8 Relevance AI Alternatives for Production Teams in 2026

Which Relevance AI alternative fits your use case?

Start with the operating model you want. The platforms below solve different problems despite sharing the “AI agent” label.

• Choose Dasha when the agent’s primary job is a live phone conversation and your technical team needs a managed production voice runtime.
• Choose Gumloop when business teams want a managed visual builder with agents, reusable skills, approvals, and evaluations.
• Choose n8n when technical teams want visual workflows, code, and a credible self-hosted path.
• Choose StackAI when governed enterprise deployment and document-heavy internal workflows drive the decision.
• Choose CrewAI when developers want an open-source multi-agent framework and control over the code and runtime.
• Choose Make AI Agents when an agent belongs inside broader visual operations automation.
• Choose Zapier Agents when fast, app-centric task execution matters more than a node-based canvas.
• Choose Lindy when employees should delegate work to an assistant from Slack and other familiar tools.

Public prices are useful starting points, but the billing units differ. Compare the cost of completing your actual workflow, including model usage, retries, external APIs, infrastructure, telephony, and engineering time.

Relevance AI is already more than a prototyping tool

A fair comparison starts with the current product. Relevance AI’s product provides agents that use tools, knowledge, and integrations to complete tasks. Relevance AI Workforces coordinate specialist agents on a visual canvas with handoffs and conditions. Relevance AI Invent can create or change agents, tools, triggers, and workforces from plain-language instructions.

The platform also covers controls that matter in production:

• tool-level Autopilot or explicit approval
• escalation and manual override
• run traces, reusable evaluations, and publish gates
• app, API, trigger, chat, and embed deployment paths
• enterprise single sign-on, role-based access control, audit logs, and multi-organization management

Some controls sit on higher plans. Agent Evaluations and enterprise security controls are Enterprise features.

The Free plan has been retired. Pro costs $19 per month billed annually, or $29 month to month, and includes 2,500 Actions plus $20 of Vendor Credits each month. Team costs $234 per month billed annually, or $349 monthly, with 7,000 Actions and $70 of Vendor Credits. One tool run counts as one Action even when it fails. Bring-your-own model keys can bypass Vendor Credits, while Actions remain metered.

That makes the switching question more precise. You may need a different billing unit, deeper deployment control, simpler employee adoption, a purpose-built channel, or a framework your engineers can own.

1. Dasha: for technical teams building production voice products

We recommend Dasha when the core workload is a live phone conversation. Our managed voice AI backend combines a runtime, REST APIs, a web application, telephony, per-customer configuration, testing, monitoring, and call execution. It fits teams embedding inbound or outbound voice into a product and operating it across customers.

Dasha is adjacent to Relevance AI rather than a direct general-workflow replacement. It does not replace a visual workforce for research, customer relationship management updates, or document processing. A team may keep those workflows in an automation platform and use Dasha for the real-time voice layer.

The Developer plan includes 1,000 free connected minutes and one concurrent call. Growth starts at $0.08 per connected minute. Voice over Internet Protocol (VoIP) and model-token costs are separate, so model the all-in cost of your own call path.

Choose Dasha if: voice is the product experience and you have technical implementation capacity.

Look elsewhere if: your main need is a general business-user agent builder.

2. Gumloop: for managed visual agents and flows

Gumloop’s agent builder gives business teams connectors, skills, knowledge, triggers, subagents, and visual flows. Tool permissions can allow, ask, or deny an action. The platform also includes approvals, evaluations, and spend analytics.

Gumloop Pro starts at $37 per month with 20,000 credits and an 8% orchestration fee. Enterprise adds deeper controls such as role-based access, single sign-on, audit logs, and an optional customer-cloud virtual private cloud.

Choose Gumloop if: you want a managed, approachable builder with both agent and workflow concepts.

Tradeoff: credit consumption varies with the work, and the stronger governance and deployment choices require Enterprise.

3. n8n: for technical workflow ownership

The n8n agent platform combines a visual workflow editor with JavaScript, Python, webhooks, Model Context Protocol (MCP) connections, and custom nodes. It can run in n8n Cloud or on customer-managed infrastructure. The Community Edition provides a free self-hosted starting point.

Cloud Starter costs €20 per month billed annually for 2,500 workflow executions. n8n bills a complete workflow execution rather than each individual step. For first-class n8n Agents, one agent turn counts as one execution. Workflow-tool calls and sub-agent calls inside that turn do not count separately.

Choose n8n if: engineers want a visual system without giving up code or self-hosting.

Tradeoff: self-hosting transfers security, upgrades, scaling, and incident response to your team. First-class n8n Agents are still in preview, while the established AI Agent node remains available.

4. StackAI: for governed enterprise workflows

StackAI’s workflow platform centers on enterprise agents and applications, especially internal processes grounded in company documents. Its deployment options include multi-tenant cloud, a customer virtual private cloud, and on-premises infrastructure. Human review, logs, versioning, granular access control, and single sign-on support regulated rollouts.

The StackAI Free plan includes 500 runs per month, two projects, and one seat. Collaborative production use moves to custom Enterprise pricing.

Choose StackAI if: deployment control, document access, and centralized governance outweigh self-serve price transparency.

Tradeoff: the path from individual evaluation to a governed team deployment is sales-led.

5. CrewAI: for code-owned multi-agent systems

CrewAI’s Python framework is open-source software for agents, crews, and event-driven flows. Crew Studio adds a visual layer and can export code. The managed platform supports tracing, testing, guardrails, and human input, while Enterprise adds policy and identity controls.

The CrewAI Basic plan is free with 50 workflow executions per month. Enterprise can run in CrewAI’s cloud, a customer virtual private cloud, or customer infrastructure.

Choose CrewAI if: your developers want to own agent behavior in code and retain deployment flexibility.

Tradeoff: the engineering burden is higher than a fully managed business-user builder, and production plans use custom pricing.

6. Make AI Agents: for agents inside visual operations automation

Make AI Agents places agents inside Make scenarios. Agents can use modules, scenarios, MCP tools, and knowledge files. A reasoning and debugging panel helps operators inspect the run. The surrounding Make platform supplies visual automation across thousands of applications.

Make AI Agents remains in open beta. The Free plan includes 1,000 credits per month. Make Core starts at $9 per month when billed annually for 10,000 credits per month. Most module actions consume a credit, while AI features can also consume credits based on token use.

Choose Make if: your agent is one decision-maker inside a larger operations workflow.

Tradeoff: scenario actions and model consumption must be measured together. Make’s on-prem agent connects hosted Make to local networks; it does not self-host Make AI Agents.

7. Zapier Agents: for app-centric task execution

Zapier Agents uses prompts and a Copilot-style setup rather than a visual node canvas. Agents work with connected applications, triggers, actions, live data sources, and attached knowledge. This makes it a natural option when the work is already spread across common SaaS tools.

Free includes 400 activities per month. Zapier Pro costs $33.33 per month billed annually for 1,500 activities. An activity can be an action, a web browse, or a knowledge lookup, so one task may use several activities.

Choose Zapier Agents if: broad app reach and quick setup are the primary requirements.

Tradeoff: activity limits can constrain long, tool-heavy runs. If app and action restrictions are required, confirm the exact controls available for Zapier Agents during procurement.

8. Lindy: for Slack-first employee delegation

Lindy’s agent docs describe an adjacent option for teams that want an AI teammate inside Slack, email, meetings, and other daily tools. Employees assign requests or routines through prompts and skills. Actions with external impact, such as sending an email or updating a ticket, wait for approval.

Plus costs $29.99 per user per month with 3,000 credits. Pro is $99.99 per user with 15,000 credits. Enterprise adds controls such as single sign-on and audit logs.

Choose Lindy if: employees should delegate work from familiar interfaces without designing a fleet on a visual canvas.

Tradeoff: it is Slack-centric, and pricing combines paid seats with a shared credit pool.

If you need phone agents, build a voice-specific shortlist

Relevance AI supports calling agents, so calling alone is not a reason to switch. The decision changes when live phone handling is the primary product requirement. Real-time voice adds telephony, turn-taking, interruption handling, transfers, carrier behavior, and call-level observability to the agent stack.

Current voice-specific options span several operating models:

• Dasha voice backend provides a managed backend and APIs for technical teams building voice products.
• ElevenAgents documentation provides managed conversational agents with telephony deployment.
• Retell’s voice platform focuses on phone agents and supports call transfers, knowledge bases, interactive voice response navigation, and queued batch calling.
• Synthflow documentation provides a visual managed builder for inbound and outbound voice agents.
• Vapi documentation combines a hosted platform with dashboard and software development kit setup.
• Vocode telephony documentation provides open-source voice orchestration alongside a hosted API.

Use these criteria to compare them:

1. Channel scope: Decide whether you need broad asynchronous workflow automation or a runtime dedicated to live conversations. Live calls require response times and failure handling designed for synchronous conversation.
2. Telephony and handoff: Test inbound and outbound calls, phone numbers or Session Initiation Protocol (SIP), warm and cold transfers, keypad input, interactive voice response navigation, voicemail handling, and transfer failure behavior.
3. Grounding and data capture: Verify how the agent retrieves approved knowledge, calls tools, writes structured outcomes, and handles missing or conflicting information.
4. Conversation quality: Test interruptions, silence, noisy audio, accents relevant to your users, long turns, and the exact phone network path. Browser demos do not reproduce telephone audio.
5. Concurrency and load: Define simultaneous-call requirements and queue behavior. A batch dialer can queue work, but queued calls still run within the platform’s concurrency limits.
6. Testing and observability: Require recordings, transcripts, tool logs, event timelines, and enough trace detail to diagnose a failed call. Use a fixed regression set before changing prompts, models, tools, or voices. Our voice agent testing guide explains how to build that evaluation loop.
7. Ownership and migration: Identify which layers come from the platform, carrier, speech providers, models, and your code. Switching a vertically integrated provider may require changes across several layers.
8. Fully loaded price: Add platform minutes, telephony, speech-to-text, text-to-speech, model tokens, phone numbers, recording, storage, support, and engineering operations. Our voice agent pricing guide provides a fuller cost model.

Run one representative pilot before you buy

A feature matrix will not reveal how an agent behaves in your environment. Compare finalists with the same workflow, data, permissions, and success criteria.

1. Pick one meaningful workflow. Include a real trigger, at least one system of record, a tool action, a human escalation, and the failures you already expect.
2. Build a fixed test set. Cover the happy path, ambiguous inputs, missing permissions, stale data, tool errors, retries, and unsafe requests. For voice, add interruptions, silence, transfer failures, and noisy audio.
3. Measure outcomes and failure handling. Track completion, incorrect actions, human intervention, latency where it affects the user, recovery from tool failures, and the evidence available for debugging.
4. Model the whole bill. Convert each platform’s credits, Actions, activities, runs, executions, minutes, and model charges into cost per completed business outcome. Include failures and retries.
5. Review operating controls. Confirm versioning, approvals, access control, logs, evaluation, environment promotion, rollback, data retention, and support before the pilot becomes production.

Frequently asked questions

Is Relevance AI free?

No. The Free plan has been retired. Relevance AI Pro starts at $19 per month when billed annually, or $29 month to month. It includes 2,500 Actions and $20 of Vendor Credits per month. Additional Actions and Vendor Credits are sold separately.

Which Relevance AI alternatives can be self-hosted?

n8n offers a free self-hosted Community Edition. CrewAI’s open-source framework can run on customer infrastructure. StackAI and CrewAI also offer customer-controlled deployment options on enterprise contracts. Self-hosting gives you more infrastructure control and makes your team responsible for more security, upgrades, scaling, and incident response.

What is the best open-source Relevance AI alternative?

Choose n8n Agents when the core job is workflow automation with visual design and optional code. Choose the CrewAI framework when developers want a Python framework for custom multi-agent behavior. For a phone-agent stack, the Vocode telephony stack is a relevant open-source option. The right choice depends on how much runtime and operational work your team wants to own.

Can Dasha replace Relevance AI?

Dasha can replace the voice layer when live phone agents are the main workload. It is not a like-for-like replacement for Relevance AI’s general agent workforces. Teams that need both can use a workflow platform for asynchronous business automation and Dasha for real-time voice.

Choose the operating model before the vendor

The useful shortlist is usually small. Decide who will build and operate the system, where it must run, what control reviewers need, how usage is metered, and which channel carries the customer experience. Then compare two or three finalists on one real workflow.

If that workflow is a production phone conversation, start a Dasha technical evaluation and test the complete call path, tool behavior, handoff, and monitoring before you scale it.