While generative AI has made it easier to build and deploy models, getting those models to run efficiently remains a massive hurdle. AI engineers still spend months manually tweaking neural networks for different devices and use cases. This process demands serious GPU power, drains time and money, and slows down innovation—especially for startups and enterprises with tight budgets. TheStage AI, a US-based startup, is aiming to make inference optimization automatic, fast, and scalable.
Backed by $4.5 million in fresh seed funding, TheStage AI wants to take the heavy lifting out of AI deployment. Instead of spending months fine-tuning a model to run efficiently, developers could soon automate the whole process using TheStage’s proprietary platform, cutting GPU costs without compromising performance.
Solving a $65B Problem with Automation
The funding round was led by notable investors like Mehreen Malik, Dominic Williams (DFINITY), Atlantic Labs (SoundCloud), Nick Davidov (DVC), and AAL VC. Their backing signals a strong belief in the startup’s mission to slash the time and money it takes to deploy AI models at scale.
The startup plans to use the investment to upgrade its core optimization engine, ANNA (Automatic Neural Network Analyzer), expand its growing library of pre-optimized models, and scale its infrastructure. There are also plans to integrate deeper with major cloud platforms—AWS, Google Cloud, and Microsoft Azure—as well as expand the team and ramp up customer acquisition, particularly among developers and model builders.
From Huawei to TheStage AI: Turning Research into Impact
The company was founded by four university friends—Kirill Solodskih, Azim Kurbanov, Ruslan Aydarkhanov, and Max Petriev—who each hold PhDs in mathematics or neuroscience. Before launching TheStage AI, they spent years working together at Huawei, where they specialized in model compression and acceleration for smartphones like the P50 and P60.
During their time at Huawei, the team built internal tools that sped up AI model optimization dramatically. One patented algorithm they developed proved crucial when Huawei had to pivot from Kirin to Qualcomm chips after facing US sanctions. That experience opened their eyes to the broader need for automation in AI deployment—and inspired them to create TheStage AI to address that pain point for the entire industry.
How It Works: A YouTube-Like Approach to AI Models
TheStage AI’s flagship product, ANNA, applies advanced techniques like quantization, pruning, and sparsification to optimize PyTorch models. The result? Elastic models that adapt to different performance and hardware requirements, much like picking video resolution on YouTube.
These models are stored in a curated Model Library and include open-source favorites like Stable Diffusion—tuned for different latency, size, and cost requirements. The platform also supports custom acceleration for companies building their own AI models and runs seamlessly across a variety of environments, including edge devices, on-premise GPUs, and cloud providers.
In a recent partnership with Recraft.ai, TheStage AI slashed processing time by 20% compared to PyTorch’s own compiler. Importantly, it doesn’t lock users into specific hardware setups. Unlike many competitors, TheStage AI lets users choose whatever infrastructure they prefer—making it more flexible and future-proof.
Carving Out a Niche in the Inference Market
TheStage AI is zeroing in on the growing inference optimization market. Competitors include inference platforms like Fireworks, Replicate, and AWS SageMaker, along with tool-based rivals like TensorRT Model Optimizer and Intel Neural Compressor. However, TheStage AI’s hybrid model—offering both pre-optimized models and deep customization—gives it a clear edge.
By focusing on automation and adaptability, the startup believes its tools can even enhance competing platforms rather than replace them. It’s a bold but strategic position in a market projected to grow rapidly as AI adoption accelerates.
Why It Matters Now
As companies rush to integrate AI, the cost and complexity of deploying these models can’t be ignored. With 70% of AI system costs tied to GPU usage and infrastructure, according to McKinsey, TheStage AI’s automated solution comes at the right time.
Meta’s recent $65 billion AI infrastructure spend is just one example of how urgently the industry needs better inference tools. By removing bottlenecks, TheStage AI could speed up deployment, reduce costs, and enable more widespread use of AI—especially in startups and mobile applications.
CEO Kirill Solodskih summed it up best: deploying AI is about turning logic into real-world value. With TheStage AI, developers can compress, package, and deploy models to any device as easily as copy and paste.
Lead investor Mehreen Malik agrees. “Smart AI infrastructure has to combine the right software with the right hardware. TheStage AI nails that balance, and I’m confident we’ll see major product growth in the coming months.”
