As technology continues to evolve at a breathtaking pace, the business models that support it must also adapt. In the world of artificial intelligence (AI), the conversation around pricing is becoming increasingly nuanced. It's projected that spending on AI systems will surpass $300 billion in the next few years. Yet, a question that persists among companies is - how much should AI software cost?
In 2023, the cost of AI software can range anywhere from $0 to over $300,000, reflecting the broad spectrum of AI applications and services available. A variety of factors can influence this cost, including the type of AI required, whether the solution is custom or pre-built, the specific features it offers, and how it's managed.
Custom-built solutions, for instance, often come at a higher price than pre-made ones, and in-house management can be more costly than outsourced management due to associated salaries and other hiring costs
Traditionally, pricing models for software, including AI, have been largely seat-based. In this model, businesses pay for each user or "seat" that has access to the software. However, as AI becomes more integrated into business processes and systems, this model is starting to show its limitations.
Limitations of Seat-Based AI Pricing Models
Seat-based pricing, a traditional model where the cost is linked directly to the number of users, is becoming less and less viable in the AI landscape. For instance, a company may have only a handful of users who interact directly with the AI software, but the benefit of the AI system could extend to the entire organization. In such scenarios, seat-based pricing can be seen as restrictive and not reflective of the actual value derived from the AI system.
Additionally, seat-based pricing does not account for the scale and complexity of tasks the AI performs. AI software that processes large amounts of data makes complex predictions, or uses extensive computational resources would be undervalued if priced solely based on the number of users. Also, seat-based pricing is a static pricing model.
With AI training costs and maintenance, the business cannot really scale if they started to implement seat-based pricing.
Understanding the Costs of AI
In 2023, the cost spectrum for AI software is vast, with prices ranging anywhere from 0 to an excess of $300,000. This price variability primarily depends on whether the solution is sourced from a third-party vendor or created by a dedicated in-house team.
The financial investment for custom-built AI solutions can start as low as $6,000 and scale up to over $300,000, encompassing development and deployment stages.
The cost of AI is influenced by various factors, including:
1. AI Type
The type of AI software, such as chatbots, analysis systems, or virtual assistants, can significantly impact the cost​​.
- Chatbots: These are AI-based systems designed to automate and enhance customer support interactions. Custom-built chatbot prices start at $6000, whereas pre-built ones can cost up to $40,000 per year​.
- Analysis Systems: These systems help businesses interpret and take action from large amounts of data, assisting in data-driven decision-making for sales and marketing initiatives. The cost of these systems can reach around $600 per page of analysis​​.
- Virtual Assistants: AI-powered virtual assistants can help businesses complete general tasks while saving time. The costs of custom-built virtual assistants can vary greatly depending on the specific requirements of the business​​.
2. Project Type
Costs vary depending on whether companies opt for pre-built solutions or custom-built ones. For instance, developing a custom chatbot can start at $6000 and go up to almost $15,000, while pre-built options like Drift or TARS can cost between $99 to $1500 per month​​.
3. AI Features
The specific features desired in an AI solution also play a role in the overall cost. Features like data format, data storage, data structure, data processing speed, the minimum accuracy rate for predictions, data visualization, and dashboard requirements can all affect the price​​.
4. AI Management
Whether AI is managed in-house or outsourced can significantly influence the cost. In-house management includes salaries, benefits, and other hiring costs for maintaining an in-house development and data scientist team.
Outsourced management usually costs less, as businesses don’t have in-house hiring costs, and instead pay a monthly rate or one-time fee to a dedicated partner​​.
Why Usage-Based Pricing for AI is the way forward?
The inherent limitations of seat-based pricing have given rise to new pricing models. One of the most promising of these is usage-based pricing, which links the cost of AI software to its actual use. The principle behind usage-based pricing is simple: the more you use it, the more you pay. This model provides a more accurate reflection of the value organizations derive from their AI systems.
Usage-based pricing could consider a number of factors, such as the volume of data processed by the AI, the complexity of the tasks it performs, and the computational resources it consumes.
For instance, a chatbot handling a high volume of customer interactions would be priced differently from a predictive analytics system processing vast amounts of data to provide detailed market forecasts.
Some AI companies have started adopting usage-based pricing. For example, DataRobot, a leading AI platform, has implemented a usage-based pricing model that charges customers based on the number of predictions made by their AI models.
Usage-based pricing is seen as more fair and transparent, as it charges customers based on the actual usage of the AI service rather than a fixed fee. This pricing model also motivates AI providers to continually improve their services and ensure optimal performance, as higher usage levels would translate to higher revenues.
However, while usage-based pricing offers several advantages, it also brings its own set of challenges and considerations, both for AI providers and their customers. AI providers need to have robust systems in place to accurately track usage and bill customers accordingly.
Customers, on the other hand, need to have a clear understanding of their AI usage patterns to predict costs and ensure that they are getting good value for their money.
But these challenges are easily overcome. Platforms like Togai help AI companies to experiment with any pricing model under the sun! Togai is a modern, usage-based pricing platform that enables businesses to implement pricing models that reflect the value they deliver.
It allows businesses to change pricing, offer promotions, and experiment with new pricing models without the need for extensive engineering. This allows teams to focus on product innovation, thereby providing a win-win situation for both the provider and the consumer.
Looking ahead, usage-based pricing is likely to become more prevalent as AI continues to proliferate across various sectors and businesses. As more AI providers adopt this model, it will be interesting to see how it shapes the AI market and influences customer behavior. But one thing is clear: in an era where value is king, usage-based pricing is set to play a key role in the AI pricing landscape.
