ChatGPT Enterprise versus Microsoft Copilot: A 2025 Comparison

Today, we want to compare ChatGPT Enterprise and Microsoft Copilot. Specifically, we want to understand the limitations of these extremely popular tools to help explain a trend.

In 2025 alone, enterprises will invest over $600B into generative AI. However, this number draws an enigma: three out of four companies can’t demonstrate tangible business value from AI. The gap isn’t in the models—they’re incredibly effective with constant improvements being rolled out. With ChatGPT Enterprise and Microsoft Copilot being two of the most popular AI tools, with over 70% adoption from Fortune 500s, it begs the question: why are these tools falling short?

Today, let’s understand how these orchestration models are limiting and discuss an alternative approach that tackles the shortcomings head on.

Why are companies adopting Microsoft Copilot and ChatGPT Enterprise

Let’s first understand why Microsoft Copilot and ChatGPT Enterprise are so popular, with nearly three-fourths of major enterprises adopting them. The reasoning relies on how both tools are generalist.

The advantage of Microsoft Copilot and ChatGPT Enterprise is that they can be used by nearly every team in an organization for a wide breath of tasks. For example, both products could be used to synthesize documents, analyze data, write code, or flag inaccuracies. This broad nature of both products compliments the fact that many enterprises don’t know how they want to use AI yet, figuring things out on a team-by-team basis. Because Microsoft Copilot and ChatGPT Enterprise are fancy chat interfaces, they could be workshopped into any workflow that involves thinking or analysis.

However, Microsoft Copilot and ChatGPT Enterprise aren’t identical tools. Let’s discuss the strengths of each, starting with Microsoft Copilot.

Microsoft Copilot: The Ecosystem Play

Microsoft Copilot is a chat interface that is accessible both from a dedicated interface and directly inside Microsoft tools such as Excel, Powerpoint, or VSCode. Microsoft Copilot champions the Microsoft 365 stack, with immediate availability in Microsoft’s tools. Accordingly, Microsoft Copilot is designed for teams that use Microsoft tools across the board.

Given tight integration with other enterprise software, Microsoft Copilot is also designed with security in mind. It leverages Microsoft’s Zero Trust security framework—where other endpoints are considered outsiders that undergo the same authentication guardrails. It also only integrates with data within Microsoft 365 boundary, preventing employees from accidentally leaking information to AI models that’s not already protected by Microsoft’s stack. These protections mean that Microsoft Copilot is a GDPR, HIPAA, and SOC 2 compliant tactic towards using AI, with comprehensive auditing available through Microsoft Purview.

However, despite having tight integrations with Microsoft’s tooling, Microsoft Copilot is also quite limited. For instance, in Excel, Microsoft Copilot can tackle table-formatted data, but cannot analyze embedded or linked content. It also has limited memory, where context is lost whenever it is refreshed (just like a browser tab). Content generation is not any more customized beyond the base models, and Microsoft hasn’t built any middleware that fine-tunes the models beyond the AI binaries available out-of-the-box.

Additionally, Microsoft Copilot is inaccessible to teams that don’t use Microsoft products and is a fragmented solution for teams that use a few, but not many Microsoft tools. For teams looking for a similar product that’s not tightly bound to Microsoft’s ecosystem, ChatGPT Enterprise might be a better pick.

ChatGPT Enterprise: Cutting-Edge AI with Surface-Level Integrations

Most users are familiar with ChatGPT, OpenAI’s flagship AI product. ChatGPT Enterprise is the highest tier of ChatGPT; it eclipses the Pro and Business plans. Given that ChatGPT is free, we’ll mostly compare ChatGPT Enterprise to Microsoft Copilot by focusing on the features exclusive to ChatGPT Enterprise, as Microsoft Copilot users also have access to ChatGPT free tier.

ChatGPT Enterprise is centered around a few tenets: massive context windows, broad integrations, ironclad security promises, and uncapped API limits.

While ChatGPT Enterprise isn’t pegged to a product suite, and therefore exists as an external program / interface, it does integrate with popular tools including GitHub, Google Workspace, Salesforce, Microsoft 365, and Box. It also integrates with Zapier, which can bridge ChatGPT Enterprise to 7,000+ integrations. These integrations enable ChatGPT users to automatically pull information without having to copy and paste data; this is especially useful to tasks that involve synthesizing or analyzing company or customer data. For instance, with ChatGPT Enterprise, users could:

• Design a brief about selling to mid-market buyers, integrating the company’s sales documentation the lives in Google Drive to properly understand the team’s playbook
• Create a report for IT leadership on spikes in traffic, integrating with the company’s Excel sheets in Microsoft 365 to identify trends
• Issue a pull request to patch a CSS syntax issue in the last pull request, integrating with the companies GitHub.

All of these tasks are possible with a 400K token context window and 2x faster processing than cheaper ChatGPT tiers. Generally speaking, ChatGPT Enterprise is OpenAI’s most performant AI chat system.

Additionally, because users can create custom GPTs in ChatGPT Enterprise—custom, fine-tuned language models for custom tasks—it’s easy to create chats attuned to specific integration-backed problems. For instance, ChatGPT Enterprise users could create a custom GPT that has context to the company’s Salesforce schema, making it easy to ask questions about customer accounts.

ChatGPT Enterprise checks off many security checkboxes, something that’s necessary since it’s introducing a new application outside the confines of an already secure environment (e.g. Microsoft 365). ChatGPT Enterprise has support for SAML SSO, SCIM provisioning, RBAC, user groups, configurable data retention, regional residency, and usage auditing. These practices make ChatGPT Enterprise compatible with compliance standards like GDPR, CCPA, SOC 2, ISO 21001, and CSA STAR.

However, while ChatGPT Enterprise isn’t confined to a single suite of tools, it’s still very limited in what it can do. Like Microsoft Copilot, ChatGPT Enterprise is primarily an AI Chat interface. Outside of a few bespoke outputs like a pull request, the primary output of ChatGPT Enterprise is the equivalent of a text message. That’s a massive constraint for businesses that want use AI to actually affect their workflows, where actions are more likely to be data writes than exported documents. This introduces the biggest limitation of both ChatGPT Enterprise and Microsoft Copilot: poor support for AI agents.

What are AI Agents? Does Microsoft Copilot or ChatGPT Enterprise support them?

AI Agents are autonomous programs that leverage AI to make decisions to carryout actions. Being able to execute actions is what separate AI Agents from AI chat. For example, an AI chat application could integrate with Salesforce to identify customers that are churn risks; meanwhile, an AI Agent could identify those customers, mark a field in Salesforce, and queue an email to go out to re-engage.

Both Microsoft Copilot and ChatGPT Enterprise have pre-built agents. However, these agents are limited to specific tasks. For example, Microsoft Copilot has an agent to create visuals via the Visual Creator agent. Or, employees could use the Jira Cloud agent to update tasks in Jira for them. Meanwhile, in ChatGPT Enterprise, the Codex agent could be used to write and make coding changes.

While these agents are certainly helpful, they are designed for specific siloed tasks with specific data requirements. Accordingly, these agents operate more like fancy integrations than a true AI agent. Rather, a fully autonomous AI agent should be able to:

• Decide what data sources to pull context from in order to make a decision. It should also keep pulling context until a confident decision could be made (or context was exhausted)
• Make a decision on what to do next independently. This might involve altering data in Salesforce, creating a Jira task, updating a Google doc, sending an email, or anything else (or combination of outputs)

In other words, truly autonomous agents are like effective employees—they are curious, learn things, and figure out what to do next without requiring explicit directions. However, Microsoft Copilot and ChatGPT Enterprise don’t have support for truly autonomous agents.

Credal: A Third Alternative

The reason that we’re strongly versed in ChatGPT Enterprise and Microsoft Copilot is because many Credal customers were former users of those platforms but needed a more configurable agentic platform. Credal is an orchestration platform for AI agents. While ChatGPT Enterprise and Microsoft Copilot are just AI chat platforms with pre-built integrations, Credal handles a lot more

• With Credal, you can integrate with data sources and inherit permissions so that access is granular to the users invoking agents
• Provide agents with tools to carryout actions in integrated programs, including Salesforce, Office 365, Jira, and Google Drive
• Set-up human-in-the-loop governance to safeguard agents from doing destructive actions, which is possible when actions can independently carryout non-deterministic actions

With Credal, you can still ship an AI chat platform that is able to tap fully-custom agents under-the-hood to execute on actions. For example, with Credal, you could create a brief about selling to mid-market buyers and include that brief in Salesforce notes for each mid-market account.

An enterprise might have Copilot drafting documents in Word or ChatGPT Enterprise summarizing customer calls, but without AI agents that have read and write access, these are siloed tasks that still depend on humans, severely hampering the potential time-savings of AI. Our goal with Credal is to build an AI agents platform with security and integrations suited for enterprises without limiting the capacity of AI.

Credal has a proven track-record, where most companies have +75% AI adoption, with nearly 50% of employees saving 20-40% of daily work. Credal is used by companies such as Wise, MongoDB, Checkr, IFRS, Lattice, and Gecko.

Pricing: ChatGPT Enterprise versus Microsoft Copilot

ChatGPT Enterprise and Microsoft Copilot both have per-seat pricing, what minimum seats enforced for each (but not disclosed).

For Microsoft Copilot, the pricing is just $30 per user per month. The pricing for ChatGPT Enterprise is meanwhile $60 per user per month, with rumors that they require 150 users minimum on an annual commitment.

Generally speaking, ChatGPT Enterprise is more expensive than Microsoft Copilot because it is a standalone solution; Microsoft Copilot, meanwhile, is expected to be bundled into Microsoft 365.

A Closing Thought: The Multi-Agent Future

ChatGPT Enterprise and Microsoft Copilot have very rudimentary AI agents. They are limited, not autonomous, and require a lot of human rigging. Long-term, this design pattern will continue to be constraining as more and more workflows move towards multi-agent workflows.

At Credal, we’re invested in building tooling for multi-agents. Multi-agents are akin to microservices—each agent focuses its context window on a specific set of tasks. For instance, one agent might be focused on deploying emails and managing flow. Another agent might be focused on managing the team’s Confluence. Another might be focused on the company’s internal policies. Ideally, agents work together to accomplish a task; that way, no single agent needs the entire company’s context, and agentic work better imitates how humans work together to accomplish things.

‍

Head to Head