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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:
"Explain the importance of sustainability in manufacturing, including environmental, social, and governance factors." (~20 tokens)
Optimized prompt:
"List ESG benefits of sustainable manufacturing." (~8 tokens, ~60% reduction)
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.
Start small: Default to GPT-3.5 or specialized fine-tuned models.
Engineer prompts carefully, ensuring concise and clear instructions.
Adopt caching and hybrid (RAG) methods early, especially for repeated or common tasks.
Implement active monitoring from day one to proactively control spend and avoid
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.
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