01What inference is
Inference is the process of an AI model generating an output given an input. When you ask ChatGPT a question, the model runs inference: it processes your input through its billions of parameters and generates a response, one token at a time.
The word 'inference' distinguishes this from 'training', which is the process of creating the model in the first place. Training a large language model is enormously expensive (hundreds of millions of dollars) and happens once, or periodically when models are updated. Inference happens every time anyone submits a query; it is less expensive per query but happens at very high volume.
02Why inference has cost and speed implications
Inference is computationally demanding. Processing a query through a model with hundreds of billions of parameters requires significant GPU compute time. This creates two practical business implications.
Cost: AI API pricing is based on inference costs. You pay per token processed (input tokens) and per token generated (output tokens). At scale, inference costs can be significant. An organisation with 1,000 users each submitting 50 substantial queries per day is running 50,000 inferences daily, each consuming computing resources that accumulate into meaningful cost.
Speed: the time to generate a response depends on the size of the model, the length of the context, the length of the requested output, and the available compute infrastructure. Larger models take longer to generate responses than smaller ones; longer responses take longer than shorter ones. For real-time customer service applications, response latency is a critical performance dimension.
03Inference infrastructure and enterprise deployment
Most enterprises access AI inference through cloud services (Azure OpenAI Service, AWS Bedrock, Google Cloud AI) rather than running models on their own hardware. This means inference costs are variable (pay per use) rather than fixed.
For organisations deploying AI at scale, understanding inference cost economics is part of AI programme financial management. The choice between different models involves tradeoffs between capability (larger models are typically more capable), cost (larger models cost more per inference), and speed (larger models respond more slowly).
Azure OpenAI Service provides enterprise inference infrastructure with the governance controls, security, and SLAs that enterprise AI deployment requires. Understanding that your Microsoft Copilot and Azure AI deployments are running inference at scale on Azure's infrastructure is relevant context for cost management and operational resilience planning.
Key Takeaways
- 1.Inference is the process of an AI model generating a response to a query; it is distinct from training (creating the model) and happens every time anyone submits a query.
- 2.Inference is computationally demanding, creating per-query costs (measured in tokens) and response latency that scale with query volume.
- 3.At organisational scale, inference costs are significant and should be part of AI programme financial management alongside licence costs.
- 4.Model size involves tradeoffs: larger models are more capable but more expensive and slower than smaller models.
- 5.Enterprise inference infrastructure (Azure OpenAI, AWS Bedrock) provides the governance controls and SLAs that consumer AI access does not.
References & Further Reading
- [1]Azure OpenAI Service: PricingMicrosoft
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