GILD verdict
Use Make for work that must run the same way every time across many apps, at volume, with error handling you can inspect.
Use ChatGPT scheduled tasks for judgment work on email, Slack, or GitHub events that a person reviews. When a ChatGPT task needs to act across many systems, have it call a Make scenario through MCP and keep the integration in Make.
Make wins for fixed, repeatable work, where a trigger starts a chain of modules the builder defined and most module runs bill credits. ChatGPT wins for judgment work, planning each task from a saved prompt inside a per-seat workspace. Both can start work on a schedule or an event in another app. ChatGPT can also call Make scenarios as tools through the Make MCP server.
Choosing between Make and ChatGPT
Choose Make if
- The work must run the same way every time across apps ChatGPT does not reach, with retries and error handlers when a step fails.
- Runs are frequent or high volume, and you want module runs billed in credits with no seat allowance to stretch.
- You want one automation layer that any assistant, ChatGPT or Claude, can call through MCP.
Choose ChatGPT if
- The task needs judgment on loosely structured input, such as triaging new email or summarizing a Slack channel, and a person reviews the result.
- Your people already work in ChatGPT Business or Enterprise, and the trigger is a schedule or a Gmail, Slack, or GitHub event.
- You want work to start from a conversation, with ChatGPT drafting the task prompt.
Side by side
| Dimension | Make | ChatGPT |
|---|---|---|
| What starts the work | A trigger module: webhooks start runs instantly, polling triggers check on a schedule down to every minute on paid plans, and MCP clients can run on-demand scenarios | A schedule, or supported Gmail, Slack, and GitHub events on eligible plans; one task cannot combine event triggers with a time-based schedule |
| How the steps are set | Modules, routers, and filters the builder defines; a Make AI Agent decides steps only where the builder places it | ChatGPT plans each run from the saved prompt, using skills and plugins; several matching events may be combined in one run |
| BillingList prices in US dollars. | Credits: 1 credit per module operation for apps without AI, once per bundle the module processes, and 1 per trigger check. Routers and error handlers use none, and Make's AI Provider adds credits by input and output tokens. Core from US$12 a month billed annually for 10,000 credits, for US buyers | Per seat: Business US$20 per user per month billed annually, or US$25 monthly, for 2+ users; included limits extend with ChatGPT credits |
| Apps reached | 3,500+ apps, HTTP modules, and custom apps | Connected apps, plugins that admins allow, and uploaded files |
| When a run fails | Error handlers such as Retry, Resume, and Rollback, and automatic retries of rate limit, connection, and timeout errors when incomplete executions are stored | Runs and findings appear in Scheduled for review; OpenAI advises reviewing the first runs and adjusting the prompt, tools, or cadence |
| Admin controls | Teams and team roles from the Teams plan; audit logs, SSO, and custom roles on Enterprise | Business: SAML SSO, MFA, and admin controls. Enterprise: SCIM, role-based access control, audit logs through the Compliance API, and retention and residency controls; admins can turn event-triggered tasks off |
| Data handling | US or EU data zone per organization; Maia and OpenAI models in Make's AI Provider process in the US; Make does not train Maia's model on customer data | No training on business data by default on Business; Enterprise adds data retention and residency controls |
| Working together | The Make MCP server lets clients such as ChatGPT run active, on-demand scenarios as tools; management scopes need a paid Make plan | Scheduled tasks can use connected tools and plugins, and Make documents ChatGPT as a client of the Make MCP server |
What practitioners report, side by side
Each product's figures come from its own practitioners, for the group you pick.
Overall signal
All users rate ChatGPT +16, 16 points above Make at 0. Most of the gap is in praise, with 31 of ChatGPT's 68 sources positive against 15 of Make's 49. ChatGPT has 19 negative and Make 14.
The score counts the authors from all users that take a side, one vote each with their latest view, and is how far positive votes lead negative ones, per 100 votes, so Make's 0 rests on votes split 14 positive, 20 mixed and 14 negative and ChatGPT's +16 on 28, 17 and 18.
A lead of a fifth of the votes reads positive and a lead of half strong positive, with the same lines below zero. The dots show each source, positive, mixed, negative. Run a pilot on one live workflow to measure failed runs and the time spent fixing them.
Footprint on the AI Knowledge Work stack
They differ on seven of 7 layers. Make owns one that ChatGPT does not, and ChatGPT owns one that Make does not. Coverage is each product's placement on this stack, as its product page shows it. Core is a job the product owns; Supports and Touches are jobs it helps with. Coverage says nothing about how well a product does them.
| Layer | Make | ChatGPT |
|---|---|---|
| Assist | ||
| AI assistant | Touches | Core |
| Find | ||
| Web research | Touches | Supports |
| Enterprise search | Touches | Supports |
| Create | ||
| Documents | Touches | Supports |
| Meeting notes | Touches | Supports |
| Analyze | ||
| Data analysis | Touches | Supports |
| Automate | ||
| Automation | Core | Supports |
What practitioners praise
Among all users, ChatGPT alone has four praise themes that reach 5 sources from 3 authors. Writing & Drafting and Research & Search lead, at 9 of 9 and 8 of 8. Make alone has three praise themes that reach 5 sources from 3 authors. Logic without code and App coverage lead, at 8 of 11 and 3 of 6.
| Praised for | Make | ChatGPT |
|---|---|---|
| Logic without code | 8 of 11 | Not a theme in its sources |
| App coverage | 3 of 6 | Not a theme in its sources |
| AI agents & MCP | 1 of 5 | Not a theme in its sources |
| Writing & Drafting | Not a theme in its sources | 9 of 9 |
| Research & Search | Not a theme in its sources | 8 of 8 |
| Custom GPTs, Projects & Memory | Not a theme in its sources | 7 of 14 |
| Agents & Connectors | Not a theme in its sources | 6 of 12 |
Each count is the sources that praise a theme, or raise it as a concern, out of those that discuss it. A row pairs the two products when both carry a theme under the same or an equivalent name.
A count ending in "so far" has under 5 sources from 3 authors among all users. "Not a theme in its sources" means GILD found no theme of that name in that product's sources.
What practitioners raise as concerns
Among all users, ChatGPT alone has five concern themes that reach 5 sources from 3 authors. Usage Limits & Cost and Accuracy lead, at 8 of 13 and 7 of 10. Make alone has three concern themes that reach 5 sources from 3 authors. Credit metering and Run failures lead, at 14 of 14 and 5 of 7.
| Raised as a concern | Make | ChatGPT |
|---|---|---|
| Credit metering | 14 of 14 | Not a theme in its sources |
| Run failures | 5 of 7 | Not a theme in its sources |
| Learning curve | 4 of 8 | Not a theme in its sources |
| Usage Limits & Cost | Not a theme in its sources | 8 of 13 |
| Accuracy | Not a theme in its sources | 7 of 10 |
| Workspace Administration | Not a theme in its sources | 7 of 10 |
| Data Privacy & Retention | Not a theme in its sources | 5 of 10 |
| Model & Feature Changes | Not a theme in its sources | 4 of 5 |
15 sources
- Make pricing
- Make Help Center: Credits
- Make Help Center: Operations
- Make Help Center: Make MCP server
- Make Help Center: MCP toolboxes
- Make Help Center: Automatic retry of incomplete executions
- Make Help Center: Error handlers
- Make Help Center: AI providers and data zones in Make
- Make Help Center: Maia by Make system card
- Make Help Center: Audit logs
- Make Help Center: Introduction to Make AI Agent (New)
- ChatGPT docs: Scheduled tasks
- ChatGPT docs: Codex and ChatGPT Work pricing
- ChatGPT admin docs: ChatGPT Work overview
- ChatGPT admin docs: Plugin controls