The AI Infrastructure Research Lab

aion aggregates underutilized, enterprise-grade GPU infrastructure into a unified platform. This gives you on-demand access to high-performance machines without long procurement cycles or complex cloud setup. The complexity of managing hardware is abstracted away and you get transparent, plug-and-play compute with no vendor lock-in, so your team can focus on building AI applications instead of wrangling servers.

Most customers can begin accessing compute within hours, not weeks or months. aion pre-qualifies and provisions capacity ahead of demand, enabling rapid onboarding compared to traditional hyperscaler procurement cycles or custom on-prem deployments.

AI inference is the process of running a trained model to generate predictions or outputs from new data. Examples include answering a user query with an LLM, generating an image from a prompt, or classifying an input in real time. Inference is where models deliver actual business value to end users, but unfortunately, it can be expensive, operationally complex, and inefficient at scale. Many teams overpay for general-purpose cloud GPUs, struggle with unpredictable latency, or lack the expertise to optimize models for production.

Training is the process of building a model by learning parameters from large datasets. It is highly compute-intensive but typically performed intermittently during initial model development or periodic retraining. Inference is the process of running a trained model to generate outputs from new inputs. It operates continuously in production and often accounts for the majority of long-term AI infrastructure costs. For most production AI systems, inference (not training) is the dominant cost and operational challenge. aion supports both batch and real-time inference, with infrastructure optimized for predictable performance, high utilization, and cost control.

aion cuts inference costs through multiple optimizations: targeted hardware that selects purpose-built GPU instances rather than overpriced servers; high utilization by scheduling and batching workloads to keep GPUs busy; software optimizations like model quantization and better memory management; and pay-as-you-go pricing so you only pay for the compute you actually use. Together, these measures let customers achieve a much lower cost per inference compared to traditional cloud setups.

aion supports a broad set of leading open-source and commercial-grade models, including state-of-the-art language, vision, and multimodal architectures. Models are continuously benchmarked and ranked through aion's serverless inference leaderboard, helping customers choose the best model for quality, latency, and cost.

aion Forge is self-service and designed for builders: training, fine-tuning, and deploying models with flexible, instant access to compute. aion Nexus focuses on custom Enterprise AI solutions tailored for your business. We leverage the power of aion Forge plus embed our world-class Forward-Deployed Engineers to transform your products and business.

Most providers advertise only $/GPU-hr with opaque pricing and surprise invoices. They omit precision tier, region, interconnect or commitment details. CFOs cannot compare apples-to-apples and worry about billing shocks. aion offers transparent, predictable pricing with no hidden fees.

aion's Forward-Deployed Engineers (FDEs) work directly with your team to: architect optimal training and inference pipelines, optimize models for performance and cost, integrate aion into your existing stack, and accelerate time-to-production. They function as an extension of your engineering team, reducing risk and speeding execution.

Forward-Deployed Engineers are offered as a premium engagement, with pricing dependent on scope, duration, and technical complexity. This flexible model ensures customers only pay for the level of hands-on support they need, without committing to long-term consulting contracts.

Aion uses a transparent, hourly, usage-based pricing model. There are no up-front fees, licenses, or long-term commitments—you pay only for the compute resources you actively use per hour. By charging per hour, aion makes AI infrastructure costs predictable, controllable, and proportional to real workload demand.

No, aion does not require any long-term commitments or exclusive contracts from its customers. The platform is available on-demand, and you are free to start or stop using it at any time without penalties. There is no vendor lock-in—you retain full flexibility to integrate aion into your stack on your own terms and to adjust your usage.

CEO and Founder Jayden Watson is a self-taught coder, algorithmic trader, and crypto-native founder who has spent over a decade building at the frontier of AI, blockchain, and systems design. Co-Founder Christian Angermayer is a serial entrepreneur, investor and executive film producer. He is the founder of the private investment firm Apeiron Investment Group and the biotechnology company atai Life Sciences, and a co-founder of the blockchain startup Plasma. The Executive team at aion are also serial founders and bring decades of experience leading technology companies like Google, Amazon, and more.