Do you evaluate SLMs for performance compared to Azure AI’s cloud-based LLMs?

When using Azure AI services, you often choose between Small Language Models (SLMs) and powerful cloud-based Large Language Models (LLMs), like Azure OpenAI. While Azure OpenAI offer significant capabilities, they can also be expensive. In many cases, SLMs like Phi-3, can perform just as well for certain tasks, making them a more cost-effective solution. Evaluating the performance of SLMs against Azure OpenAI services is essential for balancing cost and performance.

A startup builds a simple FAQ chatbot that answers repetitive customer service questions like “What are your business hours?” or “How do I reset my password?” They choose to implement Azure OpenAI services, leading to high operational costs. An SLM could have provided the same answers without the extra expense.

Figure: Bad example - Using Azure OpenAI services for simple FAQ tasks incurs high costs, while an SLM would be more cost-effective

A financial services company needs to develop a chatbot to guide customers through complex mortgage applications, requiring the chatbot to understand intricate details and provide personalized advice. After evaluating both, they use Azure OpenAI GPT-4o due to its better handling of complex queries and personalized responses, which an SLM could not manage effectively.

Figure: Good example - Choosing Azure OpenAI GPT-4o for complex tasks after evaluation provides better customer service and justifies the higher cost

Why evaluate SLMs?

Cost considerations: Azure OpenAI services, such as GPT-4o, charge per usage, which can quickly add up. On the other hand, SLMs, which can be deployed locally or in a more cost-efficient environment, may offer similar results for less complex tasks, reducing overall costs

Performance needs: Not every task requires the full power of a cloud-based LLM. Tasks like text classification, keyword extraction, or template-based responses can often be handled just as well by an SLM, saving both on compute resources and cost

Model control: Using an SLM, particularly if it is deployed locally, offers more control over the model’s behavior, updates, and fine-tuning. This can be valuable for applications where privacy, security, or specific customizations are required

How to evaluate SLMs against Azure OpenAI services

Set performance benchmarks: Run both the SLM and Azure OpenAI services on the same dataset or task. Measure their performance in terms of accuracy, response quality, latency, and resource consumption

Compare output quality: Test how well each model responds to different types of queries, from simple to complex. While Azure’s LLMs might excel at complex, open-ended tasks, an SLM may be sufficient for simpler, well-defined tasks

Consider deployment environment: Evaluate whether the SLM can be easily integrated into your existing Azure infrastructure. Consider factors like memory and CPU requirements, latency, and whether an SLM can match the scalability offered by Azure’s cloud services

Estimate long-term costs: Calculate the ongoing costs of using Azure’s LLMs, factoring in API fees and compute resources. Then, compare these costs with the deployment and maintenance costs of an SLM, especially for high-volume applications. Long-term savings can be substantial when using SLMs for tasks where full LLM power is unnecessary

When to stick with Azure AI’s cloud LLMs

For complex tasks that require deep understanding, creativity, or nuanced language generation, Azure OpenAI service, like GPT-4o, may still be the best choice
Cloud-based LLMs offer ease of scalability and quick integration with Azure services, making them ideal for projects that need high availability or require rapid deployment without complex infrastructure management

By evaluating SLMs against Azure OpenAI services, you can make informed decisions that balance performance with cost, ensuring your AI deployments are both efficient and economical.

AI Development