How Can Pay-Per-Token Inference Services Reduce AI Costs

In an era where AI is reshaping all businesses, your team is facing these challenges:

• Over-provisioning and Severe Resource Waste: To handle business peaks, you are forced to pre-procure large amounts of expensive GPU computing power. However, during most off-peak periods, these valuable hardware resources sit idle, leading to high costs.

• Huge initial investment and high trial-and-error costs: Every new AI application idea requires going through complex budget approval, hardware procurement, and deployment processes. The pace of innovation is slowed down by heavy capital expenditure.

• Volatile traffic and significant architectural challenges: A product or feature suddenly going viral can instantly overwhelm self-built inference services, while emergency scaling is time-consuming and labor-intensive, negatively impacting user experience and business revenue.

Assume your AI application needs to handle 100,000 API calls per day (peak up to 15 QPS, on average around 1.16 QPS), with each call consuming an average of 1000 Tokens.

Let's compare three approaches:

• Assume purchasing a server equipped with an NVIDIA A100 (80GB), with a monthly cost of approximately $4,500 (estimated based on major cloud provider rental prices).

• You need to invest dedicated DevOps manpower for environment setup, model maintenance, and troubleshooting – these hidden costs are not yet calculated.

• Total Monthly Cost ≈ $5,000+ (Running a server that can handle 15 QPS with an average load of 1.16 QPS results in very low resource utilization.)

• Many providers offer monthly plans with fixed QPS quotas. To meet a peak of 15 QPS, you might need to purchase a medium-sized plan.

• Assuming the package monthly fee is $5500. (This usually includes hardware costs, operational services, and vendor profits) Even if the business volume decreases one day, you still need to pay the full amount.

• Total monthly cost = $5500 (paying for peak usage, low flexibility)

• Taking deepseek-v3.1 as an example:

Assuming each input call uses 100 tokens, and each output call uses 1000 tokens.

Note: The costs in this example are based on application scenarios where the 'output is much greater than the input' (such as content generation and dialogue interaction).

• Daily Output Cost Calculation: 100,000 calls/day * 1000 Tokens/call / 1,000,000 * $1.0 = $100 / day

• Daily Input Cost Calculation: 100,000 calls/day * 100 Tokens/call / 1,000,000 * $0.27 = $2.7 / day

• Total Monthly Cost = $102.7 * 30 = $3081

The conclusion is clear: in a pay-per-token scenario, compared to plans A and B, the pay-per-token model significantly reduces costs. Additionally, when your business volume exceeds the capacity of the compared monthly subscription plans, the cost savings from pay-per-token will be even more pronounced. This model ensures that your costs are always synchronized with business value (actual usage).

Note: The pay-per-token model is particularly suitable for businesses with high traffic fluctuations, startups, or those wishing to greatly reduce trial and error costs. For applications with extremely stable and predictable traffic, it is advisable to conduct a detailed TCO (Total Cost of Ownership) comparison before making a choice.

Low cost does not come at the expense of performance. This is powered by a robust technical engine:

• Extreme Model Optimization: Pay-per-token services employ cutting-edge technologies like model quantization and compilation optimization. These drastically improve inference speed with almost no loss in accuracy, reducing the hardware cost per inference and passing the savings on to customers.

• Automatic Scaling: No need to worry about the number of servers. The scheduling system automatically creates and releases resources in milliseconds based on real-time request traffic. It scales seamlessly during traffic spikes and scales down to zero during troughs – you only pay for what you use.

Choosing pay-per-token means:

• Zero Upfront Investment: Start your AI project immediately, without long procurement and deployment cycles.

• Costs Perfectly Aligned with Business Growth: Your cost curve will closely align with your business growth curve, making financial forecasting clearer than ever before.

• Focus on Innovation, Not Operations: Free your valuable engineering team from complex infrastructure maintenance, allowing them to focus on building better products and application logic, rather than managing GPU servers.