The pricing structures for Azure AI Services and Azure OpenAI differ significantly, reflecting their distinct functionalities and usage models. Here are the key differences:
- Azure AI Services: Typically follows a pay-as-you-go model, where costs are based on the specific services and APIs used. Each service has its own pricing structure, often defined by usage metrics such as the number of API calls, processing time, or data processed. For example, Azure Cognitive Services for speech-to-text may charge per hour of audio processed or per number of characters transcribed.
- Azure OpenAI: Offers two main pricing models:
- Pay-As-You-Go (PAYG): Users pay based on the number of tokens processed, which is approximately 750 words per 1,000 tokens. For instance, as of August 2024, the pricing for the GPT-4 model is around $0.03 for input and $0.06 for output per 1,000 tokens.
- Provisioned Throughput Units (PTUs): This model allows businesses to reserve a certain amount of processing capacity for consistent performance, which is beneficial for applications with predictable workloads.
- Azure AI Services: Pricing can vary greatly depending on the specific service. For example, Azure may charge $1 per 100,000 words for text summarization, while other services like image analysis may have different rates based on the complexity of the task.
- Azure OpenAI: The costs are more straightforward and primarily based on token usage. For example, the pricing for text generation or summarization is typically lower than that of Azure AI Services, with OpenAI offering rates such as $0.5 per 100,000 words compared to Azure's $1 for similar services.
3. Fine-Tuning and Hosting Costs
- Azure AI Services: Generally, there is no specific fine-tuning cost, as most services provide pre-trained models that can be used directly. Customization may involve additional development costs but does not typically incur ongoing fees.
- Azure OpenAI: Fine-tuning models incurs costs based on three factors: training hours, hosting hours, and inference per 1,000 tokens. For instance, hosting a fine-tuned model can incur hourly costs even when not actively used, which requires careful cost management.
4. Additional Features and Costs
- Azure AI Services: This may include various features like automated evaluations, monitoring, and content safety, which could have separate pricing structures or be included in the overall service costs.
- Azure OpenAI: Additional costs may arise from using complementary services like Azure Monitor for logging and monitoring usage, which can add to the overall expenditure.
In summary, while both Azure AI Services and Azure OpenAI provide valuable AI capabilities, their pricing structures differ significantly. Azure AI Services typically have a more varied pricing model based on service-specific metrics, while Azure OpenAI offers a clear token-based pricing structure with options for consistent performance through PTUs. Understanding these differences is crucial for organizations to manage costs effectively while leveraging AI technologies.
