20 Chatbot Companies To Deploy in 2025

Enterprises need scalable conversational AI to delight customers and manage high query volumes. With over 200 chatbot companies, choosing can be challenging.

Businesses should focus on key features to find suitable solutions, including integrations with their tech stack, integrations with customer platforms, and customization options for communication.

We compared the 20 most popular chatbot platforms for developing applications.

Top 20 chatbot companies comparison

Table Features:

• Sorting: Products are listed in alphabetical order.
• UX use is also divided into two categories.
  • With a GUI type, users can interact with a system through graphical elements, such as windows, icons, buttons, and menus, rather than text-based commands.
  • Programmable refers to systems or interfaces that allow users to automate tasks or customize the functionality through scripting.

There are various tools available for creating a chatbot. While natural language processing platforms and large language models are among the most common, this article focuses solely on chatbot development services.

1. Ada AI Chatbot

Ada markets itself as an “AI-first customer service platform” that uses its Reasoning Engine™ to process queries through natural language processing (NLP) and leverages large language models (LLMs) from companies such as OpenAI and Gemini. The software integrates with Zendesk and HubSpot, supporting over 50 languages.

Significant drawbacks include users complaining about “endless loop” encounters, opaque pricing that requires unique quotations, and inefficient AI responses.

2. Amazon Lex

Amazon Lex leverages Alexa’s technology, offering ASR and NLU capabilities for voice and text interactions with pay-as-you-go pricing (10,000 free text requests monthly for the first year). Recent enhancements include generative AI for Q&A intents and an Automated Chatbot Designer.

Limitations involve integration challenges with platforms like Slack, issues with accent recognition, response delays, and complex debugging processes.

3. Botpress

Botpress is an open-source platform that can be deployed in the cloud or on-site using an Autonomous Node for LLM decision-making. It features a drag-and-drop interface with advanced AI cards (AI Task, AI Generate, and AI Transition) and allows for custom data training. Additionally, it performs well during testing with the Event Debugger.

However, users express concerns about insufficient live chat support for basic plans, occasional glitches, a steep learning curve for advanced features, and gaps in the documentation.

4. ChatBot.com

ChatBot.com provides a chatbot development platform that features a visual flow designer, natural language processing, and multi-channel deployment capabilities. Emphasizing lead generation and customer service automation across various industries, the platform also provides integration with popular products and services.

Key limitations include the inability to view a complete overview of all chatbot flows, which is problematic for larger bots. There is also a lack of Facebook-specific features, such as auto-replies to comments and Facebook Ads integration, as well as limited capabilities in managing complex customer inquiries.

5. CM.com

CM.com combines generative AI with traditional chatbot capabilities to operate across multiple channels, including voice calls, SMS, Instagram, and WhatsApp through its “Conversational AI Cloud” platform. With a drag-and-drop builder and new generative AI integrations, the platform can scan PDFs and web pages to provide AI-generated responses without requiring extensive training.

However, users do complain about the lack of regional coverage, and some point out that more countries should be supported in order to enhance marketing reach.

6. Drift

Drift is a B2B conversational marketing platform with the primary objectives of generating and qualifying leads. It automatically schedules meetings and identifies high-value prospects using machine learning and natural language processing (NLP).

Some key disadvantages include a prohibitively high cost, starting at $2,500 per month, limited multi-channel support that primarily focuses on websites, and occasional application issues.

7. Flow XO

A no-code platform for small to medium-sized enterprises, Flow XO supports six channels, including Telegram, Facebook Messenger, WhatsApp, and SMS. With a free package that offers 100 interactions per month, it provides sentiment analysis, ChatGPT integration, and more than 100 direct connectors.

Limitations include a lack of comprehensive user overview features, a heavy reliance on third-party connections that results in a “Frankenstein” approach, and minimal chat widget capabilities.

8. Google Dialogflow CX

Google’s Dialogflow CX offers advanced conversational AI, integrating with Google’s AI infrastructure and featuring a visual flow builder along with state-based routing. It supports up to 20 separate conversation flows and provides faster development in both CX (advanced) and ES (standard) editions.

However, some limitations include a steep learning curve for non-programmers, the exclusivity of Google AI models, and the challenge of developing complex chatbot processes without extensive technical knowledge.

9. Gupshup

With configurable features and support for text and multimedia communication, Gupshup provides WhatsApp, SMS, and Slack interfaces aimed at the banking, financial services, and insurance industries. The platform offers API-driven business messaging solutions across various channels, focusing on applications in the financial sector.

Its drawbacks include lengthy product development cycles, limited AI capabilities for complex customer interactions, challenges for novices, and distinct login requirements for different platform components, such as journey management and template management.

10. Haptik

With its GUI-based interface and text/voice communication features, Haptik targets the telecom and healthcare sectors and connects with Salesforce and Zendesk. The platform’s primary goal is to provide multichannel assistance, enterprise-level integrations, and industry-specific solutions.

Some limitations include the inability to modify or update current bot flows without contacting support (changes are chargeable), the high cost of the pricing structure, the significant learning curve for new users, limited customization flexibility, and intricate integration configurations that may affect the customer experience.

11. IBM Watson Assistant

With its GUI interface and text/voice capabilities, IBM Watson Assistant targets the banking, healthcare, and telecommunications industries by utilizing Salesforce and IBM Cloud interfaces. The platform is ideal for data-heavy applications, as it performs exceptionally well in enterprise-grade use and connects seamlessly with other Watson AI services.

Some drawbacks include high costs for startups and small businesses, complex integration with legacy systems, slow response times during peak usage, and limited customization options for the chat widget’s appearance. Additionally, support is initially available only in English, which makes international deployments challenging.

12. Intercom

Intercom is a comprehensive messaging platform that includes help desk features, live chat, and AI chatbots (Fin AI Agent). With over 100 connectors, it provides real-time support, process automation, and detailed analytics.

Limitations include a complex interface that may overwhelm novice users and a high price that starts at $39 per seat per month, with additional per-message fees, making it expensive for small organizations.

13. Kore.ai

With a graphical user interface and support for text, voice, and multimedia communication, Kore.ai utilizes SAP, Salesforce, and Slack interfaces to target the financial and insurance sectors. The platform emphasizes industry-specific solutions and enterprise conversational AI, boasting robust integration features.

However, some drawbacks include unclear pricing (custom quotes needed) and complex integration setups that may affect user experience. Additionally, there is no rollback mechanism for bots, and the documentation is reportedly insufficient.

14. Landbot

Landbot, which features a graphical user interface and supports text and multimedia communication, integrates with e-commerce apps such as Zapier, Slack, and Salesforce. The platform’s specialty is developing visual chatbots for conversational landing pages and lead generation forms.

The drawbacks include the inability to engage in advanced AI discussions, the limited customization options compared to enterprise systems, the potentially high costs for scenarios with heavy usage volumes, and the requirement for external connections to access more advanced functionality.

15. LivePerson

With a graphical user interface and capabilities for text, speech, and multimedia, LivePerson offers connectors for Salesforce, Genesys, and Adobe, targeting the retail and telecom industries. The platform primarily focuses on customer engagement and enterprise conversational commerce.

Some limitations include a steep learning curve for non-technical users to execute sophisticated conversational processes, high costs for full feature access, complicated setup and configuration requirements, and a necessity for substantial technical skills for optimal adoption.

16. Manychat

For e-commerce and social networking apps featuring a graphical user interface and text or multimedia communication, ManyChat provides connectors with Facebook, Shopify, and MailChimp. The platform specializes in social media commerce and Facebook Messenger marketing automation.

However, it has notable limitations, including a heavy reliance on Facebook’s platform policies and changes, restricted functionality outside of social media channels, limited AI capabilities for complex customer service scenarios that go beyond basic automated responses, and vulnerability to shifts in social media platform policies.

17. Microsoft Azure Bot Service

With its customizable interface and text/voice capabilities, Microsoft Azure Bot Service connects with Slack and the Microsoft ecosystem for applications across various industries. The platform performs effectively in business environments that already utilize Microsoft products and services.

Limitations include a steep learning curve for developers unfamiliar with Microsoft’s ecosystem, a complex setup that demands significant technical experience, high integration costs for non-Microsoft environments, and a dependence on Azure infrastructure that may not suit all business needs.

18. MobileMonkey

MobileMonkey integrates with Facebook, Zapier, and Shopify for marketing and small business applications that offer a graphical user interface along with text and multimedia capabilities. The platform mainly focuses on automating multi-channel marketing across social, mobile, and web platforms.

Limitations include a heavy reliance on the policies of third-party platforms, especially Facebook, limited advanced AI capabilities for complex interactions, restricted functionality for intricate customer service scenarios beyond basic marketing automation, and susceptibility to changes in social media platform terms.

19. Pypestream

Pypestream, which features a graphical user interface and text/multimedia communication capabilities, interacts with Salesforce, SAP, and Zendesk in the insurance and retail industries. The platform specializes in customer service automation for regulated sectors.

Some of its limitations include high implementation and license costs, complex configuration requirements that necessitate technical expertise, limited flexibility for companies outside of highly regulated sectors like finance and insurance, and the need for significant modifications for non-standard use cases.

20. Tars

Tars provides Zapier, Salesforce, and HubSpot connectors for real estate and healthcare apps with a graphical user interface and text messaging capabilities. The software leverages conversational landing pages to generate and qualify leads.

Restrictions include limited AI functionality for intricate, multi-turn conversations, limited voice and multimedia capabilities compared to competitors, pricing that can be costly for companies requiring high-volume lead processing, and a narrow focus that may not meet a variety of business needs beyond lead generation.

How to choose the right chatbot company?

Clarify your chatbot requirements

The right vendor will depend on the type of solution you are looking for. The solution will have these important parameters:

• The type of bot: FAQ bot, sales bot, customer service chatbot, etc.
• Dialog volume.
• Channels: For example, major messaging platforms such as Facebook Messenger, WhatsApp, or Slack can be considered for deployment (conversational commerce platforms).
• Industries such as e-commerce, sales, insurance, hospitality, banking, healthcare, and government.

Understand the options in the market

We can categorize the chatbot options in the market into the following three groups:

1. Self-service solutions with GUI

Build your bot using a simple Graphical User Interface (GUI) by dragging and dropping components. This approach is suitable for quickly building a simple bot with basic capabilities.

2. Self-service solutions with a programming interface

These solution providers build an API, SDK, or a library to provide a framework for your bot to use Machine Learning (ML) and Natural Language Processing (NLP) capabilities to understand the intent of user queries.

Building blocks for understanding intent, such as parsing the user query, can be provided by the API. Using the available tools, beginner or citizen developers can build bots in a couple of hours. However, a best-in-class bot would take significantly longer to build, depending on the requirements.

3. End-to-end solutions

This is the easiest way of building a chatbot. Specify what your bot needs to do and get a vendor to build it from the ground up. The price varies depending on your specifications and the chatbot developer. 

Decide whether to buy or build a chatbot

We recommend that companies deploy the following type of chatbots under these specific circumstances.

• Go for a self-service with a GUI if all of the following are true:
  • You have little to no budget for chatbots,
  • You have limited expectations of your bot,
  • You lack or can’t access many technical skills.
• Go for a self-service with an API option, SDK, or library if all of the following are true:
  • You have little to no chatbot budget,
  • You have limited expectations from your bot (however, with a great technical team, you could also build a world-class bot).
  • You have or can access some tech skills.
• If none of the above factors align with your business, consider an end-to-end solution chatbot company.
• For all other cases, it would be beneficial to continue exploring all options. For example, if you have tech skills, it might be worth building your own chatbot with some support from self-service solutions, even if you can allocate a sizable budget for it.

Understand the self-service chatbot provider landscape

Consider whether your business has the capability to handle chatbot programming. If so, there are various advanced chatbot development tools available in the market that can complete the task in just a few hours. Let’s look at your options:

1. Tech giants

Google, Amazon, Microsoft, IBM, and Facebook are all viable options for building a functional bot. Today, AI is at the forefront of all tech giants. Their solutions come with a supportive community and a large team of developers, so you won’t need to worry about developing on a platform that might suddenly become obsolete.

These chatbot development platforms can be used to design conversational AI interactions, such as chatbots, voice assistants, and virtual assistants, which are integrated with various conversational platforms.

2. Startups

Startups are exploring various strategies to innovate in this emerging market. For example, they offer graphical user interfaces and even bots designed to build other bots, facilitating bot development.

Factors to consider for deployment

Beyond choosing your chatbot type, evaluate these key factors that determine deployment success:

Integration requirements

Ensure that the platform links to your key business systems:

• CRM systems
• Support tools
• Communication platforms
• Business applications

Scalability and performance

Check platform limits before they become constraints:

• Monthly conversation volumes
• Handling of concurrent users during traffic spikes
• Guarantees on response time
• Multi-language support for global deployments

Security and compliance

Check out the essential security needs such as:

• Industry certifications
• Data encryption and residency controls
• Access controls with audit logging

Support and implementation timeline

Consider practical expectations for deployment, like:

• Setup duration: 1-3 days for FAQ bots; 4-8 weeks for intricate workflows
• Integration development: 2-6 weeks, varying by system complexity
• Support level: Dedicated account management compared to standard help desk services.

Consider these factors in relation to your business size, industry regulations, and technical capabilities.

Further reading

• Top 5 WhatsApp Business Partners
• Chatbot platform vendors
• Voice bot platform vendors