Chatbots decrease customer support expenses while maintaining 24/7 availability, thus increasing revenues and customer saftisfaction. And with the rise of generative AI chatbots, like ChatGPT, more businesses could adopt chatbots.
However, ambiguous chatbot pricing models can be a barrier to adoption. We clarified chatbot pricing by explaining pricing plans from vendors, types of chatbot pricing, costs of purchasing a platform, and best practices for selecting the right one.
Pricing plans from top chatbot vendors
We have developed a calculator that shows your pricing options for the top chatbot platforms we selected based on market presence metrics, such as the number of reviews. The pricing is simplified based on the average number of message requests your company receives each month.
* Intercom does not specify any text request limits on its website; to ensure accuracy, please contact sales.
**The prices are for deepseek-chat. The input tokens distinguish between cache hits and misses; we have provided the pricing for hits (the requests the chatbot can fulfill). We have assumed that the average input and output tokens are 20.
Understanding large language model pricing can be challenging. We recommend checking LLM pricing to understand how tokens work properly.
Products are sorted alphabetically in 2 groups: First, products with fixed pricing, and later, those with usage-based pricing.
A fixed pricing model doesn’t change with usage level, whereas a usage-based pricing model does. For example:
- A fixed-pricing model costs $200 and handles 10,000 text requests. The price is fixed, whether the chatbot is engaged in 1 or 10,000 conversations. However, some fixed plans limit the number of agents that can reach the platform.
- A usage-based pricing model charges $0.5 per conversation. If a chatbot is used only once, the user pays $0.5. If it’s used 10,000 times, they pay $5,000.
Each product on our table, except for Drift, provides a free trial. Drift also doesn’t share any pricing information publicly, so you must contact sales to employ it.
Fixed chatbot pricing models explained
Fixed models can offer different features within themselves according to the price plan:
* Drift does not offer multiple prices for different plans with various features.
What are the different types of chatbot pricing?
1. Free chatbots
Offered by proprietary chatbot companies
Most chatbot companies offer a free plan for their standard package chatbot. Free chatbots offer basic features, such as 3rd party application integration, basic analytics, and a ticketing system. But they have limitations, such as:
- Limited number of available text requests (i.e., to how many queries the bot can answer in a month)
- Low number of user licenses
- Limited chatbot integration capabilities
Free plans are ideal for businesses that lack the budget to invest in chatbots or want to explore the chatbot’s capabilities before making an investment.
Offered by open-source vendors
The other type of free chatbots is offered by open-source chatbot vendors. Open-source chatbots offer more customizability than free versions of traditional vendors’ chatbots. However, they may not provide extensive customer support and may require programming knowledge to develop the bot.
2. Advanced packages
Standard packages of chatbots charge a monthly fixed or usage-based fee. The benefits tend to be a step-up from free packages, generally providing users with:
- Multi-channel support
- Voice add-on
- More integration possibilities
- Longer session timeouts
- Longer analytics data retention
- Higher number of text requests
Standard packages are most suitable for small to medium-sized businesses (SMBs) that have outgrown the limitations of free plans, but do not yet require the Enterprise package.
3. Enterprise packages
Enterprise packages offer the most features and are the most expensive, but the pricing is flexible. The user can choose and pay only for what they need, such as the number of customized integrations. Because of case-by-case pricing, customers need to call the sales department to get a personalized quote.
In addition to offering more features in the Standard package (such as deployment on multiple channels or handling multiple languages), Enterprise chatbots:
- Are capable of batch processing
- Are overseen by a dedicated account manager to ensure optimum usage and support
- Offer advanced analytics
- Offer custom integrations
The Enterprise packages are the most suitable for large companies with scalable, complex automation and integration needs.
Building your custom chatbot
Companies may wish to develop their own custom chatbots instead of using a pre-built platform.
Developing an in-house bot gives you control over the platform, customization, and data. However, it requires time, technical skills, and ongoing maintenance.
To build your custom chatbot, you’d roughly have to pay for the following factors:
- Development and deployment cost:
- Platform licenses: If you’re using a third-party development platform, like Dialogflow or Microsoft Bot Framework, you’d need to pay for a license
- Programming: Custom development and integration costs, such as paying for the WhatsApp API
- Hosting: Cloud-based or on-premise server costs
- Human resources:
- Development team: Salaries/contracts for chatbot developers
- Project managers: For overseeing the development timeline and milestone achievements
- UI/UX designers: To design the chat interface and UX
- Data costs:
- Data collection: Acquiring and purchasing data for bot training
- Data storage: For storing data in a secure environment
- Maintenance:
- Updates & upgrades: Regular improvements to keep the bot up-to-date
- Monitoring & analytics Tools and services for monitoring chatbot performance and gathering insights
- Training:
- Staff training: To train staff to manage, use, and monitor the bot
- Other:
- Legal & compliance: For ensuring that the chatbot meets data protection and compliance standards
- Marketing and launch: Promotional activities to introduce the bot to users
How to choose a chatbot vendor suitable for your needs?
To ensure that you only pay for what you need – whether picking a chatbot off the rack or building your own – consider the following best practices. If you still have questions, reach out to us:
Find the Right Vendors
1. Define the objective
Define the objective of your chatbot, including:
- The channels it should be deployed on and the number of users it should reach
- The application area’s conversational tone (i.e., sales, customer service, marketing, etc)
- An integration that goes beyond API connections and connects the chatbot to the CRM, ERP, payment gateways, and more
- Whether or not it’d need advanced NLP, ML, or generative AI capabilities. The use case usually determines this. For example, a lead generation chatbot needs advanced AI functionality, whereas an FAQ chatbot can be rule-based.
- Customization level
2. Estimate the budget
Compare your budget with the total cost of ownership of using a pre-built or customized chatbot. Some conversational AI plans might not fit your business interests. Personalized chatbot development cost varies case by case, depending on production factors.
3. Identify & evaluate vendors
To create a shortlist amongst all chatbot platforms, focus on vendors with the highest employee count as an indicator of market success and overreach. Read users’ reviews on aggregators’ websites like G2 and customers’ case studies.
Especially if you are in a data-sensitive industry, like healthcare, see if they have developed chatbots for that sector before. Take advantage of vendors’ free chatbots and demos to see if they suit your needs.
4. Run a PoC
Running a Proof of Concept (PoC) for a chatbot involves creating a scaled-down, operational version to test its feasibility, functionality, and effectiveness before full-scale development and deployment.
The PoC helps businesses validate assumptions, evaluate technical requirements, and assess user engagement and satisfaction, thereby reducing risks and offering insights for improvement.
FAQ
Further readings
- Conversational Commerce Platforms Use Cases
- IBM Watson Competitors
- Yellow.ai Alternatives & Competitors Detailed Guide
- Conversational AI/Chatbot Challenges For Users
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
Be the first to comment
Your email address will not be published. All fields are required.