Intelligent process automation solutions like these have two components:

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Automation tool. You can choose a SaaS no-code solution like Make.com or a robust enterprise solution like UiPath. Make.com has a subscription-based pricing ($9-$29 per month for 10,000 ops) while UiPath operates on a per-bot pricing ($1000 - $10,000 annually per bot).
AI models for specialized tasks: Automation tools can call on AI components to process specialized tasks such as document parsing, intent classification, etc. This is similar to having a custom LLM, priced per unit processed (i.e, per document or API call).
How is pricing determined for AI?
AI cost isn’t just about the model you choose. It’s about how often it runs, how much data it needs, and how well it scales.

Let’s look into specific factors that affect costs country wise email marketing list across AI models.

how is pricing determined for ai?

1. Data Costs
AI runs on data. The quality of your data determines how accurate your model will be. If you’re not careful with the data you supply, AI can spit out nonsense into customer communications or include its own biases.

I’ve seen internal company data get messy. Valuable data gets stored across multiple CRMs, cloud solutions, and internal tools. The result? Inconsistent, redundant, and often unreliable data.

So, while raw data is cheap to acquire, getting clean, labeled data can become expensive. Data processing involves multiple steps: collection, cleaning, labeling, and structuring into AI-friendly formats. Each step is typically charged based on data volume or hours spent. For instance, CVAT, a data cleaning platform, estimates the cost of annotating 100k images at $300k.

If your internal data isn't sufficient, you can supplement it with external datasets from providers like Bloomberg or data marketplaces like Kaggle.

Once your data is ready, the next step is to store it. Depending on data volume, cloud data storage instances can cost anywhere between $1k-$10k a month. Your cloud storage should be able to scale with you as you collect and process new data.

Data governance is another factor to consider. I recommend budgeting around 10-20% of your costs to go towards data security and compliance with laws like GDPR.