages makes the ability to deploy models on alternative hardware, such as CPUs, a valuable advantage.

Strategic Funding Decision

Despite having sufficient runway, OctoML’s leadership team opted to pursue additional funding to capitalize on emerging opportunities. The company identified potential for accelerated growth in hardware enablement, SaaS business expansion, and cloud integration.

“We have a business opportunity right in front of us, but we need to scale fast to take full advantage of it,” stated Ceze.

Future Plans

The newly acquired funding will be allocated to rapidly expanding the company’s engineering and sales teams. This expansion will support the onboarding of new customers and the strengthening of its partner ecosystem.

Investor Perspective

John Curtius, a partner at Tiger Global, commented on OctoML’s impact: “OctoML is creating a profound shift in the way organizations build next-generation AI models and applications.”

He further added, “OctoML’s vision of delivering a unified deployment lifecycle for users across all the ML hardware vendors they rely on is making ML development more cost-effective and accessible to a broader set of developers. We’re excited to welcome Luis and the co-founding team at OctoML to the Tiger portfolio, and look forward to playing a role in their next growth chapter.”