Sentinel AI vs hosted GPT — when to bring inference in-house

Sentinel AI vs Hosted GPT — When to Bring Inference In-House

As AI models become increasingly prominent in enterprise applications, the question of where to run inference workloads has become a critical decision point for CTOs, platform engineers, and enterprise architects. Two popular options are Sentinel AI and hosted GPT, each with its own strengths and weaknesses. In this article, we will explore the key factors to consider when deciding between these two approaches, including latency, data sensitivity, cost per million tokens, fine-tuning needs, and regulatory exposure.

Latency is a crucial consideration for many applications, particularly those that require real-time or near-real-time processing. Hosted GPT solutions typically involve sending requests to a cloud-based API, which can introduce latency due to network transmission times and queueing delays. In contrast, running inference in-house with Sentinel AI can reduce latency to near zero, as the model is executed directly on local hardware.

Latency Comparison

• Hosted GPT: 50-200ms per request
• Sentinel AI (in-house): <1ms per request

Data sensitivity is another important factor, as many organizations handle sensitive or confidential information that cannot be sent to cloud-based services. In these cases, running inference in-house with Sentinel AI is the only viable option, as it allows the organization to maintain complete control over the data and ensure that it is not transmitted to external parties.

Data Sensitivity Considerations

• Hosted GPT: may not be suitable for sensitive or confidential data
• Sentinel AI (in-house): provides complete control over data and ensures confidentiality

Cost is also a significant consideration, particularly for large-scale applications that involve processing millions or billions of tokens. Hosted GPT solutions typically charge per token or per request, which can result in significant costs for high-volume applications. In contrast, running inference in-house with Sentinel AI requires an upfront investment in hardware and software, but can provide significant cost savings in the long run.

Cost Comparison

Assuming a cost of $0.01 per token for hosted GPT and an upfront investment of $10,000 for Sentinel AI hardware and software, the cost crossover point can be calculated as follows:

Cost crossover point = Upfront investment / (Cost per token - Cost per token (in-house))

Using this formula, we can calculate the cost crossover point for a given application. For example, if the application processes 1 million tokens per day, the cost crossover point would be:

Cost crossover point = $10,000 / ($0.01 - $0.001) = 1,000,000,000 tokens

This means that if the application processes more than 1 billion tokens, running inference in-house with Sentinel AI would be more cost-effective than using hosted GPT.

Cost Crossover Point

• Hosted GPT: $0.01 per token
• Sentinel AI (in-house): $0.001 per token (assuming $10,000 upfront investment)
• Cost crossover point: 1,000,000,000 tokens

Fine-tuning needs are also an important consideration, as many applications require customized models that are tailored to specific use cases or industries. Hosted GPT solutions typically provide pre-trained models that can be fine-tuned to some extent, but may not provide the level of customization required for certain applications. In contrast, running inference in-house with Sentinel AI provides complete control over the model and allows for extensive fine-tuning and customization.

Fine-Tuning Considerations

• Hosted GPT: limited fine-tuning options
• Sentinel AI (in-house): complete control over model and extensive fine-tuning options

Regulatory exposure is also a critical consideration, particularly for organizations that operate in heavily regulated industries such as finance or healthcare. Hosted GPT solutions may not provide the level of regulatory compliance required for these industries, as they may not meet specific requirements for data handling and processing. In contrast, running inference in-house with Sentinel AI allows the organization to maintain complete control over the data and ensure that it is processed in compliance with relevant regulations.

Regulatory Exposure Considerations

• Hosted GPT: may not meet regulatory requirements for data handling and processing
• Sentinel AI (in-house): provides complete control over data and ensures regulatory compliance

QubGPU is a powerful tool for running inference workloads in-house, providing a high-performance and cost-effective solution for organizations that require low latency and high throughput. By leveraging QubGPU, organizations can run Sentinel AI models at scale and achieve significant cost savings compared to hosted GPT solutions.

Decision Matrix

The decision to run inference in-house with Sentinel AI or use hosted GPT depends on several factors, including latency, data sensitivity, cost per million tokens, fine-tuning needs, and regulatory exposure. The following decision matrix provides a summary of the key considerations: