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Lindy vs Make
A plain-English comparison to help you choose between them.
Delegation against construction, again with a twist of scale: Lindy hands a described job to an agent, Make hands you a canvas to build the process yourself. Pick Lindy when the work is a recurring administrative loop with acceptable variation, inbox triage, scheduling, CRM upkeep, described in sentences and run on triggers. Pick Make when the automation has real logic and real volume, branching, iteration and error paths drawn across thousands of apps, priced by credits so sophisticated flows stay affordable, with in-scenario agents where a step needs judgement.
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Side by side
- Summary
Lindy is a platform for building AI agents that do real work across your existing tools. Each agent, a Lindy, is set up in plain language rather than code: describe the job, connect the apps it needs, such as email, calendar, a CRM or Slack, and set the rules it must follow.
Agents run on triggers rather than waiting to be asked. Typical deployments triage and draft email, schedule and reschedule meetings, prepare briefs before calls, take notes and send follow-ups, keep HubSpot or Salesforce records current and chase leads. A library of prebuilt templates covers the common jobs, so a first agent is usually assembled rather than designed.
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Lindy has moved from a free-plan model to subscription tiers with a short trial, and now presents itself as an AI assistant for the whole work loop rather than a single-purpose bot. It suits people who want delegation, not another dashboard.
- Best for
- Delegating whole jobs such as inbox triage or meeting scheduling to an agent
- Building agents in plain language without code or flowcharts
- Keeping CRM records current from email and meeting activity
- Meeting preparation, notes and follow-ups handled end to end
- Starting from prebuilt agent templates rather than a blank canvas
- Cost
- Paid only
- Ease
- Openness
- Hosted service
- Data
- Agents hold live access to email, calendar and connected tools; grant scopes per agent, per job.
- Summary
Make is visual automation with engineering sensibilities: scenarios built on a canvas where branching, loops, error handling and data transformation are first-class, connecting thousands of apps at credit-based pricing that undercuts the per-task platforms at volume.
AI has become native: agents live inside the scenario builder as reusable, shareable components, with their reasoning and tool calls visible step by step, and a real-time visual map keeps a growing automation estate comprehensible.
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It rewards builders who think in flows: more capable than the simplest platforms, more approachable than code, with credit billing that still compounds at serious scale.
- Best for
- Branching, loops and error handling built visually
- High-volume automation at credit-based pricing
- Reusable AI agents inside scenarios, reasoning visible
- Data transformation between connected apps
- A visual map of the whole automation estate
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Hosted service
- Data
- Per Make's Help Center, "effective august 27th, 2025, we're replacing operations with credits as our billing unit," with existing operations converting 1:1. Standard modules stay at 1 credit, but native AI modules consume credits variably: per one 2026 review, "A workflow with AI Agents can consume 43-50 credits per execution (Small model), versus the few credits of a classic workflow." Extra credits cost 25% more than in-plan (Help Center, updated 6 Nov 2025), for both manual and auto-purchase. The sticker price is not the bill; consider calling AI APIs directly via HTTP for cost control.
Pricing
- Lindy
$49.99·$99.99·$199.99·Custom
Prices as of August 2026.
- Plus
- $49.99per month
- Pro
- $99.99per month
- Max
- $199.99per month
- Enterprise
- Price on applicationno list price published
- Make
Free·from $9·from $16·from $29·Custom
Prices as of August 2026.
- Free
- Free
- Core
- from $9per monthbilled annually; $10.59 per month if billed monthly
- Pro
- from $16per monthbilled annually; $18.82 per month if billed monthly
- Teams
- from $29per monthbilled annually; $34.12 per month if billed monthly
- Enterprise
- Price on applicationno list price published
By area
Where each one pulls ahead, area by area.
| Area | Lindy | Make |
|---|---|---|
| By job | ||
| AI agents & automation | Lindy starts from a template, so the first agent is usually assembled rather than designed and the familiar administrative loop is covered on day one | Make draws the branching, iteration and error paths rather than working around them, so the shape of the process is on the canvas instead of in the agent's head |
| By task | ||
| Automation & agents | Lindy prepares the brief before a call and sends the follow-up afterwards, running lead sequences with human checkpoints inside them | Make is the visual power tool of mainstream automation, where the process is drawn as a flowchart instead of described in sentences |
Common questions
Which failure tolerance fits which tool?
Lindy's judgement is a feature where variation is acceptable and a liability where it is not, its own framing, so pipelines that must behave identically on every run belong on the workflow platforms, Make included. Make's determinism cuts the other way: error handling exists but needs actually configuring, and a scenario nobody documented is production infrastructure with a bus factor of one.
Who does the building on each?
Lindy asks for a manager: describe the job, connect the tools, set the rules, start from a template, then review while trust builds, since agents act on live email and customer records. Make asks for a builder, someone who enjoys constructing the machine and thinks in flows, with the visual form surviving handover better than a script but still deserving documentation.
How do their meters differ?
Both scale with activity, on different axes. Lindy's costs track how much the agents actually do, so always-on delegation deserves a look at plan allowances before running unattended. Make's credits track volume through scenarios, cheaper than task-priced rivals at scale yet compounding at the extreme. Neither punishes trying; both punish scaling without arithmetic.
Related comparisons
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Tool facts last checked August 2026