LLM economics: How to avoid costly pitfalls

Large Language Models (LLMs) like GPT-4 are advanced AI systems designed to process and generate human-like text, transforming how businesses leverage AI.

GPT-4’s pricing model (32k context) charges $0.06 per 1,000 input tokens and $0.12 per 1,000 output tokens, which makes it a scalable option for businesses. However, it can become expensive very quickly when it comes to production environments.

New models cross-reference all bits of data, or tokens, that deal with other tokens in order to both quantify and understand the context behind each pair. The result? Quadratic behavior of algorithms that becomes more and more expensive as the number of tokens increases.

And scaling isn’t linear; costs increase quadratically when it comes to the length of sequences. If you need to scale up to handle text that’s 10x longer, the cost will go up 10,000 times, and so on.

This can be a significant setback for scaling projects; the hidden cost of AI impacts sustainability, resources, and requirements. This lack of insight can lead to businesses overspending or inefficiently allocating resources.

Where costs lie

Let’s look deeper into tokens, per-token pricing, and how everything works. 

Tokens are the smallest unit of text processed by models – something simple like an exclamation mark can be a token. Input tokens are used whenever you enter anything into the LLM query box, and output tokens are used when the LLM answers your query.

On average, 740 words are equivalent to around 1,000 tokens.

Inference costs

Here’s an illustrative example of how costs can exponentially grow:

Input tokens: $0.50 per million tokens

Output tokens: $1.50 per million tokens

As LLM adoption expands, the user numbers grow exponentially and not linearly. Users engage more frequently with the LLM, and the number of prompts per user increases. The number of total tokens increases significantly as a result of increased users, prompts, and token usage, leading to costs multiplying monthly.

What does it mean for businesses?

Anticipating exponential cost growth becomes essential. For example, you’ll need to forecast token usage and implement techniques to minimize token consumption through prompt engineering. It’s also vital to keep monitoring usage trends closely in order to avoid unexpected cost spikes.

Latency versus efficiency tradeoff

Let’s look into GPT-4 vs. GPT-3.5 pricing and performance comparison.

Latency refers to how quickly models respond; a faster response leads to better user experiences, especially when it comes to real-time applications. In this case, GPT-3.5 Turbo offers lower latency because it has simpler computational requirements. GPT-4 standard models have higher latency due to processing more data and using deeper computations, which is the tradeoff for more complex and accurate responses.

Efficiency is the cost-effectiveness and accuracy of the responses you receive from the LLMs. The higher the efficiency, the more value per dollar you get. GPT-3.5 Turbo models are extremely cost-efficient, offering quick responses at low cost, which is ideal for scaling up user interactions.

GPT-4 models deliver better accuracy, reasoning, and context awareness at much higher costs, making them less efficient when it comes to price but more efficient for complexity. GPT-4 Turbo is a more balanced offering; it’s more affordable than GPT-4, but it offers better quality responses than GPT-3.5 Turbo.

To put it simply, you have to balance latency, complexity, accuracy, and cost based on your specific business needs.

High-volume and simple queries: GPT-3.5 Turbo (4K or 16K). 

Perfect for chatbots, FAQ automation, and simple interactions.

Complex but high-accuracy tasks: GPT-4 (8K or 32K).

Best for sensitive tasks requiring accuracy, reasoning, or high-level understanding.

Balanced use-cases: GPT-4 Turbo (128K).

Ideal where higher quality than GPT-3.5 is needed, but budgets and response times still matter.

Experimentation and iteration

Trial-and-error prompt adjustments can take multiple iterations and experiments. Each of these iterations consumes both input and output tokens, which leads to increased costs in LLMs like GPT-4. If not monitored closely, incremental experimentation will very quickly accumulate costs.

You can fine-tune models to improve the responses; this requires extensive testing and repeated training cycles. These fine-tuning iterations require significant token usage and data processing, which increases costs and overhead.

The more powerful the model, like GPT-4 and GPT-4 Turbo, the more these hidden expenses multiply because of higher token rates.

(Example assuming average prompt/response token counts.)

Strategic recommendations to ensure efficient experimentation without adding overhead or wasting resources:

• Start with cheaper models (e.g., GPT-3.5 Turbo) for experimentation and baseline prompt testing.
• Progressively upgrade to higher-quality models (GPT-4) once basic prompts are validated.
• Optimize experiments: Establish clear metrics and avoid redundant iterations.

Vendor pricing and lock-in risks

First, let’s have a look at some of the more popular LLM providers and their pricing:

OpenAI

Anthropic

Google

Committing to just one vendor means you have reduced negotiation leverage, which can lead to future price hikes. Limited flexibility increases costs when you switch providers, considering prompts, code, and workflow dependencies. Hidden overheads like fine-tuning experiments when migrating vendors can increase expenses even more.

When thinking strategically, businesses should keep flexibility in mind and consider a multi-vendor strategy. Make sure to keep monitoring evolving prices to avoid costly lock-ins.

How companies can save on costs

Tasks like FAQ automation, routine queries, and simple conversational interactions don’t need large-scale and expensive models. You can use cheaper and smaller models like GPT-3.5 Turbo or a fine-tuned open-source model.

LLaMA or Mistral are great fine-tuned smaller open-source model choices for document classification, service automation, or summarization. GPT-4, for example, should be saved for high accuracy and high-value tasks that’ll justify incurring higher costs.

Prompt engineering directly affects token consumption, as inefficient prompts will use more tokens and increase costs. Keep your prompts concise by removing unnecessary information; instead, structure your prompts into templates or bullet points to help models respond with clearer and shorter outputs.

You can also break up complex tasks into smaller and sequential prompts to reduce the total token usage.

Example:

Original prompt:

Optimized prompt:

To further reduce costs, you can use caching and embedding-based retrieval methods (Retrieval-Augmented Generation, or RAG). Should the same prompt show up again, you can offer a cached response without needing another API call. 

For new queries, you can store data embeddings in databases. You can retrieve relevant embeddings before passing only the relevant context to the LLM, which minimizes prompt length and token usage.

Lastly, you can actively monitor costs. It’s easy to inadvertently overspend when you don’t have the proper visibility into token usage and expenses. For example, you can implement dashboards to track real-time token usage by model. You can also set a spending threshold alert to avoid going over budget. Regular model efficiency and prompt evaluations can also present opportunities to downgrade models to cheaper versions.

The smart way to manage LLM costs

After implementing strategies like smaller task-specific models, prompt engineering, active monitoring, and caching, teams often find that a systematic approach to operationalize these approaches at scale is needed. 

The manual operation of model choices, prompts, real-time monitoring, and more can very easily become both complex and resource-intensive for businesses. This is where you’ll find the need for a cohesive layer to orchestrate your AI workflows.

Vellum streamlines iteration, experimentation, and deployment. As an alternative to manually optimizing each component, Vellum will help your teams choose the appropriate models, manage prompts, and fine-tune solutions in one integrated solution.

It’s a central hub that allows you to operationalize cost-saving strategies without increasing costs or complexity. 

Here’s how Vellum helps:

Prompt optimization

You’ll have a structured, test-driven environment to effectively refine prompts, including a side-by-side comparison across multiple models, providers, and parameters. This helps your teams identify the best prompt configurations quickly.

Vellum significantly reduces the cost of iterative experimentation and complexity by offering built-in version control. This ensures that your prompt improvements are efficient, continuous, and impactful.

There’s no need to keep your prompts on Notion, Google Sheets, or in your codebase; have them in a single place for seamless team collaboration.

Model comparison and selection

You can compare LLM models objectively by running side-by-side systematic tests with clearly defined metrics. Model evaluation across the multiple existing providers and parameters is made simpler. 

Businesses have transparent and measurable insights into performance and costs, which helps to accurately select the models with the best balance of quality and cost-effectiveness. Vellum allows you to:

• Run multiple models side-by-side to clearly show the differences in quality, cost, and response speed.
• Measure key metrics objectively, such as accuracy, relevance, latency, and token usage.
• Quantify cost-effectiveness by identifying which models achieve similar or better outputs at lower costs.
• Track experiment history, which leads to informed, data-driven decisions rather than subjective judgments.

Real-time cost tracking

Enjoy detailed and granular insights into LLM spending through tracking usage across the different models, projects, and teams. You’ll be able to precisely monitor the prompts and workflows that drive the highest token consumption and highlight inefficiencies.

This transparent visualization allows you to make smarter decisions; teams can adjust usage patterns proactively and optimize resource allocation to reduce overall AI-related expenses. You’ll have insights through intuitive dashboards and real-time analytics in one simple location.

Seamless model switching

Avoid vendor lock-in risks by choosing the most cost-effective models; Vellum gives you insights into the evolving market conditions and performance benchmarks. This flexible and interoperable platform allows you to keep evaluating and switching seamlessly between different LLM providers like Anthropic, OpenAI, and others.

Base your decision-making on real-time model accuracy, pricing data, overall value, and response latency. You won’t be tied to a single vendor’s pricing structure or performance limitations; you’ll quickly adapt to leverage the most efficient and capable models, optimizing costs as the market dynamics change. 

Final thoughts: Smarter AI spending with Vellum

The exponential increase in token costs that arise with the business scaling of LLMs can often become a significant challenge. For example, while GPT-3.5 Turbo offers cost-effective solutions for simpler tasks, GPT-4’s higher accuracy and context-awareness often come at higher expenses and complexity.

Experimentation also drives up costs; repeated fine-tuning and prompt adjustments are further compounded by vendor lock-in potential. This limits competitive pricing advantages and reduces flexibility.

Vellum comprehensively addresses these challenges, offering a centralized and efficient platform that allows you to operationalize strategic cost management:

• Prompt optimization. Quickly refining prompts through structured, test-driven experimentation significantly cuts token usage and costs.
• Objective model comparison. Evaluate multiple models side-by-side, making informed decisions based on cost-effectiveness, performance, and accuracy.
• Real-time cost visibility. Get precise insights into your spending patterns, immediately highlighting inefficiencies and enabling proactive cost control.
• Dynamic vendor selection. Easily compare and switch between vendors and models, ensuring flexibility and avoiding costly lock-ins.
• Scalable management. Simplify complex AI workflows with built-in collaboration tools and version control, reducing operational overhead.

With Vellum, businesses can confidently navigate the complexities of LLM spending, turning potential cost burdens into strategic advantages for more thoughtful, sustainable, and scalable AI adoption.