Anfloy vs Lindy.

When Lindy is the right call

• You want an AI agent live this afternoon, and the use case fits a standard pattern.
• Nobody on your team writes code and you don't want to hire.
• The agent only touches SaaS tools that already have Lindy connectors.
• You're prototyping; you'll rebuild it custom if it turns out to be load-bearing.

When Anfloy is the right call

• The agent needs to read your CRM notes, internal Slack threads, or private docs in ways no template covers.
• You want the agent code in your repo so a future engineer can extend it.
• You've already used a no-code tool and hit its ceiling.
• Per-task pricing would eat your margin at scale.
• Compliance/security means your data can't pass through a third-party runtime.

Can Anfloy migrate us off Lindy?

Often, yes. The typical pattern: you prototyped a workflow in Lindy, validated it, hit the ceiling, and now want to own it as custom code. We re-implement the validated flow in your repo, deploy to your stack, and the Lindy seat becomes optional. Most clients keep Lindy for simpler workflows and move the load-bearing agent custom.

Are you more expensive than Lindy?

Per month at low volume, yes - Lindy starts at tens per month, we start at $5,000 per build. At high volume or when you need reasoning their templates can't do, Anfloy is dramatically cheaper over 12 months, especially without per-task fees.

Do you use no-code tools inside builds?

Sometimes. If a sub-step is well-served by Lindy, Make, or Zapier, we'll wire it in. The agent's logic and orchestration live in code, but individual integrations can call out to no-code tools when that's the cleanest path.

Why not just use Lindy and a contractor to extend it?

Because Lindy's extension surface is bounded by what they expose. The moment you need something they don't support - custom retrieval, a private model, an internal tool's API - you're stuck. Custom code has no ceiling.