These days, many are building generative AI apps or retrieval-augmented generation (RAG) apps, but figuring out the actual production costs can be quite tricky. It's also important to compare the pricing of different language models to find the best fit for your use case, especially as you scale.
To simplify this process, I developed a tool that lets you compare various LLM models based on tokens, vector DB, and embedding helping you determine which one suits your needs best.
Use Cases
Launching a new generative AI or RAG app involves significant financial investment. The Gen AI Budget Planning Tool allows project managers to compare the costs of various language models before initiating development. By analyzing costs related to tokens, vector DB, and embedding, project managers can make informed decisions that align with their budget constraints, ensuring a cost-effective start to their AI projects.
As AI applications grow, so do their operational costs. The Gen AI Budget Planning Tool assists businesses in scaling their generative AI or RAG apps by providing detailed cost comparisons across different language models. This helps in identifying the most cost-effective models for large-scale operations, ensuring that businesses can scale efficiently without overspending.
Educators and trainers can use the Gen AI Budget Planning Tool to teach students and developers about the financial aspects of AI development. By integrating this tool into their curriculum, educators can provide hands-on experience in budgeting for AI projects, emphasizing the importance of cost analysis in selecting suitable language models for various use cases.