Lindy vs Relevance AI: No-Code Agent Platforms Compared
Triggers, tools, pricing, and real ceilings compared for Lindy and Relevance AI — two build-your-own agent platforms for operators.
Two platforms, one promise: let non-developers build AI agents that actually do work. Lindy and Relevance AI are the two most credible names in that space right now. Both let you wire up triggers, tools, and LLMs without touching code. But their philosophies diverge in ways that matter for operators deciding where to build.
Here’s a concrete breakdown.
What each platform actually is
Lindy lets you describe an agent in plain English, drop trigger and action blocks onto a canvas, connect apps like Gmail, Slack, HubSpot, Salesforce, and Notion, and the platform stitches it together into an LLM-driven workflow. It positions itself as the layer between a chatbot and a classic Zapier-style automation — smarter than a deterministic recipe, easier to ship than a custom Python agent.
Relevance AI is designed for businesses that want AI to perform repeatable work, not simply answer questions. The platform lets you create specialized agents, give them access to tools and knowledge, connect them into multi-agent teams, and deploy them across sales, marketing, support, and ops. This places it somewhere between an agent builder, a workflow automation platform, and an enterprise AI orchestration system.
Triggers and tool architecture
Lindy uses an event-driven trigger model that most operators find intuitive. A trigger is an event that kicks off your agent — like a new email in your Gmail inbox.
Lindy uses AI to interpret intent and make choices within a workflow. Instead of following fixed triggers, its agents can decide how to route data, summarize content, or flag items for review — helping teams automate work that once required judgment, not just repetition.
Relevance AI builds its architecture around four distinct layers. An agent provides the reasoning layer. Tools give the agent permission to perform actions. Knowledge supplies business-specific context. Triggers determine when work begins. Workforces coordinate multiple agents, tools, conditions, and human approvals. That’s more surface area to configure — but it also means you can build genuinely complex orchestration without reaching for a developer framework like CrewAI or LangGraph Cloud.
Relevance calls its coordination layer a “Workforce” — a team of specialized agents that delegate steps between each other. Instead of one agent doing everything, you build narrow agents (research, enrichment, outreach) and a manager agent delegates between them, which improves reliability for complex workflows.
Integrations
Lindy connects with over 3,000+ business apps including Gmail, Slack, HubSpot, Notion, and Google Calendar. Integrations span core categories such as productivity, CRM, marketing, and project management, giving most teams broad coverage across daily tools.
Relevance AI combines a visual no-code agent builder, an evaluation suite for scenario testing and A/B comparisons, and deployment pipelines with observability, plus enterprise controls including role-based access control (RBAC), audit trails, and connectors to 2,000+ integrations for CRMs, data warehouses, and messaging platforms.
On raw integration count, Lindy has the edge for everyday app connectivity. Relevance AI counters with depth: RBAC, audit trails, and enterprise governance controls that Lindy doesn’t offer at comparable tiers.
Pricing, plainly stated
Lindy starts at $49.99/month (Plus), $99.99 (Pro), and $199.99 (Max).
There is no permanent free tier; Lindy offers a 7-day free trial with full access to Plus features, but once that expires you’ll need a paid plan to continue. The pricing lever that catches operators off-guard: if voice agents are part of your evaluation, costs can climb steeply — calls are billed separately from your credit allowance at $0.19/minute as a starting rate, with each phone number costing an additional $10/month. Model selection matters too: Lindy supports multiple AI models as of late 2025 — Claude Sonnet 4.5 (default), Claude Sonnet 3.7, GPT-5, Gemini Flash 2.0, and Claude Haiku 3.5 — and model selection affects both performance and credit consumption.
Relevance AI restructured its billing significantly in September 2025. The platform split the old “Credits” system into two meters: Actions (what your agents do, with each tool run counting as one action) and Vendor Credits (the underlying LLM compute cost).
The public pricing page now leads with Enterprise “talk to sales,” but self-serve Free, Pro ($19/month annual), and Team ($234/month annual; ~$349 month-to-month) tiers remain available in-app.
Top-ups cost $40 per 1,000 additional Actions and $20 per 10,000 additional Vendor Credits.
The key cost-control lever on Relevance: Pro, Team, and Enterprise plans let you connect your own OpenAI, Anthropic, or Google API keys, bypassing Vendor Credit consumption entirely. For teams already managing their own model spend, that’s meaningful.
Where each platform hits its ceiling
Lindy’s ceiling is complexity. It’s best for founders, ops leads, and small teams with one or two clear workflows. Cost scales with task volume, and complex multi-agent flows require debugging. If you’re running more than a handful of always-on agents or you need fine-grained orchestration between specialized sub-agents, you’ll feel the constraint. For teams exploring that territory, comparing n8n or checking the Lindy vs Relevance AI compare page is worth the time.
Relevance AI’s ceiling is the learning curve and — at scale — cost predictability. It’s not a finished, out-of-the-box product; you have to construct, test, and tweak every process yourself, even starting from marketplace templates. For high-volume, always-on workflows, the usage-based pricing model can get expensive fast.
Meaningful value usually requires upfront integration, policy, and ops work, and some advanced custom behaviors still need engineering support — so it’s not ideal for solo builders or teams without implementation capacity.
Who each platform is actually for
| Lindy | Relevance AI | |
|---|---|---|
| Best for | Ops leads, founders, small GTM teams | GTM orgs, BDR teams, ops teams with some technical capacity |
| Trigger model | Event-driven, plain-English setup | Agents + Tools + Workforces, more configurable |
| Integrations | 3,000+ apps | 2,000+ with deeper enterprise controls |
| Entry price | $49.99/mo (no free tier) | Free tier; $19/mo Pro (annual) |
| Multi-agent orchestration | Limited | Core feature (Workforces) |
| BYOK support | No | Yes (Pro and above) |
For teams also evaluating purpose-built outbound agents, see Artisan or Clay — they sit closer to the execution layer than either platform here.
Bottom line
If you have one or two concrete workflows in mind and want something running this week with no engineering help, Lindy is the faster path. If you’re building a coordinated agent system — multiple specialized roles, delegating tasks, needing audit trails and BYOK — Relevance AI scales further, but budget time to set it up properly and watch both billing meters closely.