Best Lindy Alternatives in 2026: 13 AI Agent Builders for Teams That Outgrew Credits

Updated August 2026

If you're reading this, Lindy probably worked, until it didn't. Maybe the shared credit pool ran dry three weeks into the month with a busy inbox agent still going. Maybe adding a fourth teammate meant a fourth seat, not a bigger pool to draw from. Maybe the job needs an app catalog Lindy doesn't have, a self-hosted deployment your security team will actually approve, or branching logic that outgrew the "AI employee" framing entirely. This guide ranks 13 real alternatives against those specific walls, not a generic feature checklist, and orders them by which wall each one solves best.

Every price below comes from the vendor's own pricing or docs page, checked in August 2026, with monthly and annual rates called out separately since that's where most "wrong price" complaints start. Where a vendor no longer publishes a self-serve number, that's stated plainly, not guessed at.

Key Facts

• Enterprises buying ready-made AI agent solutions instead of building their own rose from 53% in 2024 to 76% in 2025, per Menlo Ventures' State of Generative AI in the Enterprise.
• Only 16% of what companies call an AI agent in production actually plans, observes, and adapts on its own, per the same Menlo Ventures report; the rest are largely fixed-sequence workflows wearing agent branding.
• Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027, citing unclear business value and inadequate risk controls as the leading causes.
• 57% of organizations now have AI agents running in production, rising to 67% among enterprises with 10,000 or more employees, per LangChain's State of Agent Engineering survey of 1,340 practitioners.
• The average enterprise runs 897 applications and only 29% are integrated with each other, per MuleSoft's Connectivity Benchmark Report, one reason an agent builder's app catalog often decides whether it can act on anything at all.

Why Teams Look Beyond Lindy

Lindy earns its popularity honestly. Handing an entire job function, like inbox triage or call screening, to one AI "employee" is a faster on-ramp than assembling a workflow step by step, and for a founder or a five-person team it's often the right first move. See how to build an AI agent with Lindy for what that setup looks like, and best AI agent platforms for the full three-class breakdown this guide sits inside. The reasons teams look elsewhere tend to cluster into four walls:

1. The credit pool runs out faster than expected. Voice, research, and multi-step runs cost more credits than a single Slack message, and Lindy doesn't publish one flat rate.
2. Credits and seats are welded together. Each seat does add its own credits to the shared workspace pool, but Lindy publishes no way to buy more credits without buying another seat, so the only lever for more capacity is more headcount on the bill.
3. The workflow needs data residency or self-hosting. Regulated teams, or anyone wanting agent logic on infrastructure they control, outgrow a cloud-only "employee" model fast.
4. The logic outgrew "AI employee" framing. Complex branching or deep grounding in one system of record needs real orchestration, not a single delegate.

What a Lindy credit actually buys

Lindy doesn't publish one fixed credit cost per action, since price scales with which AI model runs the task, how many steps it takes, and whether voice is involved. What is published and verifiable is the tier structure itself:

Source: lindy.ai/pricing, verified August 2026.

The per-credit rate actually drops as you move up tiers, so the real squeeze is volume and seats together, not the sticker price. Here's that arithmetic for two illustrative workloads, using the range third-party trackers report for Lindy's per-action cost (roughly 1 to 3 credits for a simple action, 5 to 10 or more for a multi-step run on a premium model). Lindy doesn't publish the exact rate, so treat these as estimates built on public tier numbers, not a vendor-confirmed guarantee:

Voice adds a separate meter on top: each phone number carries a flat monthly fee, and calls burn credits per minute regardless of tier. Seats and credits move together: two people on Pro is $199.98 a month for 30,000 pooled credits, since every seat contributes its own allocation to one workspace pool. That cuts both ways. You are not paying twice for the same credits, but you also cannot top the pool up without adding another seat. See AI agent cost optimization for the fuller framework on modeling this before you commit.

Quick Comparison Table

Sizing and Stage Fit

The useful dividing line is not company size alone. It is how much orchestration, data control, and ongoing technical ownership the team can support.

No-Code Delegate Builders (Closest to Lindy's Framing)

These two keep Lindy's core pitch, a single AI "employee" you delegate a job to, but change how the meter works underneath.

1. Relevance AI: Transparent Dual Metering Instead of One Pool

Relevance AI's bet is legibility: instead of one opaque credit pool, it splits cost into two meters, Actions (how many times a workflow runs) and Vendor Credits (the model compute those runs consume). For a team that got burned not knowing which part of a Lindy bill came from volume versus model, seeing those numbers separately is the appeal. What changed in 2026: Relevance AI's marketing page (relevanceai.com/pricing) now leads with enterprise, sales-gated messaging, so the self-serve numbers below come from its docs page instead.

Pricing: Free ($0, 200 Actions/month, $2 one-time bonus Vendor Credits). Pro $19/month billed annually or $29/month billed monthly (2,500 Actions/month, $20 Vendor Credits/month). Team $234/month billed annually or $349/month billed monthly (7,000 Actions/month, $70 Vendor Credits/month). Enterprise custom. Extra Actions cost $80 per 1,000; extra Vendor Credits cost $20 per 10,000. Source: relevanceai.com/docs/get-started/pricing.

Best for: Teams that liked Lindy's AI-workforce framing but want to see task volume and model cost as two separate, legible numbers instead of one shared pool. For a deeper head-to-head, see best Relevance AI alternatives.

2. Bardeen: The Cheapest Way to Delegate From Inside the Browser

Bardeen's angle is narrower and cheaper than most of this list: it runs as a browser extension that delegates tasks directly inside the tabs you already have open, LinkedIn, Gmail, spreadsheets, without a separate app to configure. For a solo operator or small team whose "agent" work is mostly web-based research and data entry, that's a lighter on-ramp than a full workflow platform.

Pricing: Free tier included with a starting credit allotment. Basic $10/month (100 credits/month). Premium $50/month or $480/year (1,000 credits/month, or 12,000/year on the annual plan, about a 20% discount). Enterprise custom, billed annually. Source: bardeen.ai/pricing.

Best for: Solo operators and small teams whose delegated work is mostly browser-based research, enrichment, and data entry, and who want the cheapest possible paid entry point.

Workflow-Automation Platforms That Added Agents

These platforms started as automation tools and added an agent layer on top, trading some of Lindy's "single employee" simplicity for a wider app catalog or more control over where the workload runs. See no-code vs code AI agents for that tradeoff, and best no-code AI agent builders for a ranking focused only on this class.

3. Zapier Agents: Widest App Catalog for an Agent to Call as Tools

Zapier's bet is that the fastest way to a working agent is giving it access to the automation platform teams already run. Zapier Agents sits atop core Zapier, letting an agent reason over a task and call any of its 9,000-plus app integrations as a tool, rather than follow one fixed trigger-then-action sequence. For a team that left Lindy because it needed an integration Lindy doesn't have, this is usually where it lives.

Pricing: Zapier Agents: Free (400 agent activities/month). Pro $400/year, about $33/month (1,500 activities/month). Enterprise custom. Core Zapier, which Agents runs on top of: Free (100 tasks/month), Professional from $19.99/month annual, Team from $69/month annual. Source: zapier.com/pricing.

Best for: Teams already automating on Zapier that need an agent to reach into an app catalog wider than Lindy's. If this is your shortlist, Lindy vs Zapier Agents works the two meters against each other at realistic volumes.

4. Make: AI Agents Built Into the Visual Canvas

Make's bet is that agent reasoning should live in the same visual Scenario Builder teams already use, not a separate bolted-on product. AI Agents ship on every paid plan, no extra SKU the way Lindy or Zapier price agent capability separately. For teams that want to see exactly which step an agent took and why, Make's visual debugging shows every decision inline.

Pricing: Free ($0, 1,000 credits/month, 2 active scenarios). Core $9/month billed annually (10,000 credits/month). Pro $16/month. Teams $29/month. Enterprise custom. Source: make.com/en/pricing.

Best for: Technical-leaning ops teams that want agent reasoning inside a visual canvas without paying for a separate agent product on top.

5. n8n: Self-Hosted Control With Full LangChain Access

n8n's philosophy is that automation infrastructure should be something you own outright, not rent. Every plan, including the free self-hosted Community edition, ships the same AI Agent node, built on LangChain primitives, wired up visually or extended with raw JavaScript. For a team leaving Lindy specifically for data residency or self-hosting, n8n is usually the first stop. Building an AI agent with n8n covers the setup.

Pricing: Community edition free, self-hosted, unlimited executions. Cloud Starter about €20/month annual (roughly $22 USD; 2,500 executions/month, 2,300 AI credits/month). Cloud Pro about €50/month annual (roughly $54 USD; 10,000 executions/month, up to 13,700 AI credits/month). Business about €667/month annual (roughly $720 USD), self-hosted with SSO/SAML (40,000 executions/month). Enterprise custom. Source: n8n.io/pricing.

Best for: Technical teams and regulated industries that left Lindy specifically for data control, and want agent workflows running on infrastructure they own.

6. Gumloop: Visible Per-Node Cost for Technical Teams

Gumloop's pitch sits between a no-code builder and a developer tool: workflows are built node by node, but each node's model and cost are visible and swappable, including bringing your own API key to bypass Gumloop's markup entirely. That transparency directly answers the "where did my credits go" complaint that drives people off Lindy.

Pricing: Pro $37/month (20,000 credits/month, unlimited agents, 5 concurrent workflow runs, 25 concurrent agent chats, 8% orchestration fee). Enterprise custom, with volume discounts on the orchestration fee. Source: gumloop.com/pricing.

Best for: Technical ops teams that want node-level visibility into what's consuming credits, with the option to bring their own model keys.

Vertical and Simple No-Code Builders

7. Clay: Purpose-Built for GTM Research and Outbound

Clay isn't a general-purpose agent builder, and that's the point. If Lindy's job was mostly lead research, enrichment, and outbound personalization, Clay does that one job with far deeper data sources than a general delegate, at the cost of being the wrong tool for anything outside GTM.

Pricing: Free ($0, 500 actions/month, 100 data credits/month). Launch from $167/month (15,000 actions/month, 3,000 data credits/month). Growth from $446/month (40,000 actions/month, 6,000 data credits/month). Enterprise custom, annual commitment required. Source: clay.com/pricing.

Best for: Sales and marketing teams whose Lindy use case was mostly research, enrichment, and outbound, not general job delegation.

8. MindStudio: The Simplest No-Code Builder, Pay-Per-Use Tokens

MindStudio strips the model away from the markup: agents run on whichever of 200-plus AI models you pick, billed at cost with no MindStudio surcharge, while the platform fee stays flat regardless of agent or run count.

Pricing: Free ($0 plus usage, 1 agent, 1,000 runs/month). Individual $20/month, or $16/month billed annually (unlimited agents and runs, usage billed separately). Business custom. Source: mindstudio.ai/pricing.

Best for: Small teams that want the simplest possible no-code builder and are comfortable paying model costs directly instead of through a credit markup.

Knowledge-Work and Enterprise-Lite Platforms

Built for retrieval over a company's own documents and data, closer to an internal knowledge layer than a single delegated employee.

9. Stack AI: Retrieval-Native Workflow Builder

Stack AI's core strength is grounding agents in a company's own documents and data through native retrieval-augmented generation, aimed at internal knowledge work rather than general task delegation. What changed in 2026: Stack AI's pricing page now shows only two tiers, Free and Enterprise. Third-party trackers report a Starter tier around $199/month and a Team tier around $899/month, but neither figure appears on the vendor's own page, so treat those as unconfirmed.

Pricing: Free ($0, 500 runs/month, 2 projects, 1 seat). Enterprise custom (dedicated infrastructure, solution engineers, on-premises and VPC options, SSO). Source: stackai.com/pricing, verified August 2026.

Best for: Teams whose Lindy use case was really internal knowledge retrieval, and who are prepared to go straight to an Enterprise conversation once the free tier is outgrown.

10. Dust: Company-Wide Knowledge Agents, Not One Delegate

Dust's model is seat-based rather than employee-based: every person on the team gets a seat with a bundled credit budget, drawing on 20-plus models from OpenAI, Anthropic, Google, Mistral, and DeepSeek, aimed at giving a whole company access to grounded agents rather than delegating one job to one bot. What changed in 2026: Dust moved to this credit-metered, per-seat structure mid-year, from its earlier unlimited fair-use model.

Pricing: Free ($0, 500 credits, lifetime allotment). Pro $30/month, or $24/month billed annually (8,000 credits/month). Max $150/month, or $120/month billed annually (40,000 credits/month). Enterprise custom. Source: dust.tt/home/pricing, cross-checked against docs.dust.tt/docs/subscriptions.

Best for: Companies that want every employee equipped with grounded agents drawing on the same knowledge base, not one delegate handling one function.

Enterprise Managed Suites

For a team already standardized on Microsoft or Salesforce, the ecosystem you're already paying for is usually a stronger agent platform than any standalone tool. See enterprise AI agent platforms for a ranking focused only on this class.

11. Microsoft Copilot Studio: Microsoft 365 and Azure Grounding

Copilot Studio's core strength is grounding an agent in whatever an organization already has in SharePoint, Dataverse, and Microsoft Graph, with no separate data pipeline to build. Billing runs on Copilot Credits, pooled at the tenant level rather than metered per user the way Lindy's seats work, efficient at scale but harder to estimate upfront.

Pricing: Copilot Studio user license free. Tenant Copilot Credits: a prepaid pack of 25,000 credits costs $200/month on an annual commitment, about $0.008 per credit, or pay-as-you-go via Azure at roughly $0.01 per credit with no commitment. Source: Microsoft's official Copilot Studio licensing documentation.

Best for: Microsoft 365 and Azure-standardized organizations that want agents grounded in data they already store there, without per-seat credit silos.

12. Salesforce Agentforce: Deepest Native CRM Grounding

Agentforce reasons directly over live Salesforce data, accounts, cases, opportunities, with no integration layer in between. For a Salesforce shop that adopted Lindy for CRM-adjacent tasks, Agentforce closes that gap entirely. The catch in 2026 is pricing complexity: Agentforce runs on two separate, incompatible pricing models that can't operate in the same org at once.

Pricing: Flex Credits model, $500 per 100,000 credits (one standard action equals 20 credits, about $0.10), for employee and voice use cases. Enterprise Edition orgs get 100,000 free credits via Salesforce Foundations. Source: Salesforce's official Agentforce pricing help article.

Best for: Salesforce-standardized sales and service orgs automating work that already lives inside CRM records. For a deeper head-to-head against Agentforce specifically, see best Agentforce alternatives, and for Copilot Studio, best Copilot Studio alternatives.

Open-Source and Developer Framework

Open-source frameworks remove the packaged credit meter, but the trade is direct ownership of orchestration, model selection, deployment, and maintenance.

13. CrewAI: Zero Credit Meter, Full Engineering Control

CrewAI is the one entry on this list with no credit meter at all. It's an open-source Python framework you install and code against: define agents by role (researcher, writer, reviewer), give each a goal and tools, and let CrewAI handle handoffs. For a team that outgrew "AI employee" framing and has engineering capacity to spend, this is the escape hatch from per-seat and per-credit pricing altogether. See open-source AI agent frameworks for a ranking focused only on this class.

Pricing: Open-source Python framework free (MIT license). Hosted platform Basic tier free (50 workflow executions/month, visual editor, AI copilot, GitHub integration). Enterprise custom, adds SSO, RBAC, workload identity, and PII redaction. Source: crewai.com/pricing.

Best for: Engineering teams that have fully outgrown no-code delegation and want to own the logic, the model choice, and the cost, without a credit meter anywhere in the stack.

How to Choose: Decision Framework

For a broader checklist beyond just Lindy switches, see how to choose an AI agent platform.

What to Do Next

Pick the wall that's actually blocking you, credit burn, seat pricing, data control, or outgrown logic, and run a two-week pilot with the single alternative that solves it best, using one real workflow you're already running in Lindy today. Measure whether it finishes that job correctly without a human rescuing it at every step, and check the actual bill against the credit math worked out earlier in this guide. That tells you more about fit than a longer shortlist would.