AI startup Clarifai launches reasoning engine to double AI speed and cut costs by 40%

On Thursday, AI platform Clarifai announced the launch of a new reasoning engine designed to make running AI models significantly faster and cheaper. The company claims the system can double inference speed while reducing costs by up to 40%, offering a software-based boost at a time when global demand for compute power is straining infrastructure.

Clarifai’s CEO, Matthew Zeiler, explained that the engine achieves its efficiency through a suite of low-level and algorithmic optimizations.

“It’s a variety of different types of optimizations, all the way down to CUDA kernels to advanced speculative decoding techniques,” Zeiler said. “You can get more out of the same cards, basically.”

Verified Performance Gains

Independent benchmarking by third-party firm Artificial Analysis confirmed the company’s claims, recording industry-leading results for both throughput and latency. The improvements focus squarely on inference—the phase where trained AI models generate outputs for users. With the rise of agentic and reasoning models that require multiple steps per request, inference has become one of the most compute-intensive aspects of modern AI workloads.

From Vision to Compute Orchestration

Clarifai, which originally launched as a computer vision service, has steadily expanded into broader AI infrastructure. As the AI boom has increased competition for GPUs and data center capacity, the company has pivoted toward compute orchestration to help customers maximize the efficiency of their hardware. Clarifai first unveiled its compute platform at AWS re:Invent in December, and the new reasoning engine marks its first product explicitly designed for multi-step, agentic AI systems.

A Broader Infrastructure Crunch

The launch comes as the industry faces unprecedented pressure on compute resources. OpenAI has signaled plans for up to $1 trillion in future data center investments, highlighting how costly and energy-intensive AI infrastructure could become. Zeiler argues that alongside hardware expansion, software innovation must play a role in scaling AI sustainably.

“There’s software tricks that take a good model like this further, like the Clarifai reasoning engine,” he said. “But there’s also algorithm improvements that can help combat the need for gigawatt data centers. And I don’t think we’re at the end of the algorithm innovations.”

With its reasoning engine, Clarifai positions itself not just as a model provider, but as a critical player in the effort to make AI infrastructure more efficient—a challenge that is becoming central to the industry’s future.