The Best Microsoft Copilot Studio Alternatives

Since Microsoft partnered with OpenAI, it started releasing a range of products and features to leverage AI technology in existing products. Under the single name of Copilot, you can find it in any Microsoft product, usually in the form of a chat experience that extends the experience of the app you’re accessing it on.

Microsoft Copilot Studio is one of these new products: you can create a range of conversational AI experiences that seamlessly integrate with Microsoft 365 apps, customizing them with your data and instructions. But, despite the push for innovation, the marketing promises aren’t living up to expectations.

Users are frustrated with the platform’s complexity and poor performance. Salesforce CEO Marc Benioff compared the new Copilot to the old Clippy of Microsoft Office. And, on a more serious level, a vulnerability on the Copilot Studio platform exposed sensitive data in August 2024. It has since been patched but raises questions about the development process and commitment to quality, making it a risky choice for an enterprise environment.

These shortcomings open the field for better Copilot Studio competitors. If you’re considering whether Copilot Studio is the right choice for your business—or looking to move away from the platform—here are the best alternatives to upgrade your AI building experience.

Why you need to try Microsoft Copilot Studio alternatives

Here’s a short list of Microsoft Copilot Studio’s current limitations:

• Not fully no-code. While you can build and customize your Copilot Studio experience via the web app, doing so requires technical knowledge, especially in setting up data integrations and actions.

• Not enough customization and flexibility. Copilot Studio takes a low-code approach, which should give more customization, it introduces a double problem: it’s too complex for non-technical users and not flexible enough for developers.

• Challenging integration process. Despite highlighting thousands of data integrations available, most are handled externally in Microsoft Graph and Power Platform, two separate products. 

• Unfinished product feel. Users on Reddit complain that there isn’t adequate visibility into what happens at each moment of building a chatbot, making customization harder in Copilot Studio.

• Poor documentation. While the documentation coverage for Copilot Studio has been growing, it still doesn’t cover the entire product with a consistent level of depth, leaving users to fill in the gaps with experimentation or by asking for help.

The best Microsoft Copilot Studio alternatives

Stack AI

Stack AI pros:

• Rapid deployment

• Versatility

Stack AI cons:

• Feature limitations in free tier

• Expensive for small businesses

Stack AI is an enterprise-grade generative AI platform, giving you the tools to create automation workflows and AI agents for a variety of daily use cases. Instead of menus and forms, you’ll be using a no-code, visual drag-and-drop interface to build. You can drop nodes onto a canvas, connecting inputs, data sources and LLMs together to process and transform data. The building process is intuitive, accessible to non-technical users and professional developers alike.

It offers a wide range of integrations, from the most popular sources such as Microsoft Sharepoint or Google Drive, making it easy to set up RAG based on your company data. For added versatility, it integrates with all AI providers: you can access OpenAI, Anthropic, and Google models, among many others, through native integrations, requiring zero setup to configure or update. It has high privacy and security standards, making it possible to build tools like HIPAA-compliant chatbots for healthcare companies.

Stack AI is the best Microsoft Copilot Studio alternative as it’s easier, more versatile, and integrates with more AI models. It has its entire feature set inside a single platform, offering a more straightforward building, deployment, and iteration experience.

OpenAI GPTs

OpenAI GPTs pros:

• Simple and intuitive

• Easy sharing

OpenAI GPTs cons:

• Unclear data-sharing terms

• Occasional inaccuracies

Accessible via ChatGPT, OpenAI’s GPTs let you customize the original experience with your instructions and data. Then, instead of using the baseline GPT-4o to ask questions or generate content, you can use the GPT for more relevant and contextually-aware answers. 

Beyond creating your own, you can browse the store to discover and try trending GPTs. You can use image generators with custom instructions, ask for specialized research, or use a code copilot to build a new program. This can save time when trying to solve a problem in your work. Additionally, it can also be used as a tool to promote your prompt engineering skills or your brand: if your GPT becomes popular in the store, this can drive traffic to your online channels.

As one of Microsoft Copilot Studio alternatives, OpenAI’s GPTs are a simple and intuitive way of customizing the original ChatGPT experience with your data.

Amazon Q

Amazon Q pros:

• Deep integration with AWS

• Coding assistance

Amazon Q cons:

• Struggles with understanding complex code relationships

• Limited handling of non-text data

Amazon Q, developed by Amazon Web Services (AWS), is a chat-driven assistant ready to help you in two ways: Amazon Q Business answers questions about your business data; and Amazon Q Developer answers technical questions about the AWS infrastructure, making it easy for your IT team to deploy solutions.

The configuration process aims to be as automated and hands-off as possible. Start by connecting your data sources to Amazon Q: upload all the documents, images, databases, and data warehouses. The platform will read document metadata to understand what your company does, your industry, and your offering. At the end of the setup stage, it will configure the AI assistant to answer questions based on the data, drawing from the connected data sources.

As one of Microsoft Copilot Studio alternatives, Amazon Q has a lower setup complexity and the bonus of assisting with developer work if needed.

Google Vertex AI

Google Vertex AI pros:

• Easy integrations with Google services

• Strong integration capabilities

Google Vertex AI cons:

• Documentation could be more thorough

• Steep learning curve

Vertex AI is a unified AI infrastructure platform, part of Google Cloud Platform. It offers APIs for popular AI models on the market, so you can connect them to your apps and internal tools. With deep integration with Google Cloud Services and Search, it opens the door to everything this tech giant has to offer in a single place.

Beyond these features, Vertex AI has an Agent Builder to help configure a conversational experience that answers questions and triggers actions in your systems. You can connect nodes to decide what happens based on the conversation topic or system state. Despite being powerful, it has a steep learning curve and fits best in the low-code category, requiring good systems thinking and data integration knowledge.

As one of Microsoft Copilot Studio alternatives, Google Vertex AI is a more flexible, powerful solution with AI model API endpoints—albeit coming with a steeper learning curve.

Zapier Agents

Zapier Agents pros:

• Extensive list of integrations

• Friendly user interface

Zapier Agents cons:

• Steep learning curve for complex workflows

• Full feature unlock can be expensive

Zapier Agents takes the approachable experience of Zaps to the conversational agent territory. Using the platform’s native integrations with over 7,000 apps, you can set up agents using system instructions in plain language. Then, whenever you chat with your agent, it will detect action intents to search for information or start an action in an external system.

Part of a larger platform, the Agents feature set plays well with other Zapier products. Beyond Zaps—the workflow automation product—there are Tables for data storage, Interfaces for building a frontend with components, and Chatbots for knowledge-retrieval use cases.

As one of Microsoft Copilot Studio alternatives, Zapier Agents is easier to use and integrate with other apps, while also being more oriented for taking action on external systems.

Salesforce Agentforce

Salesforce Agentforce pros:

• Scalable

• Seamless integration with existing Salesforce data

Salesforce Agentforce cons:

• Complex to implement

• High costs

CRMs are the backbone of a company’s data. The bigger you get, the harder it can be to navigate that data and do something useful with it. This is why Salesforce is rolling out Agentforce, a suite of tools to build conversational experiences that put you in touch with your data while automating processes around it.

Agentforce is deeply integrated into the data you have in Salesforce. Only Enterprise customers can access the full suite of tools needed to deploy an agent—a high entry bar if you’re just getting started, but welcome news if you’re a loyal user. Creating a chatbot involves picking the information sources you want to use, setting up the instructions, and then connecting predetermined actions that you can configure in a visual workflow builder. 

As one of Microsoft Copilot Studio alternatives, Salesforce Agentforce has better guardrails, being more configurable to deal with inappropriate prompts. However, it requires too many Salesforce products to set up and configure.

IBM watsonx Assistant

IBM watsonx Assistant pros:

• Advanced NLP capabilities

• High security standards

IBM watsonx Assistant cons:

• User interface is not very intuitive

• Limited customization

IBM’s reputation joins the conversational agents category with watsonx Assistant. Like most of the competition on this list, it offers a no-code experience and templates to get a head start. Unlike other options, it offers a range of language models to increase the assistant’s intelligence. This includes the now-traditional LLMs, models for natural language processing/understanding (NLP/NLU), and intelligent context gathering tools.

With all of these combined, watsonx can understand instructions better, being a good match for customer support cases with many topics or potential actions to take. However, these capabilities come at a cost: watsonx also has a steep learning curve with an unintuitive user experience.

As one of Microsoft Copilot Studio alternatives, IBM watsonx Assistant displays higher intelligence, being able to interpret questions and commands with higher accuracy.

Lutra AI

Lutra AI pros:

• Easy to use

• Fast setup

Lutra AI cons:

• Limited custom integrations

• Lack of advanced building control

Founded in 2023 by former employees of Google and Coursera, Lutra AI goes further than no-code: it’s a zero-code platform where you can build agents by chatting with an agent. It walks you through everything the platform needs to build a new tool, without requiring coding or complex integrations at any time. It’s fast and easy to create a conversational tool for contact enrichment, web scraping, or data analysis.

Leveraging its native integrations with popular platforms such as Airtable, Slack, and Google Workspace, you can push out the results to wherever you want to store or share them. Or the other way around: you can use the data in these data sources to start actions just by using English.

As one of Microsoft Copilot Studio alternatives, Lutra AI is much less complex, offering useful actions if you work with a lot of data, with demanding search and storage needs.

Rasa

Rasa pros:

• Flexible open-source platform

• Advanced dialogue management

Rasa cons:

• Technical expertise required

• Resource-intensive

Rasa is an open-source conversational AI platform, where developers can build, manage and deploy conversational chatbots and voice assistants. It offers a machine learning framework for processing natural language and managing dialogue, allowing deep customization of agent intelligence in complex conversations.

Deploying it yourself will require Python skills—which, despite not being the hardest language to learn, still offers a steep learning curve. However, the no-code web app reduces the entry bar somewhat, even if it doesn’t offer the full range of features.

As one of Microsoft Copilot Studio alternatives, Rasa is more powerful but comes with a steeper learning curve, while also being much more expensive.

Kore.ai

Pros:

• Enterprise and small business offering to match different needs

• On-premises deployment available

Cons:

• Limited out-of-the-box features

• Resource intense when running on-premises

Kore.ai is an enterprise-grade conversational AI platform. With a no-code interface and plenty of pre-built templates, you can leverage a multi-engine NLP approach to build experiences that pick up on every nuance of user messages.

For a pure conversational experience, Kore.ai offers a range of features that let you create intelligent virtual assistants for information retrieval use cases, including interacting with external systems. It integrates with multiple integration channels such as live chat, SMS and email, helping you deploy your chat experience wherever you need it.

As one of Microsoft Copilot Studio alternatives, Kore.ai has an easier time integrating with other apps by comparison, offering a simpler building experience that’s easier to manage.

Which is the best Microsoft Copilot Studio alternative for you?

Microsoft Copilot Studio’s seamless integration with other Microsoft products is a big motivator to start using the tool. But, with it’s heavy low-code leaning, complicated integration with external platforms, and occasional platform instability pushes you to look elsewhere.

When choosing the best alternative for you, consider the skill level: if your team doesn’t have technical skills, you’ll need to rely on a simpler platform; take a look at your current tech stack, and see if there are AI products already that you can try to extend functionality; and never forget to give your favorite platform shortlist a good round of testing to see if it matches your use cases.

If you’re strapped for time, give Stack AI a try: it’s a well-rounded, fully-featured solution, with a no-code approach for all skill levels. See how much you can build: create a free account and start automating.

Frequently asked questions

How is Copilot Studio different from Copilot?

Microsoft streamlined the name of its AI features under the single name “Copilot”. However, this can create some confusion as to what each product actually does.

Microsoft Copilot is an AI-powered assistant available in Microsoft products, such as the entire Microsoft 365 suite, Windows, and the Bing browser. It’s a general-purpose chatbot that uses OpenAI’s models to answer questions on any topic. Beyond writing prompts to instruct the AI to carry actions, it’s not much more customizable than that: it only offers predefined functionality.

Microsoft Copilot Studio allows you to configure and customize the original Copilot experience with your data, allowing you to connect to your data sources, and connect the chatbot to other channels. This makes it more than a chatbot builder, crossing into AI agent territory.

How is Microsoft Copilot Studio different from Azure AI Foundry?

Microsoft Copilot Studio is a low-code platform for configuring AI chatbot and agent experiences that integrate seamlessly with other Microsoft products. 

Azure AI Foundry is a product that belongs to the Microsoft Azure Cloud offering: it is more oriented towards developers and enterprise customers. It offers a higher level of customization and integration when compared with Copilot Studio, also allowing for deeper integrations with both data sources and channels.

Can I integrate Microsoft Copilot Studio with any third-party applications?

Yes. Microsoft Copilot Studio integrates with third-party apps in three different ways: through Microsoft Power Platform, Microsoft Graph, and by setting configurable actions in the chatbot or agent project.

How secure is Microsoft Copilot Studio? Does it comply with GDPR and HIPAA regulations?

Microsoft Copilot Studio is fully compliant with a wide range of privacy and security certifications, such as GDPR, HIPAA, and SOC.

Is Microsoft Copilot Studio a no-code or low-code platform?

Despite having a user-friendly web app interface, Microsoft Copilot Studio isn’t fully no-code. Using its full functionality requires technical knowledge, especially when integrating APIs and connecting your chatbot to external channels.

How does Microsoft Copilot Studio use generative AI for chatbots?

Microsoft Copilot Studio uses LLM technology in three major ways:

• To process natural language, taking questions or commands and understand what they mean

• To create code or text necessary to interact with external systems

• To generate text as it answers the user’s questions or provides feedback on actions taken

How does an AI chatbot take action on external systems?

To interact with external systems, an AI chatbot needs instructions on detecting commands or system states that trigger the start of the action. Most platforms have a setting that lets you express that in natural language, such as “detect when a user asks a question about paid time off and search our company policy knowledge base for the answer.”

Then, you need to manually connect the chatbot to the data source or system you want it to act on. This means setting up a no-code software integration in the platform (if supported), or an API connection to the external system.

Once the connection process is complete, every time the model detects the command or intent based on your instructions, it will execute the action on the external system. This will always be based on how you set up the action previously. Bear in mind that, to accurately interpret and execute commands, it’s recommended to use a model with a high level of intelligence, such as GPT-4o.

How do I build my own AI chatbot or agent from scratch?

To build your chatbot or agent from scratch, start by picking one of the platforms on the list above. Create an account, read the quick start guide, and explore the templates.

If any of the templates relate to your use case, you can customize it to fit your needs. Connect your data sources, set up actions, and keep testing your agent as you move forward.

Once you’re happy with your first version, share your AI chatbot with other team members. Gather feedback and use the platform’s analytics tools to improve it over time, making it more useful for everyone.

How do I train an AI chatbot to improve its accuracy?

While training or fine-tuning an AI model is reserved for machine learning experts, you can improve chatbot response accuracy by using RAG. In a nutshell, you can upload documents and files that will be used to provide context to an LLM when generating an answer.

This is a relatively common feature of most AI chatbot and agent builders, and it doesn’t require any technical expertise to set up.

Can AI chatbots handle complex decision-making and reasoning?

At the time of writing, AI chatbots can handle an impressive range of complex decision-making and reasoning, but there is still more ground to cover for perfect accuracy. This depends on two major factors:

• Model intelligence. Higher intelligence means better capacity to understand language, gauge user intent, and respond appropriately. Additionally, reasoning models such as DeepSeek R1 have the potential to break down complex problems, providing a more accurate response when compared with earlier models.