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Chatbot Pricing: How Much Different Chatbots Cost in 2024?

Cem Dilmegani
Updated on Feb 22
5 min read

Chatbots decrease customer support expenses and maintain 24/7 availability, thus increasing revenues and customer satisfaction. And with the rise of generative AI chatbots, like ChatGPT, more businesses could adopt chatbots.

However, ambiguous chatbot pricing models can be an adoption barrier. This article will clarify chatbot pricing by explaining:

  1. Pricing plans from top chatbot vendors
  2. The different types of chatbot pricing plans 
  3. Costs involved in purchasing a chatbot platform 
  4. The best practices to pick the right chatbot platform for your needs

Pricing plans from top chatbot vendors

With +50 chatbot companies, we had to create a shortlist of the top ones. We first displayed the fixed, later usage-based vendors.

ProductPricing Model*Free TrialFree PlanText
Starter package price***
IBM Watson AssistantFixed1,000$140.00/month
Google DialogflowUsage-based85,000– Text: $0.007
– Voice: $0.001
$600 starting credit
Amazon LexUsage-based10,000– Text: $0.00075
– Voice: $0.004
Kore.aiUsage-based100,000– Text: $0.2
– Voice: $0.04
$500 starting credit
Azure Bot ServicesUsage-based10,000
$0.0005 for requests on Premium Channels (non-Microsoft apps & those w/o public API)

*A fixed pricing model doesn’t change with usage level, usage-based pricing model does. For example, a:

  • A fixed-pricing model costs $200 and handles 10,000 text requests. Price is fixed whether the chatbot is engaged in 1 conversation, or 10,000 conversations.
  • 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.

**Text requests are the number of times a chatbot engages in a conversation. If a Standard package offers 20,000 text requests in a month, the 20,001st query would cost extra.

***Standard package is the price of whatever package coming after the free plan.

What are the different types of chatbot pricing?

1. Free chatbots

1.1. Offered by proprietary chatbot companies

Most chatbot companies offer a free plan of their Standard package chatbot. These plans are suited to businesses that: 

  • Don’t have the budget to invest in chatbots 
  • Want to try the chatbot’s capabilities before investing in it

Free chatbots offer basic features, such as 3rd party application integration, basic analytics, and a ticketing system. But they have limitation, such as:

  • Limited number 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 

1.2. Offered by open-source vendors

The other type of free chatbots are offered by open-source chatbot vendors. Open-source chatbots offer more customizability than free versions of traditional vendors’ chatbots. But they may not provide extensive customer support and may require programming knowledge to develop the bot.

2. Standard packages 

Standard packages of chatbots charge a monthly fixed, or usage-based fee. The benefits tend to be a step-up from free packages, providing users with:

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. And because of case-by-case pricing, customers need to call the sales department to get a personalized quote. 

The Enterprise package is useful for large companies with scalable, complex automation and integration needs.  

In addition to offering more on features offered 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


  • Vendors may offer more packages, especially more variations of the standard package. We’ve generalized the categories for simplicity.
  • We didn’t discuss the cognitive capabilities – like NLP, ML, dialogue management, and context awareness – because usually all packages offer those. We focused on operational differences – like, the number of licenses, integration options, and multi-platform support.

Building your own custom chatbot

Companies may wish to develop their own custom chatbots instead of using a pre-built platform. 

Developing an in-house bot gives control over the platform, customization, and data. But it needs time, technical skills, and ongoing maintenance. 

To build your own 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 cost, such as paying for 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 milestones 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 
  • Miscellaneous: 
    • Legal & compliance: For ensuring that chatbot meets data protection and compliance standards 
    • Marketing and launch: Promotional activities to introduce the bot to users 
  • Training: 
    • Staff training: To train staff to manage, use, and monitor the bot

How to choose the chatbot vendor suitable to your needs? 

To ensure that you only pay for what you need – whether you’re picking a chatbot off-the-rack, or building your own – consider the following best practices. If you still had 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 (i.e., sales, customer service, marketing, etc) Its conversational tone 
  • 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. This is usually determined by the use case. 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. We looked at the pricing of vendors on the first page of our data-driven chatbot companies, and figured that: 

  • Standard chatbots cost, on average, ~$800/month 
  • Enterprise chatbots cost, on average, ~$1,200/month 
  • Personalized chatbot development cost varies case by case with respect to the factors of production. For example, a chatbot developer’s salary is different in the US than it is in India. Therefore, we can’t provide an estimate. But you can expect it to cost more than an Enterprise package.

3. Identify & evaluate vendors 

To create a shortlist amongst all chatbot platforms, focus on vendors with the highest employee count as an indication 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 of the chatbot 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.

Further readings

If you need more information to find the best chatbot companies, you can reach us:

Find the Right Vendors

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related 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.

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