How Alibaba Cloud's Aegaeon Slashed GPU Usage By 82% While AI Giants Scramble For Chips

Last Updated on October 21, 2025 1:10 pm by Laszlo Szabo / NowadAIs | Published on October 21, 2025 by Laszlo Szabo / NowadAIs

How Alibaba Cloud’s Aegaeon Slashed GPU Usage by 82% While AI Giants Scramble for Chips – Key Notes Section

Dramatic Hardware Reduction Through Software Innovation: Alibaba Cloud’s Aegaeon proved that smart resource scheduling can reduce GPU requirements by 82%, cutting hardware needs from 1,192 Nvidia H20 GPUs to just 213 for serving dozens of large language models. This achievement demonstrates that software optimization can extract massive efficiency gains from existing hardware without requiring newer or more powerful chips, fundamentally challenging the assumption that better AI performance requires proportionally more hardware investment.
Token-Level Auto-Scaling Enables Unprecedented GPU Sharing: The system’s breakthrough lies in performing auto-scaling decisions at the token level during inference, allowing a single GPU to dynamically switch between up to seven different models mid-generation. This approach reduces model-switching latency by 97% while achieving 2 to 2.5 times higher request rates compared to alternative solutions, making it possible for cloud providers to serve many models concurrently on shared infrastructure without degrading user experience.
Strategic Response to Geopolitical Hardware Constraints: Alibaba Cloud’s Aegaeon emerged as Chinese tech companies faced severe restrictions on accessing cutting-edge Nvidia GPUs, forcing innovation in software efficiency rather than hardware scaling. The system’s techniques work equally well with domestic alternatives like Huawei’s Ascend chips, potentially neutralizing the competitive disadvantage created by export controls while establishing optimization approaches that benefit the entire industry regardless of which hardware manufacturers dominate future markets.

Alibaba Cloud’s Aegaeon Slashed GPU Need

When tech companies are spending billions on graphics processing units to power their artificial intelligence dreams, Alibaba Cloud’s Aegaeon system just proved you don’t always need more hardware—you need smarter software. The company’s latest innovation has managed to cut Nvidia H20 GPU usage from 1,192 units down to just 213 for serving dozens of large language models, achieving an 82% reduction that’s making waves across the tech industry.

This isn’t just about saving money on expensive chips. Alibaba Cloud’s Aegaeon represents a fundamental rethinking of how cloud providers manage artificial intelligence workloads at scale, especially at a time when access to cutting-edge hardware has become a geopolitical chess match.

The Problem With Traditional GPU Allocation Was Massive Waste

Cloud service providers face a peculiar challenge when running model marketplaces. Companies like Alibaba Cloud’s Aegaeon team and ByteDance’s Volcano Engine serve thousands of AI models simultaneously to users around the world. But here’s the catch: not all models are created equal in terms of popularity.

Research from Peking University and Alibaba Cloud revealed something shocking during their analysis. Nearly 17.7% of GPUs allocated in Alibaba Cloud’s marketplace were serving only 1.35% of actual requests. That’s like keeping an entire warehouse staffed and powered just to store a few boxes in the corner. Popular models like Alibaba’s Qwen and DeepSeek handled the bulk of inference requests, while hundreds of other models sat idle most of the time, each occupying its own dedicated GPU.

The traditional approach was simple but wasteful: assign one GPU to each model, keep it running, and hope someone requests that model. This made sense from a simplicity standpoint, but it created massive inefficiencies. Think of it like having a dedicated chef for every possible dish on a restaurant menu, even the ones ordered once a month. You’re paying for labor that’s sitting around doing nothing most of the time.

For Chinese companies, the problem became even more acute after U.S. export restrictions limited access to Nvidia’s most powerful chips. While the H20 GPU was specifically designed to comply with export controls, these chips were still expensive and in limited supply. Companies needed to squeeze every ounce of performance from the hardware they could actually obtain.

Token-Level Auto-Scaling Changed Everything About GPU Sharing

Alibaba Cloud’s Aegaeon took a radically different approach to solving this resource allocation nightmare. Instead of assigning entire GPUs to individual models and hoping for efficiency, the system performs what researchers call “auto-scaling at the token level”. This technical innovation sounds abstract, but its implications are profound.

Need ROI on Social Media? Create content with AI!
Join 100,000+ businesses in 180+ countries using Ocoya!

Here’s how it works in practice: When a large language model generates a response, it doesn’t produce the entire answer at once. Instead, it generates text one token at a time—roughly one token per word or word fragment. Traditional systems would dedicate an entire GPU to one model for the complete response generation process, locking up resources even during the brief pauses between token generation.

Alibaba Cloud’s Aegaeon realized these pauses were opportunities. By scheduling work at the token level, the system can switch a GPU between different models during inference, mid-generation. One GPU might generate a few tokens for Model A, then instantly pivot to generate tokens for Model B, then back to Model A, all within milliseconds. To the end users, responses still arrive quickly and smoothly—but behind the scenes, that single GPU is serving up to seven models simultaneously instead of the usual two or three.

The researchers at Peking University and Alibaba Cloud achieved this through three key technical breakthroughs. First, they implemented aggressive component reuse, avoiding the need to reload model elements unnecessarily. Second, they developed explicit memory management techniques that keep GPU memory organized and accessible. Third, they created fine-grained KV cache synchronization, which maintains the context needed for coherent text generation while sharing resources.

The result? Model-switching latency dropped by 97%. That means Alibaba Cloud’s Aegaeon can juggle multiple models on a single GPU without users noticing any slowdown in response times. The system achieved 2 to 2.5 times higher request arrival rates compared to alternative solutions, and delivered 1.5 to 9 times more “goodput”—a measure of effective, useful output.

Real-World Testing Proved The Numbers Weren’t Just Marketing Hype

When companies announce dramatic efficiency improvements, skepticism is healthy. But Alibaba Cloud’s Aegaeon underwent rigorous beta testing in a real production environment for over three months before publication. This wasn’t a controlled laboratory experiment with ideal conditions—it was deployed in Alibaba Cloud’s actual model marketplace, serving real customers making unpredictable requests.

The testing environment included dozens of large language models with parameters reaching up to 72 billion. These weren’t toy models or simplified versions created for testing purposes. They were production-ready models that customers relied on for business operations. During the three-month period, Alibaba Cloud’s Aegaeon successfully reduced the GPU count from 1,192 Nvidia H20 units down to 213.

The academic credentials backing this work add credibility too. The research paper was accepted and presented at the 31st Symposium on Operating Systems Principles (SOSP) in Seoul, South Korea. SOSP is considered one of the premier academic conferences for computer systems research, and papers undergo rigorous peer review before acceptance. Alibaba Cloud’s Chief Technology Officer, Zhou Jingren, was listed among the paper’s authors alongside researchers from Peking University.

The system has now moved beyond beta testing. Alibaba Cloud’s Aegaeon technology has been deployed in the company’s Bailian platform, which serves Qwen models to enterprise customers. This marketplace hosts some of the most widely-used open-source language models in China, with over 90,000 derivative models developed based on the Qwen family on platforms like Hugging Face.

Need ROI on Social Media? Create content with AI!
Join 100,000+ businesses in 180+ countries using Ocoya!

For organizations using thousands of GPUs, an 82% reduction translates to enormous cost savings. An individual Nvidia H20 GPU costs between $12,000 and $15,000. Reducing a deployment from 1,192 units to 213 means the difference between spending roughly $14 million to $18 million versus $2.6 million to $3.2 million—a savings of over $11 million in hardware procurement costs alone. Those savings don’t even account for reduced power consumption, cooling requirements, and data center space.

The China Context Makes This Achievement Even More Strategic

Understanding Alibaba Cloud’s Aegaeon requires understanding the unique pressures facing Chinese tech companies. Nvidia CEO Jensen Huang stated in October 2025 that the company had lost its entire Chinese market share, going “from 95% market share to 0%”. This dramatic statement reflects the cascade of U.S. export restrictions that progressively cut off Chinese companies from America’s most advanced AI chips.

The H100 and A100 GPUs—Nvidia’s flagship AI accelerators—have long been banned from export to China. The H20 was specifically designed as a cut-down version that complied with export control thresholds while still providing meaningful AI capabilities. But even the H20 faced uncertainty, with Chinese regulators ordering tech giants to cancel orders in September 2025 citing security concerns.

This hardware scarcity created an existential pressure: Chinese AI companies needed to compete globally while having limited access to the best chips. Domestic alternatives like Huawei’s Ascend series are improving but still lag Nvidia in both raw performance and ecosystem maturity. The Ascend 910C, for instance, offers more memory than Nvidia’s H100 but generally trails in compute performance.

Alibaba Cloud’s Aegaeon provides a software solution to a hardware problem. By making each available GPU radically more efficient, Chinese companies can close the gap with competitors who have easier access to cutting-edge chips. The system demonstrates that even with restricted hardware access, clever software optimization can extract performance that was previously being wasted.

The timing is also significant. While Nvidia reported massive revenue growth from AI chip sales globally, Chinese companies were forced to innovate in efficiency rather than simply scaling up hardware. This constraint-driven innovation might actually provide long-term advantages, as the techniques developed for Alibaba Cloud’s Aegaeon could work equally well with domestic chips from Huawei or other Chinese manufacturers.

How Aegaeon Compares To The DeepSeek Software Efficiency Movement

How Alibaba Cloud's Aegaeon Slashed GPU Usage by 82% While AI Giants Scramble for Chips - inline image — How Alibaba Cloud’s Aegaeon Slashed GPU Usage by 82% While AI Giants Scramble for Chips – inline image

Alibaba Cloud’s Aegaeon fits into a broader trend of Chinese AI companies achieving impressive results through software optimization rather than just throwing more hardware at problems. The most prominent example is DeepSeek, a Chinese startup that stunned the industry by training high-performance AI models at a fraction of the expected cost.

DeepSeek achieved its efficiency through several techniques that share philosophical similarities with Alibaba Cloud’s Aegaeon. The company used a mixture-of-experts architecture, where only relevant portions of the model activate for any given query, reducing computational needs. They optimized at the GPU instruction level using PTX programming instead of CUDA, giving engineers finer control over how work gets scheduled. They also automated much of the reinforcement learning process that typically requires expensive human review.

While DeepSeek focused on making model training and inference more efficient within a single deployment, Alibaba Cloud’s Aegaeon tackles efficiency across many models sharing infrastructure. One optimizes how you build and run individual models; the other optimizes how you serve dozens of models concurrently to thousands of users. Both approaches challenge the assumption that better AI performance requires linearly more hardware investment.

The broader implication is that software optimization in AI is nowhere near its limits. For years, the industry operated under the belief that bigger models trained on more data using more chips was the only path forward. DeepSeek and Alibaba Cloud’s Aegaeon are proving that architectural cleverness and efficiency innovations can deliver comparable or superior results.

This matters beyond just Chinese companies dealing with chip restrictions. As AI models proliferate and inference costs mount, any organization running AI at scale faces pressure to optimize. The techniques pioneered by systems like Alibaba Cloud’s Aegaeon will likely spread across the industry, helping cloud providers everywhere reduce their GPU footprints.

The Technical Architecture Reveals Sophisticated Engineering Decisions

The elegance of Alibaba Cloud’s Aegaeon lies in how it handles the complex choreography of switching between models without breaking user experiences. When the system decides to switch which model a GPU is processing, several things must happen almost instantaneously.

First, the system must save the current state of the model being paused—specifically, the key-value cache that maintains conversation context. This cache remembers what’s been discussed so far, allowing the model to generate coherent responses. Losing this information would break the conversation flow, so Alibaba Cloud’s Aegaeon synchronizes this cache efficiently.

Second, the system must load the relevant components of the next model that needs GPU time. This includes model weights and parameters. Alibaba Cloud’s Aegaeon uses explicit memory management to keep frequently-needed components readily accessible, avoiding expensive reloads.

Third, the scheduling algorithm must decide which model gets GPU time when. Alibaba Cloud’s Aegaeon prioritizes popular models that are actively handling high request volumes, while cold models (those rarely requested) borrow brief slices of compute only when needed. This prevents the system from degrading performance for high-traffic models while still serving low-traffic models efficiently.

The researchers measured auto-scaling overhead at just 3% of total processing time—meaning 97% of the GPU’s work goes toward actual inference rather than administrative task switching. For comparison, older serverless systems might dedicate 20-30% of resources just to managing model loading and unloading.

The system also implements intelligent request batching and scheduling. When multiple requests arrive for the same model, Alibaba Cloud’s Aegaeon can batch them together, processing them more efficiently than handling each individually. When requests for different models arrive, the scheduler determines optimal switching points—typically between token generation steps—to minimize disruption.

Industry Response Suggests This Approach Will Spread Quickly

The tech industry’s reaction to Alibaba Cloud’s Aegaeon has been notable for its recognition that the underlying problem is universal. While the system was developed within Alibaba, the inefficiency it addresses—poor GPU utilization in multi-model serving environments—affects every major cloud provider.

ByteDance’s Volcano Engine, for instance, serves similar AI model marketplaces and faces identical challenges with resource allocation. Amazon Web Services, Google Cloud Platform, and Microsoft Azure all run model serving infrastructure where popular models dominate traffic while long-tail models sit mostly idle. The techniques in Alibaba Cloud’s Aegaeon could translate to these environments with appropriate adaptation.

GPU pooling and resource optimization have been active research areas for years. Companies like MemVerge have developed fractional GPU technologies that allow multiple workloads to share GPU resources more efficiently. NVIDIA offers virtualization software for its GPUs. But Alibaba Cloud’s Aegaeon appears to be the first system to achieve token-level scheduling specifically optimized for concurrent large language model serving at scale.

The peer-reviewed publication at SOSP means the techniques are now part of the public research record. While proprietary implementation details may remain within Alibaba, the core concepts are available for other researchers and companies to study and build upon. This is typical in systems research, where publication serves both to establish precedence and to advance the field collectively.

Some skepticism remains about whether the 82% reduction represents comparison against industry best practices or against Alibaba’s previous suboptimal setup. One Reddit commenter noted that the reduction might reflect improvement from inefficient baseline practices rather than superiority over other companies’ solutions. The Register similarly pointed out that hyperscalers typically keep their optimization techniques confidential, so it’s difficult to know if competitors have already solved similar problems internally.

Regardless of whether Alibaba Cloud’s Aegaeon represents absolute cutting-edge or catching up to best practices, the published results demonstrate that major GPU efficiency gains remain achievable. For organizations currently running AI infrastructure with low utilization rates, the paper provides a roadmap for potential improvements.

What This Means For The Future Of AI Infrastructure Economics

The emergence of systems like Alibaba Cloud’s Aegaeon signals a maturation in how the industry thinks about AI infrastructure. The initial AI boom was characterized by a land grab for GPUs—whoever could secure the most chips could train the biggest models and run the most inference workloads. This drove Nvidia’s valuation to stratospheric heights as demand far outstripped supply.

But the GPU scarcity is forcing a reckoning with efficiency. When chips are abundant and cheap, there’s little incentive to optimize utilization. When chips are expensive, scarce, or restricted by export controls, every percentage point of utilization matters. Alibaba Cloud’s Aegaeon demonstrates that dramatic efficiency gains are possible with software innovation alone.

This shift affects economics throughout the AI stack. Lower GPU requirements for serving models mean lower infrastructure costs for cloud providers. Those savings can translate to lower API pricing for customers, making AI applications more economically viable. Reduced hardware needs also mean lower power consumption and carbon footprints, addressing growing concerns about AI’s environmental impact.

For model developers and researchers, better GPU utilization democratizes access. When serving infrastructure becomes more efficient, smaller companies can compete with tech giants. A startup doesn’t need to purchase thousands of GPUs to run a competitive model serving business if software like Alibaba Cloud’s Aegaeon allows them to serve many models with a fraction of the hardware.

The geopolitical implications are also significant. If software optimization can offset hardware limitations, then chip export controls become less effective as tools of technological competition. Chinese companies forced to innovate around hardware restrictions may develop techniques that ultimately benefit the entire industry. The irony would be that restrictions intended to slow China’s AI development instead accelerated innovations in efficiency that other countries now want to adopt.

Practical Implications For Companies Running AI Infrastructure Today

For organizations currently operating AI infrastructure, Alibaba Cloud’s Aegaeon offers both inspiration and a benchmark. The first question any engineering team should ask is: what’s our current GPU utilization? Many companies are shocked to discover that their expensive accelerators spend significant time idle, waiting for work.

Measuring utilization requires proper monitoring tools. NVIDIA provides utilities like nvidia-smi that report GPU metrics including compute utilization, memory usage, and power consumption. Kubernetes-based deployments can use GPU operators and device plugins to track utilization across clusters. Simply gathering this data often reveals optimization opportunities that weren’t previously visible.

The specific techniques in Alibaba Cloud’s Aegaeon—token-level scheduling, explicit memory management, fine-grained cache synchronization—require sophisticated engineering. Not every organization has the resources to replicate this system from scratch. But commercial solutions are emerging that provide GPU pooling and optimization capabilities. Companies like MemVerge offer fractional GPU technologies, while cloud providers increasingly offer managed services that handle optimization automatically.

Organizations can also apply broader optimization strategies inspired by the philosophy behind Alibaba Cloud’s Aegaeon. Implementing intelligent request batching improves throughput. Using model quantization reduces memory requirements. Deploying smaller specialized models for simple tasks instead of large general models for everything cuts computational needs. These approaches don’t require replicating Alibaba’s exact system but still deliver meaningful efficiency gains.

Definitions Section

GPU (Graphics Processing Unit): A specialized electronic circuit originally designed for rendering graphics but now essential for artificial intelligence work because its architecture excels at performing many calculations simultaneously. Modern AI models require massive parallel processing to train on data and generate responses, making GPUs far more efficient than traditional CPUs for these workloads.

Token-Level Scheduling: A technique where computational work is divided and scheduled at the granularity of individual tokens—the basic units that language models process when generating text, roughly equivalent to words or word fragments. This fine-grained scheduling allows systems to switch between different models during text generation without users noticing delays, maximizing hardware utilization.

Inference: The process of using a trained AI model to generate outputs based on new inputs, such as answering questions or generating text. While training a model is typically a one-time computational expense, inference happens repeatedly every time a user interacts with the model, making inference efficiency critical for operational costs at scale.

Auto-Scaling: The ability of a system to automatically adjust computational resources allocated to different tasks based on current demand. In the context of Alibaba Cloud’s Aegaeon, auto-scaling happens at the token level, meaning GPU resources are reallocated between models during the brief intervals between generating individual words rather than requiring an entire request to complete before switching.

Model Marketplace: A platform where multiple AI models are hosted and made available to users through APIs or other interfaces. Companies like Alibaba Cloud and ByteDance operate marketplaces serving thousands of models simultaneously, creating unique challenges around resource allocation since request patterns are unpredictable and uneven across different models.

Large Language Model (LLM): An artificial intelligence system trained on vast amounts of text data to understand and generate human-like language. LLMs like GPT, Claude, and Alibaba’s Qwen contain billions of parameters and require substantial computational resources to operate, making efficient serving infrastructure essential for practical deployment.

H20 GPU: A specialized AI accelerator chip designed by Nvidia specifically to comply with U.S. export control regulations while still providing meaningful performance for Chinese customers. The H20 offers reduced capabilities compared to flagship chips like the H100 but remains more powerful than most alternatives available in the Chinese market.

KV Cache (Key-Value Cache): A memory structure used by language models to store context from previous parts of a conversation or text, allowing the model to generate coherent responses without recalculating everything from scratch. Efficient management of KV cache is critical for systems like Alibaba Cloud’s Aegaeon that switch between models frequently.

Goodput: A measure of useful throughput that accounts for quality of service, distinguishing productive work from wasted computational cycles. In AI serving contexts, goodput measures how many successful, usable model responses are generated per unit of computational resources, providing a better metric than raw throughput alone.

Mixture-of-Experts (MoE): An AI architecture where a model contains multiple specialized sub-models (experts) and a routing mechanism that determines which experts should process each input. This approach reduces computational costs because only a fraction of the model’s total parameters activate for any given request, similar to how Alibaba Cloud’s Aegaeon shares GPUs between models based on current demand patterns.

Frequently Asked Questions (FAQ)

How does Alibaba Cloud’s Aegaeon achieve 82% GPU reduction without degrading performance?

Alibaba Cloud’s Aegaeon accomplishes this dramatic efficiency gain through token-level auto-scaling, which allows a single GPU to serve up to seven different AI models simultaneously by scheduling work at extremely fine granularity. The system switches between models during the brief intervals between generating individual tokens (words or word fragments), maintaining smooth user experiences while maximizing hardware utilization. By implementing sophisticated memory management, component reuse, and KV cache synchronization techniques, Alibaba Cloud’s Aegaeon reduces model-switching overhead to just 3% of processing time, ensuring that GPUs spend 97% of their cycles on actual inference work rather than administrative tasks. The three-month beta test in Alibaba Cloud’s production marketplace demonstrated that this approach sustains 2 to 2.5 times higher request rates compared to alternative solutions while serving dozens of models with parameters reaching 72 billion.

Can Alibaba Cloud’s Aegaeon technology work with GPU alternatives like Huawei Ascend chips?

Yes, the techniques developed for Alibaba Cloud’s Aegaeon are fundamentally hardware-agnostic and can adapt to alternative accelerators including Huawei’s Ascend series, which is important given China’s push for semiconductor self-sufficiency. The system’s core innovations—token-level scheduling, intelligent resource pooling, and dynamic model switching—operate at the software layer and don’t depend on Nvidia-specific features, meaning they can coordinate work across different types of AI accelerators. While Alibaba Cloud’s Aegaeon was tested using Nvidia H20 GPUs because those were available during development, the scheduling algorithms and memory management techniques translate to other hardware architectures with appropriate adaptation. This flexibility is strategically valuable as Chinese companies face ongoing uncertainty about access to American chips and increasingly deploy domestic alternatives; software optimization systems like Alibaba Cloud’s Aegaeon ensure that limited or lower-performance hardware can still deliver competitive AI capabilities through superior resource utilization.

What’s the difference between Alibaba Cloud’s Aegaeon and DeepSeek’s efficiency innovations?

Alibaba Cloud’s Aegaeon and DeepSeek represent complementary approaches to AI efficiency that address different parts of the problem, with DeepSeek focusing on making individual models more efficient and Aegaeon optimizing how multiple models share infrastructure. DeepSeek achieved efficiency through model architecture innovations like mixture-of-experts structures that activate only relevant parameters for each query, plus low-level GPU instruction optimization using PTX programming instead of CUDA, demonstrating that smart training and deployment can match expensive models at far lower cost. In contrast, Alibaba Cloud’s Aegaeon tackles the multi-tenancy challenge that cloud providers face when serving thousands of models concurrently, using token-level scheduling to dynamically allocate GPU resources across many models rather than optimizing single-model performance. Both approaches share the philosophy that software cleverness can substitute for hardware scaling, proving that the industry’s efficiency frontier extends far beyond simply acquiring more chips. Organizations can benefit from both strategies simultaneously—using efficient model architectures like DeepSeek’s while deploying them on optimized serving infrastructure like Alibaba Cloud’s Aegaeon.

Will other cloud providers like AWS, Google Cloud, and Microsoft Azure adopt similar GPU pooling systems?

Major cloud providers are likely already working on or have deployed similar GPU optimization techniques, though most keep their infrastructure innovations proprietary rather than publishing detailed technical papers like Alibaba did with Aegaeon. The fundamental problem that Alibaba Cloud’s Aegaeon addresses—poor utilization when serving many models with uneven demand patterns—affects every cloud provider operating AI model marketplaces, creating universal economic pressure to improve efficiency. Companies like AWS have developed custom AI accelerators (Inferentia and Trainium) partly to control optimization opportunities that Nvidia’s closed ecosystem limits, while Google’s TPU architecture allows deep vertical integration of hardware and software for efficiency gains. The publication of Alibaba Cloud’s Aegaeon techniques at the prestigious SOSP academic conference means the core concepts are now part of the public research record, enabling other organizations to study these approaches and potentially incorporate similar strategies into their own infrastructure. Competition among cloud providers to offer the most cost-effective AI serving will accelerate adoption of advanced scheduling and pooling technologies across the industry.

How can companies running their own AI infrastructure implement techniques inspired by Alibaba Cloud’s Aegaeon?

Organizations don’t need to replicate Alibaba Cloud’s Aegaeon entirely to benefit from its underlying principles, as several practical steps can improve GPU utilization using commercially available tools and proven optimization strategies. Start by measuring current GPU utilization using monitoring tools like nvidia-smi or Kubernetes GPU operators to establish baselines and identify waste, as many organizations discover their expensive accelerators spend significant time idle waiting for work. Implement intelligent request batching to group similar workloads together, increase batch sizes to keep GPUs busier during each processing cycle, and use model quantization to reduce memory requirements and allow more models to fit on available hardware. For organizations running multiple models, consider commercial GPU pooling solutions from companies like MemVerge that provide fractional GPU capabilities, or explore managed services from major cloud providers that handle optimization automatically without requiring deep systems engineering expertise. Apply tiered model strategies where simple tasks use lightweight models and reserve expensive large models only for complex requests, similar to how Alibaba Cloud’s Aegaeon prioritizes popular models while serving cold models opportunistically; these incremental improvements compound to deliver substantial cost savings without requiring the sophisticated engineering resources that went into developing Aegaeon itself.