How Cassidy and ChatGPT Enterprise compare at a glance
| Strategic Goal | ChatGPT Enterprise | Cassidy |
|---|---|---|
Overall positioning | One managed AI workspace for employees to use directly across chat, Work, and Codex | One platform for chat, Agents, and governed automation together Draw |
Chat and agent quality | Strong chat, Work, and Codex experience for ad hoc research, drafting, and coding | Strong chat and Agents, with governed automation layered on top Draw |
Multi-step workflows and event triggers | Work supports scheduled and event-triggered tasks, and Workspace Agents can run on a schedule or API trigger | No-code triggers from CRM changes, meetings, forms, tickets, and any event from the apps you use Win |
Knowledge Base and company context | Company Knowledge is turned on per chat, and Enterprise app access is admin-controlled | Knowledge Base context applies automatically to every Agent and automation Win |
Model choice and provider flexibility | Runs exclusively on OpenAI's models | Routes each Agent or Workflow to OpenAI, Anthropic, Google, or open source models Win |
Deployment surfaces | Web, desktop, mobile, Slack, Microsoft Teams, Excel, Sheets, PowerPoint, and Word surfaces | Slack, Teams, Chrome, Word, Excel, PowerPoint, and Outlook surfaces for the same Agents and Workflows Draw |
Governance, permissions and audit logs | Broad platform-wide controls: SSO, SCIM, RBAC, EKM, Compliance API, IP allowlisting | Permissions and audit logs cover every Agent and Workflow in one place Draw |
Everyday usability and learning curve | Familiar to employees who already know ChatGPT, with less change management for broad rollout | A newer interface for most employees, with less consumer familiarity to build on Lose |
Multi-step workflows and event-triggered automation
ChatGPT Enterprise’s workflow automation
- Workspace Agents can run on a schedule or from an API trigger
- @ChatGPT in Slack and Microsoft Teams can turn repeat work into a recurring task on a schedule
- Admins enable event-triggered tasks on Enterprise, and ChatGPT asks for approval before taking action in connected tools
- Work can also run event-triggered tasks for Gmail, Slack, and GitHub when enabled
Cassidy’s workflow automation
- CRM changes, meetings, forms, tickets, and any event from your connected apps can start Workflows automatically
- No-code trigger setup connects business events to Workflows without custom API plumbing
- A Workflow can read from one system and act in another in a single run
- Results feed directly into the next step of the same Workflow, with branching and approvals built in and each step visible for review
Knowledge base, integrations, and deployment surfaces
ChatGPT Enterprise’s integrations and deployment
- Company Knowledge is available on ChatGPT web
- Users turn Company Knowledge on per conversation and choose which connected apps to search
- Enterprise admins control plugins and apps, and underlying app permissions still apply
- Answers include citations and respect each user's source permissions
- OpenAI also ships native surfaces for Slack, Microsoft Teams, Excel, Google Sheets, PowerPoint, and Word
Cassidy’s integrations and deployment
- Knowledge Base syncs continuously from Drive, SharePoint, Confluence, and CRMs
- Context can apply automatically to every Agent and Workflow with no chat-by-chat toggle
- Source permissions are inherited so access matches the original system
- Agents and Workflows share the same Knowledge Base, so chat and automation draw on one source of context
- Deploys natively inside Slack, Teams, Chrome, Word, Excel, PowerPoint, and Outlook
Model choice and provider flexibility
ChatGPT Enterprise’s model options
- ChatGPT, Work, Codex, and Workspace Agents use OpenAI models
- Workspace owners can set model access and defaults, but the available model list is OpenAI-only
- Switching models means choosing among OpenAI's own models and modes, not Anthropic or Google
Cassidy’s model options
- Choose OpenAI, Anthropic, Google, or open source models per Agent or Workflow step
- Switch models without rebuilding the Agent or automation
- New models can be added as they ship instead of on one vendor's schedule
- Route cheaper or faster models to lower-stakes steps automatically
- A recommended models selector, kept current by the Cassidy team, makes it easy to pick the right model for the task at hand while keeping costs efficient
- Admins decide which models are available to end users, controlling both cost and quality
Governance, permissions, and audit logs
ChatGPT Enterprise’s governance controls
- Enterprise adds SAML SSO, SCIM, RBAC, EKM, IP allowlisting, and data residency in ten regions
- Compliance API covers conversations and app activity, and OpenAI also documents visibility for agent activity
- Admins can control models, plugins, apps, actions, and usage by workspace role
- Agent activity is covered by audit logs and monitoring, and admins manage agents, spend, and plugins from a central console
Cassidy’s governance controls
- RBAC, SSO, and audit logs cover every Agent and Workflow, managed from one place
- Permissions and logging apply to each Workflow's triggers, steps, and actions alongside chat
- Admins can set org-wide and per-user usage caps around operational automations
- SOC 2 Type II, GDPR, HIPAA, and CASA validated controls support enterprise rollout
Implementation, onboarding, and support model
ChatGPT Enterprise’s support model
- Enterprise includes dedicated onboarding and ongoing account management
- Eligible customers can get hands-on support for implementation, training, and change management
- SSO, SCIM, RBAC, and workspace setup are configured through OpenAI’s admin console and docs
- Agent templates, deployment guidance, and plugin controls speed up initial rollout
Cassidy’s support model
- A dedicated solutions team helps design and launch the first Workflows
- Onboarding includes hands-on training for the teams who will own each Workflow
- SSO and provisioning setup is guided, not purely self-serve
- Support continues as new Workflows get added, not just at launch
Everyday chat and agent experience
ChatGPT Enterprise’s everyday experience
- ChatGPT runs across web, mobile, and desktop in one workspace
- Work is built for longer multi-step tasks, and Codex covers coding workflows
- Workspace Agents can be created from templates and shared to the workspace directory or Slack
- Voice works in Work on web and mobile and in Work or Codex on desktop
Cassidy’s everyday experience
- Chat and Agents can be grounded automatically in the company's Knowledge Base
- No separate product boundary between everyday chat and governed Workflows
- Agents and Workflows live in the same employee surfaces such as Slack, Teams, and Chrome
- New Agents can roll out without retraining the whole team on a separate automation tool
Choosing between Cassidy and ChatGPT Enterprise
Choose Cassidy if…
- You need a process to run the same way across every employee, not just your best prompters
- You do not want an Agent or Workflow tied to one model provider's roadmap
- Work needs to trigger from a business event without custom API plumbing
- Your work is regulated or high-stakes, so you need to see every step the AI takes, governed together with your Agents and Knowledge Base
Choose ChatGPT Enterprise if…
- You mainly need a strong general-purpose AI workspace for quick, simple team-wide chat, research, documents, and coding inside one OpenAI environment.
- Familiar ChatGPT UX and native surfaces like Slack, Teams, Word, Excel, and PowerPoint matter more than cross-system automation.
- Your security team wants OpenAI's built-in enterprise controls, dedicated onboarding, and ongoing account management.
More enterprise AI platform comparisons
FAQ
No. ChatGPT Business is the self-serve workspace for teams of 2 to 200. ChatGPT Enterprise is sales-led and adds controls such as SCIM, custom RBAC, EKM, compliance logs, and advanced analytics.
It can, and for many teams it does. The same chat experience and underlying models, including OpenAI's, are available inside Cassidy's secure environment, and Cassidy adds the governed workflows that need audit trails, trigger from business events, and run the same way every time. Some teams keep ChatGPT alongside it for quick, simple chat, research, documents, or coding, and adopting Cassidy does not require abandoning ChatGPT first.
Because new models keep arriving, and each one tends to be better at specific capabilities such as reasoning, writing, speed, or cost. If your workflows are tied to one provider, you wait on that provider's next release and re-test prompts, latency, and edge cases to switch. Cassidy keeps workflow logic separate from model choice, so teams can pick the best OpenAI, Claude, Gemini, or open source model per step as new ones ship, without rebuilding the process. A recommended models selector kept current by the Cassidy team makes the right choice easy.
Yes. ChatGPT Workspace Agents can run on a schedule or from an API trigger, and ChatGPT Work can also run scheduled or supported event-triggered tasks. Event-triggered tasks cover supported apps such as Gmail, Slack, and GitHub, and Enterprise admins control whether they are enabled. Cassidy Workflows run on a schedule too and start from CRM changes, meetings, forms, tickets, and any event from the apps you use, with approvals built in and every step visible for audit.
ChatGPT Company Knowledge is a per-conversation tool in ChatGPT web that searches connected apps, shows citations, and respects source permissions. In Enterprise, admins control plugin and app availability. Workspace Agents and Work tasks use the apps, files, and context configured for them. In Cassidy, the Knowledge Base applies automatically to every Agent and Workflow with no per-chat toggle, and source permissions carry through.
Often not for long. Because the same models and chat experience are available inside Cassidy, many teams run everyday chat there alongside governed workflows. Some companies keep ChatGPT for broad employee adoption at first and layer Cassidy on top when a repeatable process needs triggers, routing, approvals, or multi-model orchestration. Over time, some teams consolidate more work in Cassidy, while others keep both.
