1 day Ago
TheStage AI Elastic models In the rapidly evolving artificial intelligence landscape, one of the most persistent challenges has been the resource-intensive process of optimizing neural networks for deployment. While AI tools have automated countless tasks for developers, designers, and business professionals, AI engineers themselves have been left to manually fine-tune models — a process that can take months and consume significant GPU resources. Delaware-based TheStage AI is changing this paradigm with their innovative approach to neural network optimization.
The startup recently announced a $4.5 million funding round to commercialize their Automatic NNs Analyzer (ANNA), a technology that promises to reduce optimization time from months to hours while making neural networks up to five times more cost-effective. The investment round attracted a roster of prominent backers including Mehreen Malik , Dominic Williams ( DFINITY ), Atlantic Labs (SoundCloud), Nick Davidov (DVC) and AAL VC .
The startup has already attracted customers like Recraft.ai and Praktika.a i, collaborated with Nebius , and brought on the Liberman brothers as advisors.
"AI models excel at implementing ideas and logic that are difficult to express through traditional deterministic algorithms. When we deploy these AI models, we're bringing these ideas to life," said Kirill Solodskih , CEO and Co-Founder of TheStage AI. "We've created a service that allows AI engineers and developers to compress, package, and deploy models to any device as easily as copy and paste.
" TheStage AI founding team, from left to right: Azim Kurbanov, Kirill Solodskih and Ruslan ...
More Aydarkhanov, Lead investor Mehreen Malik expressed confidence in the company's approach: "I look for people who have the horsepower to solve hard problems, the determination and ambition to build an enduring company for the long term, and the ability to explain a future and make it seem inevitable. Kirill had this in spades with an almost romanticised version of what a tool could do for machine learning architects." The startup has already attracted customers like Recraft.
ai and Praktika.ai i, partnered with Nebius , among others. The timing for TheStage AI's solution appears opportune.
According to McKinsey data , up to 70% of the cost of deploying AI systems stems from GPU infrastructure. This creates a significant barrier for AI startups and established enterprises alike, particularly as demand for inference processing grows. Despite speculation that new models like DeepSeek and the buzz around it, industry leaders are doubling down on infrastructure investments.
Mark Zuckerberg has reinforced Meta's plans to invest up to $65 billion in infrastructure this year, noting that computational demand is shifting from training to inference — making AI applications smarter and more efficient at runtime. This shift comes amid rapid enterprise adoption of generative AI technologies. Deloitte's 2024 report shows that 74% of enterprises have already met or exceeded their generative AI initiatives, reflecting the technology's growing importance across sectors.
At the core of TheStage AI's offering is ANNA (Automatic NNs Analyzer), a system that leverages discrete mathematics and AI to automatically optimize PyTorch models through advanced techniques including quantization, sparsification, and pruning. This approach creates models that meet specific requirements for size, latency, and quality without the months of manual tuning traditionally required. Through this technology, TheStage AI offers what they call "Elastic models" — a range of optimized versions of open-source models from smallest (XS) to largest (XL).
This gives customers the flexibility to choose the optimal balance between quality, speed, and cost, adjustable with what the company describes as "a simple slider movement". The concept is comparable to how streaming platforms like YouTube offer different video qualities (360p, 480p, 720p, 1080p), with ANNA delivering a range of optimized models with different performance characteristics to match specific deployment scenarios. In its Model Library, TheStage AI currently offers several dozens of optimized models, including popular open-source solutions like Stable Diffusion, each optimized for different performance, speed, and cost requirements.
They also provide automatic acceleration services for companies that customize or build their own models. Early collaborations have shown promising results. In a partnership with Recraft.
ai , TheStage AI reportedly doubled performance and reduced processing time by 20% compared to PyTorch's compiler — significant improvements that translate directly to cost savings and better user experiences. Unlike competitors that lock users into proprietary hardware, TheStage AI offers flexibility in supporting a wide range of hardware setups. Their technology works across smartphones, custom on-premises GPUs, and cloud environments, integrating seamlessly with major cloud providers including AWS, Google Cloud, and Microsoft Azure.
This hardware-agnostic approach addresses a key pain point for AI developers who often find themselves constrained by vendor-specific optimization solutions. TheStage AI is targeting two primary user groups: application developers seeking pre-optimized models for seamless integration into their products, and model builders requiring granular control for custom neural networks. This dual approach offers a significant competitive advantage over rivals that only provide ready-made solutions, effectively addressing the scaling challenges faced by startups that need to balance performance with cost as they grow.
"We're essentially democratizing access to AI optimization," Solodskih explained in an interview. "Previously, only the largest tech companies could afford the expertise and compute resources needed to properly optimize models for deployment. We're making that capability available to companies of all sizes.
" TheStage AI was founded by four university friends Kirill Solodskih , Azim Kurbanov , Ruslan Aydarkhanov and Max Petriev with PhDs in mathematics and neuroscience, bringing over a decade of expertise in optimizing deep neural networks. The team has a robust research background, filing over 10 patents and publishing more than 15 papers, including an award-nominated study presented at CVPR and contributions at ECCV . Their expertise isn't merely theoretical.
Prior to founding TheStage AI, the team worked together at Huawei, where they developed cutting-edge model compression and acceleration technologies that were integrated into the AI cameras of the P50 and P60 smartphones — devices known for their computational photography capabilities. This combination of academic research and practical implementation experience gives the team a unique perspective on the challenges of deploying AI in resource-constrained environments. For businesses deploying AI solutions, the potential impact of TheStage AI's technology is substantial.
The difference between an optimized and unoptimized model can mean the difference between a viable product and one that's too expensive or slow to deploy at scale. Consider a startup developing an AI-powered feature for a mobile application. Without optimization, the model might require cloud processing, introducing latency and ongoing operational costs.
With proper optimization, the same functionality could run directly on the device, improving user experience while reducing backend expenses. Similarly, for cloud-based AI services, optimization directly affects the cost structure. A model that runs five times more efficiently translates to an 80% reduction in computing costs — a difference that can determine whether a business model is sustainable.
TheStage AI enters a market with established players in the AI optimization space, including companies like OctoML (acquired by NVIDIA in late 2024), Neural Magic (acquired by Red Hat in January this year), and offerings from larger cloud providers. However, most existing solutions require significant expertise to implement effectively or are tied to specific hardware platforms. The company's automated approach and hardware-agnostic strategy position it uniquely in this landscape, offering a more accessible solution for the growing number of businesses seeking to deploy AI capabilities.
With $4.5 million in funding secured, TheStage AI is focused on expanding its Model Library and enhancing its automatic optimization capabilities. The company plans to use the funding to grow its engineering team and establish partnerships with additional cloud providers and hardware manufacturers.
The long-term vision, according to background materials, is to create an ecosystem where AI deployment is as seamless as software deployment has become — where considerations of optimization are handled automatically, allowing developers to focus on the core functionality rather than the infrastructure requirements. As AI continues its rapid integration across industries, tools that reduce the friction of deployment will play an increasingly important role. TheStage AI's approach to automating one of the most technically challenging aspects of the AI development process positions it to potentially capture significant value in this evolving market.
If successful, the company could help accelerate the broader adoption of AI technologies by removing one of the key bottlenecks in the development and deployment process — potentially bringing advanced AI capabilities to applications and services where they were previously impractical due to performance or cost constraints..
