Using PTU vs pay as you go in production LLMs models

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Using PTU vs pay as you go in production LLMs models

louey Bnecheikh lehocine 145

Hi everyone

I have seen in a Microsoft video that pay as you go sku models are more for labs and dev purposes and not suited for production workloads, they advice to use PTU sku models in production instead, to guarantee stable throughput.

My question is, has someone faced some throughput issues with the pay as you go sku in production and moved to PTU instead ?

Thanks

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Answer 1

Hi ,

Thanks for reaching out to Microsoft Q&A.

Yes, what you heard in the microsoft video aligns with real-world experiences. Here is the situation, straight up:

Pay-As-You-Go SKUs - Dev/Test Only

Best suited for: Labs, testing, experimentation, low-volume interactive use.

No guaranteed throughput: These SKUs are on shared infrastructure, so performance can vary depending on regional demand.

Throttling: If too many users are hitting the same model endpoint (especially during peak times), you might get rate-limited or experience latency spikes.

Cost: Cheaper and flexible, but not stable under heavy or critical production loads.

PTU SKUs (Provisioned Throughput Units) - Production Grade

Dedicated capacity: You are reserving compute capacity upfront (via PTUs), so you get predictable and consistent performance.

Throughput guarantees: Ideal for production scenarios where you need SLA-level latency, concurrency, and reliability.

Higher cost, but stable: You pay for the reservation, regardless of usage—but in return, you do not get throttled or face sudden slowdowns.

Real-world use cases

Yes, there are teams who started with pay-as-you-go for prototypes and internal pilots and then ran into issues when they moved those same setups to handle production workloads:

Examples of issues: Latency spikes, token rate throttling, unpredictable timeouts.
Resolution: Most ended up switching to PTUs, especially for LLM chatbots, copilots, summarization services, or any API-integrated solution exposed to external users.

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Answer 2

Hi louey Bnecheikh lehocine,

Many users have encountered throughput challenges when using Pay-As-You-Go (PAYG) SKU models in production and have opted to switch to Provisioned Throughput Units (PTU) for better stability. While PAYG SKUs offer flexibility for development and testing, they lack dedicated resources, leading to performance inconsistencies such as fluctuating response times, rate limits, throttling, and latency spikes due to shared infrastructure. These limitations can severely impact real-time and high-volume applications, making PAYG less suitable for production workloads. PTU, on the other hand, ensures dedicated compute capacity, delivering consistent performance, lower latency, and predictable costs. It also provides better service-level agreements (SLAs) and priority access to resources, reducing the risk of service degradation during peak demand. Many organizations have moved from PAYG to PTU after facing performance bottlenecks, particularly for mission-critical LLM applications where reliability and scalability are essential. For best practices on optimizing PTU usage, refer to Microsoft's official guidance: Best Practice Guidance for PTU.

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Using PTU vs pay as you go in production LLMs models

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