10 Best Relevance AI Alternatives in 2026

Relevance AI helps teams build agents that handle work across tools, from researching leads to updating a CRM. As you plan those workflows, the next question is how much they’ll cost to run.

That makes pricing an important factor when considering Relevance AI alternatives. Its public pricing page highlights an Enterprise offering with custom pricing, while its documentation still lists Pro and Team plans with an in-app upgrade path. If you’re comparing options on a set budget, published plans and clear usage rates can make that decision easier.

This guide compares 10 AI agent builders, covering their capabilities, pricing models, and limitations. Whether you want clearer costs, more control over hosting, or agents that work alongside your projects, you’ll find options to match your team’s needs.

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Relevance AI Alternatives at a Glance

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What Should You Look for in a Relevance AI Alternative?

A good Relevance AI alternative lets a non-developer build an agent that runs on a trigger or a schedule and takes actions in connected apps. Every tool on this list passes that test. Use these questions to narrow the field, and see our roundup of AI agents for no-code users if your builders have no technical background.

• What does one unit of billing cover? Find out whether you pay per tool call, per workflow run, per credit, or per seat, then model one real agent against it
• What happens when the meter runs out? Check for a grace period, an automatic top-up, or a hard stop that pauses agents mid-task
• Can you bring your own model keys (BYOK)? BYOK moves model costs to your own provider bill, which helps when model usage drives the spend
• Where does the agent get its context? Scoping an agent to relevant, permissioned sources can reduce unnecessary retrieval steps. Check whether the platform connects natively to your data or relies on external API calls
• Who maintains the agent after launch? A builder only your one technical teammate understands becomes a bottleneck the first time a workflow breaks
• Can you see cost per agent? Per-agent cost reporting tells you which agent to fix before the invoice does

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How Much Does Relevance AI Cost, and Why Do Teams Switch?

Relevance AI’s public pricing page highlights Enterprise plans with custom pricing. Its official documentation still lists Pro at $29/month and Team at $349/month, with an in-app upgrade path described in the docs.

According to the documentation, Pro includes 2,500 Actions and $20 in Vendor Credits per month, while Team includes 7,000 Actions and $70 in Vendor Credits. Extra Actions cost $80 per 1,000. Paid users can also bring their own model API keys to bypass Vendor Credits, though they’ll still pay their model provider directly.

The costs become clearer when you look at how Actions are counted. Each time an agent runs a tool, Relevance AI counts one Action, even if the tool fails.

Say your agent researches a lead, checks your CRM, drafts an email, and logs the result. If each step uses one tool call, that’s four Actions per lead. At 100 leads a day over 20 working days, you’d use 8,000 Actions.

That’s 5,500 more than Pro’s monthly allowance. Since top-ups come in blocks of 1,000, you’d need to purchase another 6,000 Actions for $480. Your total would reach $509 for the month, before any additional model costs.

This concern extends beyond Relevance AI. In June 2025, Gartner predicted that over 40% of agentic AI projects would be canceled by the end of 2027, citing rising costs, unclear business value, and inadequate risk controls. Gartner Senior Director Analyst Anushree Verma warned that hype-driven experiments obscure what agents actually cost to run:

This can blind organizations to the real cost and complexity of deploying AI agents at scale, stalling projects from moving into production. They need to cut through the hype to make careful, strategic decisions about where and how they apply this emerging technology.

When comparing alternatives, calculate the cost of a typical workflow at your expected monthly volume. Include tool calls, retries, model usage, and any fees for exceeding plan limits. That gives you a high-level view of what you’ll spend as your agents take on more work.

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The Meter-Fit Test: Match the Billing Unit to How Your Agents Work

AI agent builders often offer similar features, but they don’t charge for the work in the same way. An agent that calls 10 tools in one run could cost much less on one platform than another, depending on what each provider counts.

Before comparing features, look at how your busiest agents operate:

• Agents with many tool calls: If your agent researches leads, checks records, and updates several apps in one run, execution-based pricing like n8n’s may keep platform costs down. External AI model charges can still vary
• Agents that run occasionally: For tasks like weekly reports or contract reviews, usage-based plans may be enough. Check how many credits each run consumes, particularly when the work involves large documents or complex AI models
• Knowledge agents: If several people use agents to search company data, look at per-seat allowances and shared credit pools. Compare how many questions your team asks and how much research each answer requires

Here’s how Relevance AI and the 10 alternatives measure usage:

These figures give you a starting point, but the number of credits alone won’t tell you which platform is cheaper. Test the same workflow on your shortlisted tools and compare the cost per completed task. Include retries, AI model charges, and hosting costs where applicable.

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The 10 Best Relevance AI Alternatives

1. n8n (Best for technical teams that want flexible hosting and multi-step agent workflows)

n8n’s AI Agent node gives a language model access to tools and memory, allowing it to decide which tools to call. The agent sits inside a regular n8n workflow, which can start from a webhook, schedule, or form submission. Other nodes prepare the data going into the agent and handle its output.

The billing model is a big draw for teams leaving Relevance AI. n8n charges per workflow execution, regardless of how many steps it contains. An agent that qualifies a lead, checks HubSpot, writes a reply, and posts to Slack counts as one execution when those actions run within the same workflow.

There’s also the Community Edition, which you can self-host for free. It gives technical teams control over where their workflows run, plus the option to customize integrations and use their preferred AI models.

Standout features

• Execution-based billing: A multi-step workflow counts as one execution, no matter how many steps it runs. External AI model costs may still vary
• Code nodes: Drop JavaScript or Python into a workflow when a built-in node can’t shape the data the agent needs
• Self-hosted data: Keep prompts, tool outputs, and customer records on your own infrastructure for compliance reviews
• Community workflows: Start from shared agent templates instead of wiring tools from a blank canvas

Pricing

• Free trial
• Starter: €20/month (billed annually)
• Pro: €50/month (billed annually)
• Business: €667/month (billed annually)
• Enterprise: Custom pricing

Note: n8n also offers a free, self-hosted Community edition, separate from its Cloud trial. Hosting, maintenance, and any paid AI model usage will be your responsibility

Ratings

• G2: 4.7/5 (300+ reviews)
• Capterra: 4.6/5 (40+ reviews)

Where it taps out: Self-hosting trades subscription costs for engineering time. Teams without someone comfortable with Docker, SSL, and server updates often move to n8n Cloud or to one of the n8n alternatives with managed hosting.

What real-life users are saying about n8n

A user review says:

2. Gumloop (Best for ops teams building AI-heavy workflows on a visual canvas)

Gumloop lets you build AI agents and workflows on a drag-and-drop canvas. Model calls have their own nodes, just like regular automation steps. You can connect them to other tools and decide how data moves through the flow.

For example, a flow can read rows from Google Sheets, classify each entry with an AI model, and write the results back. You can also use several models within a workflow. The output from one model becomes the input for the next, which gives you room to build more detailed AI processes.

Gumloop supports multiple AI models and handles access through its credit system. There’s no need to set up a separate account with each model provider. Your operations, sales, and support staff can work in the same organization and draw from a shared credit pool.

Standout features

• Error handling: Catch failed steps and configure paths to retry or reprocess affected data
• Subflows: Split complex automations into smaller workflows that can be reused across different processes
• MCP nodes: Connect agents to compatible external tools and services through the Model Context Protocol
• Chat-based agents: Run supported agents through conversations in connected interfaces such as Slack

Pricing

• Free trial: 14 days
• Pro: $37/month
• Enterprise: Custom pricing

Note: Pro includes 20,000 credits per month. The trial requires a credit card and converts to a paid Pro subscription unless canceled.

Ratings

• G2: Not enough reviews
• Capterra: Not enough reviews

Where it taps out: Teams with formal procurement requirements should check whether Gumloop’s security controls and deployment options meet their needs. Compare those requirements across the platforms in our AI agent tools roundup.

What real-life users are saying about Gumloop

A user review says:

3. Zapier Agents (Best for agents that act across a broad SaaS stack)

Zapier Agents lets you describe a job in plain language, connect the apps the agent may use, and let it decide the steps. The agent uses the same app connections as Zapier’s Zaps. That’s the main reason to pick it. If your CRM, help desk, and billing tool are niche, Zapier probably already connects to them.

Consider a customer success manager watching for downgrade requests in a help desk. The agent pulls the account’s usage from a product analytics tool. Then it drafts a retention offer in the CRM for human review. Each of those actions is a Zapier activity, and the agent can hand off to existing Zaps for the steps that never change.

Zapier is moving its standalone Agents product into AI by Zapier, bringing agent features into the Zap editor. Standalone Agents has separate plans, while the newer experience uses task-based billing. Check which experience you’ll use before comparing costs.

Standout features

• Agent chaining: Let one agent call another to split a larger assignment into smaller tasks
• Web browsing: Give agents access to current information from the web when completing a request
• Live data sources: Bring information from supported connected apps into agent conversations
• Chrome extension: Interact with agents directly from the browser

Pricing

• Free plan available, with 400 activities/month
• Agents Pro: $33.33/month, billed annually, with 1,500 activities/month
• Agents Enterprise: Custom pricing

Note: These plans apply to standalone Zapier Agents. AI by Zapier uses task-based billing.

Ratings

• G2: 4.5/5 (2,000+ reviews, Zapier platform)
• Capterra: 4.7/5 (3,000+ reviews, Zapier platform)

Where it taps out: Agents that call several tools can use their activity allowance quickly. The Free plan also limits individual runs to 10 activities, while Pro allows up to 40 per run.

What real-life users are saying about Zapier

A user review says:

4. ClickUp (Best for agents that work inside the same workspace as your tasks and docs)

ClickUp Super Agents are AI teammates you can build with plain-language instructions. They work with your ClickUp Tasks, Docs, and Chat conversations, using the information their permissions allow them to access. You can assign them tasks, @mention them in Chat, or trigger them through an Automation.

For example, a Super Agent can help manage bug intake by reading a report in Chat, creating a task, setting its priority, and searching your workspace for similar issues. It can then reply in the original thread so the person who reported the bug can follow up. You control when the agent runs, what it can access, and which actions it can take.

For teams comparing running costs, ClickUp provides usage details for each agent. A typical Super Agent run uses an estimated 100 to 300 AI Credits, depending on the work involved. The AI credits guide explains how usage is calculated. Workspace admins and agent managers can also check an agent’s average run cost, most recent run cost, and total usage in its cost and usage panel. These details can help you review spending and decide which workflows to adjust.

Standout features

• AI Skills: Save reusable instructions that agents can apply to recurring work
• Knowledge and memory: Give agents access to relevant information and configure how they retain context
• Activity logs: Review Super Agent activity to investigate completed actions or troubleshoot failures
• Scheduled triggers: Run agents at set times, with options for recurring updates and reports

Pricing

Ratings

• G2: 4.6/5 (15,000+ reviews)
• Capterra: 4.6/5 (4,600+ reviews)

Where it taps out: Super Agents do their best work when the tasks, docs, and conversations live in ClickUp. Teams whose work sits mostly in other systems get less of that built-in context and rely more on integrations.

What real-life users are saying about ClickUp

A user review says:

Here’s how you can build and work with Super Agents:

5. Make (Best for visual, branching automations at a low monthly price)

Make lets you build automations as scenarios on a visual canvas. Each module handles an action, and routers send records down different branches based on the rules you set.

Its AI Agents feature, currently in open beta, adds model-driven decisions to those scenarios. An agent can choose which tools to call for a request, while the rest of the workflow follows your configured steps.

The credit system follows the work your modules perform. Standard actions typically cost one credit each, while built-in AI features may use additional credits based on the model and token usage. Make’s Free plan includes 1,000 credits per month, which gives you room to build and test a basic scenario before upgrading.

Standout features

• Filters: Control which records continue through a workflow based on conditions you define
• Execution history: Inspect the data passed between modules to find errors and troubleshoot failed runs
• Webhooks: Start scenarios when an external app or service sends an event
• Custom AI connections: Use supported external model providers on paid plans and pay their API costs directly

Pricing

• Free
• Core: $12/month, billed annually
• Pro: $21/month, billed annually
• Teams: $38/month, billed annually
• Enterprise: Custom pricing

Note: The Free plan includes 1,000 credits per month. The paid prices above are for 10,000 credits per month with annual billing. Pricing varies with the credit allowance and billing period.

Ratings

• G2: 4.6/5 (400+ reviews)
• Capterra: 4.8/5 (400+ reviews)

Where it taps out: Working with nested data, such as JSON arrays inside API responses, takes practice. Complex scenarios may also need careful error handling and testing. Make’s credit model also needs attention as workflows grow, particularly when AI modules consume additional credits for token usage.

What real-life users are saying about Make

A user review says:

6. Lindy (Best for delegating inbox, calendar, and meeting follow-up)

Lindy works like an AI teammate you can message through Slack or iMessage. Ask it to sort your inbox, book a meeting, or prepare a follow-up, and it works across your email, calendar, and connected apps.

Meeting follow-ups are a good example of how this works. After a sales call, Lindy can read the notes, draft a recap email, and update deal details in your CRM. It can also help fill in qualification fields such as MEDDPICC, which sales teams use to track what they’ve learned about an opportunity.

Try Lindy with $50 in free credits for seven days, with no credit card required. After that, its Team plan starts at $29.99 per active user each month. Every seat adds credits to a shared pool, so your team can draw from the same balance as work comes in.

Standout features

• Chat-first control: Delegate tasks by messaging Lindy in Slack without opening the agent builder
• Approval controls: Review emails, record updates, and other actions with an external impact before Lindy carries them out
• Model selection: Choose from supported AI models based on the task you’re assigning
• Ready-made skills: Start with built-in capabilities for scheduling, email management, and meeting preparation

Pricing

• Free trial: $50 in credits for 7 days
• Team: Starts at $29.99/user/month, with 3,000 credits per seat
• Enterprise: Custom pricing

Ratings

• G2: 4.9/5 (100+ reviews)
• Capterra: Not enough reviews

Where it taps out: Lindy’s recent direction centers on personal delegation for individual knowledge workers. It also supports agent-to-agent handoffs, so teams replacing Relevance AI Workforces should compare how each platform configures agent roles, handoffs, and approvals before switching.

What real-life users are saying about Lindy

A user review says:

7. Clay (Best for sales teams replacing Relevance AI’s research and BDR agents)

Clay runs AI research agents inside a spreadsheet-style table. Claygent, its web-research agent, fills custom columns like a company’s pricing model or latest funding news. Waterfall enrichment checks one data provider after another until it finds an email or phone number.

This is the most direct swap for teams that used Relevance AI as one of their AI agents for sales. A growth team can load target accounts and have Claygent summarize each company’s recent news. The next columns find the right contact and write a first line for outreach. If an enrichment returns nothing, Clay charges neither Actions nor Data Credits.

Clay splits billing into Actions for platform operations and Data Credits for purchased data and AI usage. CRM sync, webhooks, and HTTP API calls start on the Growth plan.

Standout features

• Waterfall enrichment: Search across more than 150 data providers and move to another source when the first one has no match
• Buying signals: Track job changes, company news, and other events that may indicate a sales opportunity
• Bring your own keys: Use existing provider API keys to avoid Clay’s Data Credit charges for those calls
• Reusable functions: Apply the same custom enrichment logic across different tables and workflows

Pricing

• Free
• Launch: $167/month, billed annually
• Growth: $446/month, billed annually
• Enterprise: Custom pricing

Ratings

• G2: 4.6/5 (200+ reviews)
• Capterra: Not enough reviews

Where it taps out: Clay is built around go-to-market data, which limits its appeal for general operations, support, or finance agents. The table builder can also take some getting used to. Complex formulas, conditional runs, and enrichment rules require careful setup, particularly when several steps depend on each other’s results.

What real-life users are saying about Clay

A user review says:

8. Dust (Best for knowledge agents grounded in company data)

Dust builds AI agents around the information your company already has. Connect sources like Notion, Google Drive, Slack, and your CRM, then give an agent access to the knowledge it needs. It can search those sources, answer questions, and work with connected tools.

This approach works well for teams that rely on information spread across several systems. A revenue agent, for example, can pull together recent customer emails, CRM notes, and internal documents before a sales call. You can also use Dust for data analysis, research, and workflows that involve several agents.

Dust lets you choose the AI model for each agent, with options from OpenAI, Anthropic, Google, Mistral, and other providers. Its Business plan also supports different seat types, so occasional users and people who run more complex agents can have different credit allowances.

Standout features

• Model choice per agent: Select from 20+ supported models based on the work an agent needs to handle
• Scheduled workflows: Have agents prepare reports, account summaries, or other recurring updates
• Shared agents: Create agents that colleagues can access within the same workspace
• Permission controls: Manage which company sources and connected tools an agent can access

Pricing

• Free with 500 lifetime credits
• Business Pro: €24/seat/month, billed annually, with 8,000 credits/seat/month
• Business Max: €120/seat/month, billed annually, with 40,000 credits/seat/month
• Enterprise: Custom pricing
• Programmatic usage: $0.01 per credit on Business; custom pricing on Enterprise

Ratings

• G2: 4.6/5 (80+ reviews)
• Capterra: Not enough reviews

Where it taps out: Dust’s credit usage changes with the model, tool calls, and the complexity of each request. Agents that do detailed research or use several tools can use up their allowance faster.

What real-life users are saying about Dust

A user review says:

9. StackAI (Best for regulated enterprises deploying document-heavy agents)

StackAI lets you build AI agents on a drag-and-drop canvas, focused on enterprise workflows and document processing. You connect an AI model to a knowledge base, add the tools it needs, and publish the finished agent through an interface or API.

For insurance and legal ops teams, this could mean an agent that reads incoming PDFs and extracts key fields. It can check those details against policy documents and send exceptions for review. The same setup can support contract analysis, internal knowledge search, and service desk workflows.

StackAI also offers deployment options for organizations with strict security requirements. Beyond its managed cloud service, Enterprise customers can choose from private cloud, VPC, and on-premises deployments.

Standout features

• Multiple AI models: Connect models from different providers within the same workflow
• Document readers: Process PDFs, Word files, and PowerPoint presentations as part of agent workflows
• Flexible deployment: Publish agents through chat interfaces, forms, or API endpoints
• Enterprise governance: Use access controls, SSO, and deployment safeguards to manage agents across the organization

Pricing

• Free
• Enterprise: Custom pricing

Note: The Free plan includes 500 runs per month, 2 projects, and 1 seat.

Ratings

• G2: 4.6/5 (30+ reviews)
• Capterra: Not enough reviews

Where it taps out: StackAI moves from its limited Free plan straight to custom Enterprise pricing. Small teams that want a self-service paid option have fewer choices here.

What real-life users are saying about StackAI

A user review says:

10. Microsoft Copilot Studio (Best for organizations standardized on Microsoft 365)

Microsoft Copilot Studio lets you build agents that work across Microsoft Teams, SharePoint, and Microsoft 365 Copilot. These agents can use your organization’s existing data, answer questions, and take action through connected tools.

An IT team, for example, can connect an agent to its SharePoint policy library and publish it in Teams. Employees can ask about internal procedures or request help with an issue. The agent can retrieve the relevant policy and start a ticket through an agent flow when further action is needed.

Copilot Studio also works with Power Platform connectors, which give agents access to business systems beyond Microsoft 365.

For employees with a Microsoft 365 Copilot license, eligible agent interactions in Copilot Chat, Teams, and SharePoint may have no additional Copilot Credit charge when the agent operates under their authenticated identity. Other agent activities are billed through Copilot Studio’s usage-based model.

Standout features

• Microsoft Graph grounding: Use organizational information from supported Microsoft 365 sources, subject to existing permissions
• Agent flows: Connect agents to defined actions for tasks like creating tickets or routing documents
• Power Platform connectors: Extend agents to business applications through standard, premium, and custom connectors
• Tenant controls: Manage agent publishing, data access, and governance through Power Platform administration tools

Pricing

• Microsoft 365 Copilot (add-on): $30/user/month, billed annually
• Copilot Credit Pre-Purchase Plan: One-year prepaid commitment
• Copilot Studio Pay-as-you-go: $0.01 per Copilot Credit, billed based on usage

Note: Microsoft 365 Copilot requires a qualifying Microsoft 365 plan. Its included agent usage differs from standalone Copilot Studio billing.

Ratings

• G2: 4.4/5 (100+ reviews)
• Capterra: Not enough reviews

Where it taps out: Copilot Studio offers less value if your organization doesn’t use Microsoft 365, as you’ll need more integrations to connect your existing tools. Costs also need attention. Microsoft enforces purchased credit limits, and agents may face interruptions if you exceed them without pay-as-you-go billing enabled.

What real-life users are saying about Microsoft Copilot Studio

A user review says:

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What Other Relevance AI Alternatives Are Worth Watching?

MindStudio: Build agents with access to more than 200 AI models and set spending limits for each agent. The Individual plan supports unlimited agents and runs, while team collaboration requires a custom Business plan. Consider it for flexible models and cost controls, though its visual agent-building overlaps with Gumloop, which we cover in our main list. Model usage is billed separately, so unlimited runs don’t mean unlimited AI usage at no cost.

CrewAI: Build multi-agent workflows in which agents take on distinct roles and share tasks. Its visual builder supports multi-agent workflows, with execution tracing and GitHub integration. Advanced human-approval management for Flows requires Enterprise. The Free plan lets you test workflows before deciding whether to consider an Enterprise agreement. We’ve kept it here because its focus on multi-agent coordination is more specialized, but it’s worth exploring if you rely heavily on Relevance AI Workforces.

Activepieces: Connect AI agents, workflows, and apps through a visual builder. Its credit-based pricing covers workflow runs and AI actions, and the Plus plan lets you use your own AI model keys. Make and n8n cover similar workflow automation use cases in our main list. Activepieces is another option if you want to compare workflow-run costs and bring your own AI model keys.

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How Do You Choose a Relevance AI Alternative?

Start with the Relevance AI agent that costs you the most. Count how many tools it calls per task, how often it retries, and which AI models it uses. Then compare what the same work would cost on two or three alternatives.

Beyond pricing, here’s how to narrow down your options based on the work your agents handle:

Before switching, save your existing agent’s instructions, tool settings, and a few sample tasks. Rebuild your busiest workflow in the new platform and test both versions against the same sample inputs, using test records and disabling live sends or updates. Compare the cost per completed task, how often each agent needs intervention, and the time spent maintaining it.

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Make Every AI Agent Worth the Investment

Relevance AI may still suit your team if its agents and Workforces handle the processes you need. A reason to switch is a specific requirement another platform meets better, such as self-hosting, document processing, or working directly with your project data.

You don’t have to move every agent at once. Keep the workflows that serve you well and replace the ones that leave your team doing too much manual follow-up.

If that follow-up happens in your tasks, Docs, and team conversations, ClickUp Super Agents are worth considering. Start with a familiar process and define what the agent can handle independently and when a person should step in.

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Frequently Asked Questions About Relevance AI Alternatives

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