Fleece AI vs Make: stop building scenarios, start delegating
Make turns you into the workflow engineer, assembling and maintaining every scenario on a canvas. Fleece gives you autonomous agents that work out the steps themselves — with judgment, approval gates, and a team structure. Here is the feature-by-feature comparison.
7-day trial · Cancel anytime
Fleece AI is the strongest Make alternative for teams that want outcomes instead of building the machine. Make is a visual scenario builder — you assemble modules, routers, and iterators on a canvas and maintain every branch yourself, with each module execution metered. Fleece runs autonomous agents that read context, handle the exceptions that stall a scenario, and coordinate your whole stack from one plain-language brief — in hierarchical teams, under one-click approval gates, on flat monthly plans.
By Loïc Jané · Updated June 25, 2026
One builds scenarios. The other does the work.
Make, formerly Integromat, is a visual automation platform, and a capable one: you build a scenario on a drag-and-drop canvas where each app action is a module, routers split the flow into branches, and iterators and aggregators reshape data along the way. It connects to 2,000+ apps, its data mapping is unusually powerful, and operations-based pricing keeps it cheap at low volume. But the model puts you in the engineer's seat — you assemble every scenario, own every branch, and pay by the operation as each module runs. AI arrived as steps bolted onto that same scenario model.
Fleece AI starts where that model gets heavy. You describe the job to an agent in plain language — "triage my support inbox, escalate outages, file real bugs in GitHub" — and the agent works out the steps, with an LLM at the core of every run. Agents connect to 3,000+ apps through managed OAuth, react to real-time triggers or run on a schedule, and drive a real browser for tools without an API. A lead agent delegates to specialized child agents, and approval gates keep anything sensitive behind your one-click sign-off.
The difference shows up as your logic grows. On a canvas, every branch reality might take is a router you draw and a case you debug by hand; complex scenarios become a diagram you maintain forever. A Fleece agent reads the actual message, decides, and handles the case in between the rules. And for the truly fixed, data-shaped tasks Make excels at — map these fields, move these rows on a schedule — a scheduled Fleece flow covers those too, without the upkeep. You don't need a second tool for the simple cases.
Fleece AI vs Make at a glance
The short version: Make builds the scenario you assemble and maintain on a canvas; Fleece delivers the outcome you delegate.
| Criterion | Fleece AI | Make |
|---|---|---|
| Core model | Autonomous AI agents that reason about every run | Visual scenarios you assemble from modules and routers |
| Setup | Describe the job in one plain-language brief — no canvas | Build each module, router, and mapping on a canvas yourself |
| Exceptions and edge cases | The agent reads context, adapts, or asks for approval | Every branch needs an explicit router; unplanned cases stall or error |
| Team of agents | Hierarchical teams — a lead agent delegates to specialists | No equivalent — one scenario per job |
| Integrations | 3,000+ apps via managed OAuth + browser automation for the rest | 2,000+ connectors, limited to what each module exposes |
| Human control | Autonomy levels + one-click approval gates on sensitive actions | Approval steps must be placed inside each scenario by hand |
| Maintenance | Update the brief; the agent adapts | Every process change means editing the scenario and its branches |
| AI | LLM-native — judgment is the engine of every run | AI added as steps on top of the scenario model |
| Pricing model | Flat monthly plans — predictable at any volume | Operations-based metering — every module execution counts |
| Best for | Delegating whole workflows, messy and simple alike | Fixed visual pipelines, if you accept building and maintaining them |
Choose Fleece AI if…
- You want to delegate an outcome in one sentence instead of assembling and maintaining a scenario per job.
- The work involves judgment — triage, drafting, classification, exceptions — where a fixed canvas of modules breaks.
- You want one agent (or a team of agents) running a whole workflow across Slack, Gmail, your CRM, and your docs.
- You want real control without babysitting: autonomy levels, one-click approval gates, and a step-by-step record of every run.
- You prefer a predictable flat plan over per-operation metering that grows with every module your scenarios run.
Where Make still makes sense
- You genuinely enjoy building visually and your pipelines are truly fixed — though a scheduled Fleece flow covers fixed tasks too, without a canvas to maintain.
- You run extremely budget-constrained, high-volume trivial automations where Make's per-operation economics win — though flat plans stay predictable as that volume grows.
- You have a stable library of scenarios you'll retire only gradually — they can keep running while Fleece takes on the judgment-heavy work.
Switching is smaller than it looks
One Fleece agent typically replaces several related scenarios, because it holds the whole workflow — triage plus drafting plus escalation — in a single brief instead of separate canvases. Start with the scenario that keeps breaking on new branches or never fit cleanly on the canvas, run it in Fleece during the 7-day trial, and compare on your own data. Most teams then move the rest at their own pace; nothing forces a big-bang migration.
Frequently asked questions
Delegate a real workflow and compare
Connect your tools, brief an agent in plain language, and run it next to your scenarios for a week. 7-day trial, cancel anytime.
Powered by Fleece AI · autonomous agents for 3,000+ apps