AI Engineering Manager at a tech vendor with 10,001+ employees
Real User
Top 20
Jul 4, 2025
In terms of pricing for Azure OpenAI, I would rate it as average compared to Gemini. Currently, Gemini is becoming increasingly popular, which prompts leadership to consider a switch primarily due to pricing. I am also using other Azure products such as Azure Document Intelligence for OCR purposes, but it is quite costly, and the output format in HTML isn't particularly impressive.
Data Engineer at a educational organization with 201-500 employees
Real User
Top 5
Mar 31, 2025
In the past, the primary expense involved token limitations which constrained scaling. Recent iterations have increased token allowances, mitigating some challenges associated with concurrent user access at scale.
The pricing is very good for handling various kinds of jobs. While small jobs are manageable, more complex jobs require a higher model, which is a bit challenging.
Associate consultant at a tech services company with 1,001-5,000 employees
Real User
Top 20
Dec 6, 2024
The pricing is standard and similar to other cloud-based systems. There might be minor differences. Generally, the costs are comparable across platforms.
While the product meets our business requirements well, I consider it relatively expensive, especially for individual users like myself. However, as I become more accustomed to its benefits, it may become more affordable over time.
Regarding pricing and licensing, it's a bit complex due to the minimum purchase requirement for PTO units. We're evaluating the best approach between PTE and pay-as-you-go models. Our organization is cautious about committing to PTE due to the fixed bandwidth reservation, while pay-as-you-go doesn't offer enough flexibility. We're discussing these matters with legal teams to ensure compliance and data security.
Senior management assistance for Christian Roy at a tech services company with 1-10 employees
Real User
Top 20
May 1, 2024
The pricing really depends on the specific requirements and underlying needs. For example, if the goal is to implement innovative solutions for the future or to improve productivity in decision-making and governance, then the cost might be justified. In a recent project, I achieved strong results using only 60% of the allocated budget. The client was impressed. They were curious about my approach, but I assured them it was simply my way of working.
Data Scientist at a tech services company with 1,001-5,000 employees
Real User
Top 5
Apr 29, 2024
If you consider the long-term aspect of any project, Azure OpenAI is a costly solution. However, the solution is cheap if you just want to see results or try some POC in the initial stages. This is because you don't need to spin up your instance; you can just consume things and see the results.
Azure OpenAI is a bit more expensive than other services. Many cloud services and Anthropic AI are cheaper than OpenAI. Many open-source models and API services are also relatively cheap to Azure OpenAI.
We've been a long-term Microsoft shop with an enterprise agreement, so that gives us some advantages. As an Azure-certified partner, we receive preferred pricing. However, AWS also has a very competitive solution. Ultimately, the best choice depends on your relationship with Microsoft. Azure OpenAI doesn't use a traditional licensing model. Instead, it's interaction-based, meaning transactional. The cost depends on the complexity of the business use case and the amount of computing used within OpenAI. It's important to engineer your solution carefully and implement controls. With any AI solution, there's a risk of operational expenses spiraling if the team doesn't put guardrails in place. Tools like Azure Synapse can help ensure usage stays within defined limits. This is true for any cloud technology – you need financial controls to prevent unexpected costs. I would rate the pricing a five out of ten. It's reasonably priced for now. It will likely become more affordable over time. As more providers offer support services within the Microsoft ecosystem and as user feedback shapes the technology, we'll see improvements. Because the technology is only about 20 months old, there's a lot of potential for growth.
The solution's pricing depends on the services you will deploy. The solution's ChatGPT service would have a different price depending on the number of tokens or requests. If you go for machine learning, it comes at a different price. Azure OpenAI doesn't have a fixed price. The pricing depends on the services you deploy, the amount of data you push, and the endpoint output. For example, if you increase the memory of a virtual machine, its cost will increase. Azure OpenAI will show you the cost based on the services you use.
The pricing is similar. It's a token-based system, so you pay per token used by the model. The cost itself is comparable to other service providers. It's not like one charges significantly more. It's mainly the cost of the language model and the tokens we use. So, from a pricing perspective, it's comparable, but we can configure token usage in OpenAI to potentially save costs. With Microsoft, we have an enterprise agreement covering various products, including Office 365, Azure Cloud, and Windows. With IBM and Watson XS, we have separate data insights pricing deals.
Head of IT at a manufacturing company with 1,001-5,000 employees
Real User
Dec 11, 2023
The cost is pretty high. So, hopefully, once the turbo is available, in the general availability, market problem, my cost will come down. But as of now, the cost is pretty high. Even by US standards, you would find it high.
The cost structure depends on the volume of data processed and the computational resources required. There might be additional costs for private cloud usage and security considerations.
Azure OpenAI integrates advanced language models with robust security for precise information extraction and task automation. Its seamless Azure integration and drag-and-drop interface simplify implementation and enhance accessibility.Azure OpenAI offers a comprehensive suite of features designed for efficient data processing and task automation. It provides high precision in extracting information and strong conversational capabilities, crucial for developing chatbots and customer support...
In terms of pricing for Azure OpenAI, I would rate it as average compared to Gemini. Currently, Gemini is becoming increasingly popular, which prompts leadership to consider a switch primarily due to pricing. I am also using other Azure products such as Azure Document Intelligence for OCR purposes, but it is quite costly, and the output format in HTML isn't particularly impressive.
In the past, the primary expense involved token limitations which constrained scaling. Recent iterations have increased token allowances, mitigating some challenges associated with concurrent user access at scale.
The pricing is very good for handling various kinds of jobs. While small jobs are manageable, more complex jobs require a higher model, which is a bit challenging.
The pricing is standard and similar to other cloud-based systems. There might be minor differences. Generally, the costs are comparable across platforms.
We started with monthly payments, but we plan to switch to yearly billing once we've stabilized our solution.
While the product meets our business requirements well, I consider it relatively expensive, especially for individual users like myself. However, as I become more accustomed to its benefits, it may become more affordable over time.
I rate the product pricing six out of ten.
Regarding pricing and licensing, it's a bit complex due to the minimum purchase requirement for PTO units. We're evaluating the best approach between PTE and pay-as-you-go models. Our organization is cautious about committing to PTE due to the fixed bandwidth reservation, while pay-as-you-go doesn't offer enough flexibility. We're discussing these matters with legal teams to ensure compliance and data security.
The pricing really depends on the specific requirements and underlying needs. For example, if the goal is to implement innovative solutions for the future or to improve productivity in decision-making and governance, then the cost might be justified. In a recent project, I achieved strong results using only 60% of the allocated budget. The client was impressed. They were curious about my approach, but I assured them it was simply my way of working.
If you consider the long-term aspect of any project, Azure OpenAI is a costly solution. However, the solution is cheap if you just want to see results or try some POC in the initial stages. This is because you don't need to spin up your instance; you can just consume things and see the results.
Azure OpenAI is a bit more expensive than other services. Many cloud services and Anthropic AI are cheaper than OpenAI. Many open-source models and API services are also relatively cheap to Azure OpenAI.
We've been a long-term Microsoft shop with an enterprise agreement, so that gives us some advantages. As an Azure-certified partner, we receive preferred pricing. However, AWS also has a very competitive solution. Ultimately, the best choice depends on your relationship with Microsoft. Azure OpenAI doesn't use a traditional licensing model. Instead, it's interaction-based, meaning transactional. The cost depends on the complexity of the business use case and the amount of computing used within OpenAI. It's important to engineer your solution carefully and implement controls. With any AI solution, there's a risk of operational expenses spiraling if the team doesn't put guardrails in place. Tools like Azure Synapse can help ensure usage stays within defined limits. This is true for any cloud technology – you need financial controls to prevent unexpected costs. I would rate the pricing a five out of ten. It's reasonably priced for now. It will likely become more affordable over time. As more providers offer support services within the Microsoft ecosystem and as user feedback shapes the technology, we'll see improvements. Because the technology is only about 20 months old, there's a lot of potential for growth.
The solution's pricing depends on the services you will deploy. The solution's ChatGPT service would have a different price depending on the number of tokens or requests. If you go for machine learning, it comes at a different price. Azure OpenAI doesn't have a fixed price. The pricing depends on the services you deploy, the amount of data you push, and the endpoint output. For example, if you increase the memory of a virtual machine, its cost will increase. Azure OpenAI will show you the cost based on the services you use.
The pricing is similar. It's a token-based system, so you pay per token used by the model. The cost itself is comparable to other service providers. It's not like one charges significantly more. It's mainly the cost of the language model and the tokens we use. So, from a pricing perspective, it's comparable, but we can configure token usage in OpenAI to potentially save costs. With Microsoft, we have an enterprise agreement covering various products, including Office 365, Azure Cloud, and Windows. With IBM and Watson XS, we have separate data insights pricing deals.
According to the negotiations taking place and the contract, there is a need to make either monthly or yearly payments to use the solution.
The cost is pretty high. So, hopefully, once the turbo is available, in the general availability, market problem, my cost will come down. But as of now, the cost is pretty high. Even by US standards, you would find it high.
The pricing is acceptable, and it's delivering good value for the results and outcomes we need.
The cost structure depends on the volume of data processed and the computational resources required. There might be additional costs for private cloud usage and security considerations.