AI inference cost optimization: An enterprise guide

What are AI inference costs?

Inference occurs whenever an AI tool is used, such as when a user provides a prompt. Each inference event generates a cost for the business operating the AI platform. Thus, inference cost is the collective cost of processing a query or task through a trained machine learning (ML) model to produce an outcome, make a decision or elicit a response.

Token use is what fundamentally drives inference. A token is a basic unit of data that an ML model processes. Every inference generates numerous tokens that result when the prompt or request is broken down into actionable components. For example, a prompt such as "Hello!" might be broken down into three tokens: the "H," the "ello," and the "!". Each token incurs compute costs, so larger, more sophisticated prompts use more tokens, and the more user volume, the higher the inference cost for the business.

Effective AI inference cost optimization starts with a clear understanding of the underlying cost factors in inference. There are several cost elements involved in AI inference:

• ML model complexity. As models become more complex, they need more compute resources to maintain satisfactory throughput and response times. This increases the AI tool's cost per token and the long-term inference costs. Consequently, selecting the optimal model for the application has a profound effect on inference costs. Using an unnecessarily complex model for a relatively simple application results in larger inference costs, while using a budget model for a sophisticated AI task impairs performance.
• Token use patterns. The number of tokens used also affects inference costs, with longer, more sophisticated queries incurring higher inference costs. In general, budget models for simple tasks can cost $0.14 to $1 per million tokens, midrange models $2 to $15 per million tokens, and high-end models for complex tasks and coding $20 to $75 per million tokens. At $5 per million tokens, an average use of 20 million tokens per day will cost $100 per day, or $3,000 per month before output tokens and other costs. Techniques that simplify queries, reduce token use and cache repeated elements can cut token costs with little effect on performance.
• Compute hardware and infrastructure. It's important to consider the amount and complexity of hardware resources allocated to models. Applying too few resources can shave costs but lower model performance. Overprovisioning underutilized GPUs and other resources can inflate token costs. Look for at least 50% GPU utilization for smaller models and 80% or more for more complex models to get the most from hardware costs.
• Model stack configuration. The way a model is deployed can affect its throughput performance and cost per token. Inefficient deployment stacks can degrade performance, leading to increased resource allocation and higher token costs. Optimize the model's runtime environment to make the most efficient use of GPU resources and maximize throughput while minimizing cost per token. Experienced AI architects can help design and optimize the most effective model environments.
• Data characteristics and movement. AI demands extensive data movement among GPUs, memory and storage. Use high-bandwidth interconnects and architect the most efficient data pathways to improve data movement performance. This improves model throughput and performance, and reduces token cost during inference.
• Burst traffic behaviors. AI traffic often operates in bursts, stretching resources and impairing performance. Traditional scaling methodologies -- such as GPU utilization-based scaling -- can easily lead to overprovisioning, where extra GPU capacity goes unused until the next traffic burst. Instead, examine other traffic factors, such as queue length and batch size, and base scaling decisions on those metrics.

Challenges of AI inference cost management

AI inference cost optimization is difficult because it often involves combining highly nuanced, granular cloud costs with multiple model expenses and unpredictable use patterns. Challenges include the following:

• Unchecked token use. AI inference costs spiral out of control when token use is unchecked. Such token bloat can occur because of redundant or overly complex prompts, or when agentic AI agents loop through many varied requests to solve problems or execute tasks. For example, an agentic task can consume 30 times as many tokens as a basic chat. Techniques such as context layers can help the AI system access specific data to reduce token bloat.
• Poor hardware utilization. AI requires sophisticated processing chips such as GPUs, TPUs, NPUs and ASICs. These chips cost money to operate, and every processing cycle that they sit idle wastes money. Use metrics to determine actual hardware utilization, right-size GPU instances, use effective workflows and data pipelines, and consider techniques such as model compression to enhance hardware utilization and efficiency.
• Resource overprovisioning. It's easy to overprovision computing resources to ensure ample headroom and avoid bottlenecks, but this can lead to poor utilization. Overprovisioning can also happen when high-end, top-tier models are used for simple tasks. Techniques such as model routing can direct queries to the most appropriate model to handle the request cost-effectively.
• Unchecked costs. Typical infrastructure costs are relatively straightforward to track, but AI costs are unusually complex because their operations are closely tied to sophisticated math on GPUs and other chips. This can make it extraordinarily difficult to assign token or infrastructure costs to specific departments, teams, users or other AI stakeholders. FinOps practitioners can help establish the metrics and KPIs that correlate AI token and infrastructure use to cost figures.

7 AI inference cost optimizations strategies

So, what's the secret to managing and mitigating AI inference costs? Many strategies can help, but each depends on awareness: a careful analysis and understanding of the actual cost factors, backed by real-world performance metrics that serve as a basis for comparison and improvement. The most important approaches include the following:

1. Apply context compression

Fewer input tokens mean lower costs. One effective way to reduce the number of input tokens is to shrink larger prompts, conversations or documents into smaller summaries. This technique can reduce input tokens by 50%-70% by removing redundant or unnecessary filler words and discarding outdated details to save memory, time and cost. A variation of this technique is to reduce the context window size to send only what the model needs.

2. Route prompts to the most appropriate model

Mindlessly relying on a single model for everything can result in higher costs. Consider a task's needs, then route it to the most appropriate model. Simple classifications should be handled by a simple model, coding by a mid-tier model and complex reasoning by a top-tier model. While model routing -- sometimes called semantic routing -- can increase complexity, it can also cut inference costs in half.

3. Cache routinely used prompts

Prompt caching stores repetitive, unchanging parts of a prompt, such as reference media or recurring instructions, so they don't need to be included with each new prompt. This can reduce the number of tokens that must be reprocessed each time, thereby cutting costs.

4. Take advantage of batch processing

Batch API processing lets businesses group huge numbers of AI prompts into one file that can be submitted and processed asynchronously. This might take 12 to 24 hours to return a response, so it's best-suited for offline workloads, such as content generation, because real-time responses are essentially impossible. Still, some major AI platforms offer discounts on input and output tokens processed through batch endpoints because GPUs are always kept busy.

5. Apply quantization where possible

The quantization process reduces the mathematical precision of model weights, such as from 32-bit to 8-bit or even less. This reduces model size and memory use while maintaining an acceptable level of accuracy for the task at hand. For simpler tasks, quantization can yield cost savings depending on the workload, model and required precision.

6. Select suitable runtime enhancements

It's best to use server and platform technologies that are best suited to inference tasks, such as Nvidia's NIM, TensorRT and Triton platforms. Combining optimized hardware with dynamic batching can dramatically reduce token use and compute costs. In many cases, batch size and concurrency can balance latency and throughput for optimal results. Similarly, speculative decoding techniques can speed LLMs, using fewer tokens per second without reducing output quality.

7. Optimize infrastructure

It's important to consider how infrastructure drives AI functionality and optimizes resources, particularly for cloud infrastructure. This involves right-sizing GPU instances from the start and configuring scaling behaviors based on metrics that best align with the real computing load, such as GPU use, queue size and batch size. Right-sizing reduces costly overprovisioning. Cost-saving mechanisms, such as spot capacity and scaling stabilization, prevent thrashing when loads are bursty or rapidly changeable.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.