Evaluating the Costs and Strategic Implications of Open-Source vs Commercial Large Language Models

Artificial intelligence has become an indispensable pillar of digital transformation. If you are a technology-driven company, leveraging large language models (LLMs) can be invaluable for functions across all areas of your business. However, the costs and implications of deploying open source LLMs versus commercial solutions like OpenAI require careful evaluation. This article provides an in-depth analysis to inform your strategy.

Comparing the Cost Structures

Summary: The cost structure of an open source LLM implementation is heavily weighted towards initial infrastructure and talent investments. However, ongoing costs are relatively minimal excluding maintenance and incremental enhancements. Commercial LLM APIs like OpenAI on the other hand have negligible startup costs but require continuous spend proportional to usage volumes.

Open Source LLM Cost Considerations

Infrastructure

Specialized high-performance GPU servers for training and inference could cost over $100,000 upfront for on-premises deployment. Cloud computing provides more flexibility but also incurs ongoing costs of $10,000+ per month depending on usage. This is because a single machine to run a Llama 2 costs at least $5 per hour. Considering at least 3 environments to manage AI deployment,

Talent

Data scientists and ML engineers with expertise in neural networks and NLP are crucial for development, optimization and maintenance of open source LLMs. Annual compensation of $150,000+ per head should be budgeted for such scarce talent, irrespective of the location of the talent.

IT Infrastructure

Backend engineering and DevOps skills needed to build pipelines, REST APIs, deploy models and ensure high availability and scalability of infrastructure for production deployments. Plan for at least $100,000/year for this specialized talent.

Data Annotation

Large volumes of training data may need human annotation which can cost upwards of $100/hour for quality labeling from professional annotation firms. This could result in upfront bills of $100,000+ for annotation of sizable datasets. If you are using in-house employees for data annotation, you might be able to save on the costs for this part, but you will still need to figure out the best way to manage the data annotations by leveraging a platform that can help with it.

Energy and Cooling

Just in case, you want to run GPUs on premises for model training and inference, account for electricity bills as well. It can easily be over $10,000- $50,000/month. Additional cooling needed for heat management may necessitate HVAC investments of $20,000+.

Commercial API Cost Considerations

Usage-based pricing

OpenAI charges per API call so costs scale linearly with usage. It is not uncommon to spend about 200 tokens in prompting and about 800 tokens in completions esp. when you are iteratively conversing with the OpenAI endpoint.

As you can notice, pricing increases significantly once you have conversations at scale. However, if your business model supports such a linear pricing model, this could in fact be just a cost line item. For instance, if the cost to resolve a customer's request right now is $1, but because of using AI, this is dropping to 10c even with the best 32K context of GPT-4, it is enough to justify that cost.

Also, there is reason to believe that once you hit scale, you could get volume discounts with OpenAI or Azure OpenAI.

Things to consider.

While there are other models such as Anthropic’s Claude, or Google’s PaLM, they all always follow the usage-based pricing model. The biggest benefit of going with a usage-based pricing model is that you only pay for what you use. This is akin to spinning up Serverless compute which you are only charged for when you use it.

Unlike the upfront investment required into private models, and fine-tuning, this helps you plan better. This is also best if you’re still experimenting with AI and want to figure out the real use cases that will drive traction in your product.

There’s also the OpenAI fine-tuning -

Training with about 1000 conversations, each with 1000 tokens will cost a one-time fee of $8, and every time you conduct a conversation, it’ll cost you about 1.5c. This might be super cost effective compared to the GPT-4 models, since it is more relevant and trained on your own company data.

Request optimization

Another way to optimize your costs is by caching responses where applicable reducing API calls. This could be very effective in case there are common customer queries that are frequently asked.

Evaluating the Tradeoffs

Overall, factor in these other key considerations relative to your organization's capabilities when weighing the open source versus commercial LLM options:

In-house AI expertise

- Do you have data scientists with deep learning experience to customize, optimize and maintain open source LLMs? If not, the learning curve could be steep.

Data privacy policies

OpenAI's vague data privacy policies may be unsuitable for applications dealing with sensitive data like healthcare. Self-hosted open source gives full control and transparency.

Integration effort

Commercial APIs reduce integration workload substantially compared to installing and managing complex deep learning infrastructure.

Model customization needs

Open source offers full customizability like training on proprietary datasets. But basic capabilities may suffice for many usage scenarios.

Cost of delays

Long setup, development, training and maintenance timelines for open source could severely impede delivery of AI initiatives. The business costs of delays should be quantified.

Growth trajectories

Usage based pricing means commercial API costs escalate linearly with surges in usage, making longer term forecasts prudent.

Total cost of ownership

Weigh capital expenditures versus operational expenses between both approaches for a fair lifetime cost comparison.

Best Practices for Cost-Optimized Deployment

Adopting the following practices can help optimize costs for both open source and commercial LLM deployments:

1. Start with a well-scoped proof of concept to allow more accurate cost projections.
2. Leverage cloud computing so infra demands dynamically adjust to workloads.
3. Understand what the right data is to be used for training, and the approaches to deliver on the use case.
4. Evaluate if a smaller pretrained model can fulfill initial requirements instead of deploying massive models directly in case you are going with open-source models.
5. Utilize batching and caching techniques to optimize and reduce API call volumes irrespective of the approach.
6. Consider staged rollout and iterative enhancements to get clarity into costs and quality.
7. Continuously monitor usage and spending to minimized unnecessary expenditures.

Key Takeaways for Senior Leaders

• Weigh customization needs, integration complexity, data privacy and in-house capabilities when choosing between open source and commercial LLMs.
• Factor in the substantial upfront infrastructure and talent investments required for open source LLMs.
• Account for cost growth with scaling usage when projecting commercial LLM costs.
• Adopt a PoC approach and implement cost optimization best practices, regardless of which LLM implementation option is chosen.
• Avoid overprovisioning during early stages - start small and scale up infrastructure, functionality and costs iteratively.