Current facts
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Check pricing, packaging, security docs, support limits, AI feature availability, and contract terms directly with the vendor.
Compare = pick the system around the number
Lindy is a no-code platform for building AI assistants — you describe the job in plain English, pick a trigger, and it wires up email triage, scheduling, meeting notes, and simple CRM updates in an afternoon. For standard, self-served automations that is a genuinely fast, cheap way to start. The question this page answers is what changes when the workflow is a revenue lever that has to be right in front of a customer, not just helpful in your inbox.
Quick verdict
Choose Lindy when a non-technical team needs standard assistant work handled fast and cheap — inbox triage, scheduling, note-taking, and light CRM updates — and you are happy to keep an eye on it. Choose a custom AI agent system when the workflow is a revenue lever that needs governed judgment, real evals and observability, deep integration with your own data, and someone accountable when it is wrong. Lindy is a helpful assistant you supervise. A custom build is a production system you can put in front of customers and trust.
Side by side
The dimensions that matter when the stack has to support qualified leads, fast follow-up, clearer pipeline, or better conversion.
| Dimension | AI Agents | Lindy |
|---|---|---|
| What it is | A purpose-built agent system designed and governed around one revenue workflow, delivered done-for-you. | A no-code platform for building AI assistants ("Lindies") from triggers and a template library, set up in plain English. |
| Who builds and runs it | Built, evaluated, and maintained by an outside team. You own the outcome, not the upkeep. | You build and run it yourself. A non-technical team can stand one up in an afternoon, then owns the babysitting. |
| Judgment vs triggers | A governed judgment loop — reads intent, decides the next move, drafts, and stops at the guardrail you set. | Trigger-plus-instruction assistants. Strong on the happy path; judgment gets shaky past roughly five or six steps. |
| Data & integration reach | Wired deep into your proprietary data and systems, only where the workflow needs it, with access planned and logged. | Thousands of prebuilt integrations for breadth. Fast to connect standard apps; deep proprietary-data logic is on you. |
| Governance & evals | Evals, tracing, and human review gates built in, so you can prove it is right often enough to trust. | Light by design. Runs show in an activity feed, but there are no real evals, and weak error handling means failures can pass silently. |
| Pricing shape | Fixed, named build plan plus ongoing inference and maintenance. You know the number before we start. | Starts free, then usage-based paid tiers where every task burns credits, so cost scales with volume. Verify on lindy.ai. |
| Time to value | Weeks. Discovery, build, evals, deployment, and handoff. | Minutes to a first working assistant. Hardening it for real volume and edge cases is the part that takes longer. |
| Best fit | Teams whose workflow is a revenue lever that needs judgment, evals, and an owner accountable for the result. | Non-technical teams automating standard assistant work who want it live today and are fine supervising it. |
Vendor pricing and feature claims change frequently. Verify details directly with each platform before committing.
Verify before buying
Comparison pages are useful only when the buyer knows what to verify. Use this section as the buying checklist before trusting any vendor page, review article, or sales deck.
Current facts
Check pricing, packaging, security docs, support limits, AI feature availability, and contract terms directly with the vendor.
What changes often
AI features, usage caps, add-ons, integration limits, and support tiers can change faster than a comparison page can stay current.
Workflow decision
The right answer depends on the repeat workflow, source data, owner, review step, integration needs, and measurable business result.
Choose AI Agents
Choose Lindy
How we would actually decide
Lindy is a good tool and we are not going to pretend otherwise. If you are a non-technical team who wants email triage, scheduling, notes, and light CRM work handled by this afternoon, describing the job in plain English and letting it run is a genuinely fast, cheap way to get there. For standard, self-served automations it earns its price.
The catch is the gap between "helpful assistant" and "production system." Lindy is strong on the happy path and gets shaky past a handful of steps, error handling is thin, and when something fails it tends to fail quietly — an email that never went out, a CRM record that never updated, no alarm. That is fine for work you are watching. It is not fine when the workflow sits in front of a customer or moves money, because the failure you do not see is the expensive one.
So the honest split is about what the workflow is worth. If it is convenience, supervise an assistant and move on. If it is a revenue lever, you need the parts a self-serve builder leaves out: governed judgment, evals that tell you it is right often enough to trust, observability to catch drift, deep and planned access to your own data, and a person accountable when it is wrong. That is a custom build, not a template.
We do not start with the tool. We start with the workflow that is losing money, decide whether an agent is even the right fix, then build the smallest governed system that moves the number. If you want that read on your own stack before you buy or build, start with a free AI System Plan.
Frequently asked
For standard assistant work, yes. A non-technical team can wire up email triage, scheduling, meeting notes, and simple CRM updates in an afternoon, and for those jobs Lindy is fast and cheap. The limit shows up past the happy path — complex multi-step workflows get unreliable, and error handling is weak, so it can fail quietly. Great for convenience work, riskier for anything a customer sees.
Supervision and accountability. A Lindy assistant fires on a trigger, follows your instructions, and does well on the happy path, but you are the one watching the activity feed and fixing misses. A custom AI agent adds a governed judgment loop, real evals, observability, and deep access to your data — and it comes with someone accountable for the outcome. One is a helpful assistant you babysit. The other is a production system you can trust in front of customers.
Lindy starts free and then moves to usage-based paid tiers. Every task consumes credits, and heavier models cost more credits per task, so your bill scales with volume and can be hard to predict on complex, looping workflows. Pricing has changed more than once recently, so do not anchor on a number from an old review. Verify on lindy.ai.
When the workflow is a revenue lever, not a convenience. If a wrong answer reaches a customer, moves money, or has to hold up to review, you need governed judgment, evals, observability, and someone accountable — and a self-serve assistant does not give you those. Lindy is also the wrong fit when the job runs many steps deep or leans on proprietary data logic, because reliability drops off the happy path. For those, a custom build is the safer call.
Yes, and it is often the smart path. Use Lindy to prototype the workflow cheaply and prove an assistant is worth it, then harden the parts that matter with evals, observability, and human review gates. Nothing is wasted — the prototype tells you exactly what the governed build has to do and where it has to be reliable. A free AI System Plan tells you which stage you are at.
Answer two questions. (1) Does this workflow need governed judgment and evals because a wrong answer reaches a customer or costs money, or is it convenience work you are happy to supervise? (2) Do you want it live today and self-served, or delivered and maintained with someone accountable? If it is convenience and you want it now, Lindy is a strong pick. If it is a revenue lever and you would rather not babysit it, build custom. If you want a no-pitch second opinion, contact us.
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Next step
If the wrong stack is slowing response speed, qualification, handoff, or reporting, the AI System Plan tells us whether a build should exist.