Relevance$1.5M+
Additional ARR generated in 3 months with coaching and enablement agents
Mike WittigChief Information Officer, Gusto
View storyGumloop vs Relevance AI: Which is best for you?
Relevance AI builds AI agents for GTM teams. Gumloop is a platform for building, deploying, and governing AI agents across your entire organization.
The agent infrastructure powering the world's most AI-native companies
Tasks Automated
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Agents Deployed
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What Gumloop does that Relevance AI can't
Govern agents (even non-Gumloop ones)
Gumloop gives you an org-wide inventory and activity log of tool calls and data use across every AI agent, on and off Gumloop.
Live, hosted, and interactive artifacts
Gumloop agents can create dashboards, decks, and apps that pull live data from your connected tools, shared via URL on your company's own domain or behind a password.
Create triggers in plain language
Tell Gumloop in plain English what to watch for across any combination of apps, and it'll monitor and fire automatically when the conditions match.
Skills that run code and improve themselves
Gumloop skills bundle executable Python in a sandbox, and agents refine existing skills and create new ones as they work.
White-labeled Slack agents for every team
Gumloop deploys agents through your own Slack app, with your branding, your OAuth scopes, and your admin approval. On Enterprise plans, agents shared with your organization can serve your entire Slack workspace, including teammates without a Gumloop account.
Stop agents before they touch sensitive data
Gumloop's Connector Rules can block or flag specific tool calls before they run, fail-closed, so an agent never deletes a Salesforce record or leaks PII to a third party.
Gumloop vs. Relevance AI
Both Gumloop and Relevance AI have agents. Here's how they differ when deployed to your whole organization.
 Relevance AI | ||
|---|---|---|
| Platform and ease of use | ||
| Platform and ease of use | ||
| Deterministic workflows | ||
| Multi-agent orchestration | ||
| White-labeled Slack agents | ||
| Multiple distinct Slack agents per workspace | ||
| Email your agents | ||
| Host agents on web pages | ||
| AI capabilities | ||
| AI capabilities | ||
| Agents write and run their own code | ||
| Agents produce hosted, shareable files (artifacts) | ||
| Agents create apps with live data | ||
| Skills with executable code and self-improvement | ||
| Multiple model providers (Anthropic, OpenAI, Google, DeepSeek, etc.) | ||
| Bring your own key (BYOK) for models | ||
| Continuous web monitoring | ||
| Conversational analytics agent | ||
| Phone and meeting agents | ||
| Pricing | ||
| Pricing | ||
| Major LLM models included in subscription | ||
| Premium enrichment tools included (Apollo, Exa, Firecrawl, DataForSEO, etc.) | ||
| Usage scales without per-seat fees | ||
| Security, compliance, and governance | ||
| Security, compliance, and governance | ||
| SOC 2 Type II | ||
| SSO/SAML | ||
| Role-based access control (RBAC) | ||
| SCIM user provisioning | ||
| Audit logs | ||
| AI proxy routing | ||
| VPC / private deployment | ||
| Single-tenant isolation | ||
| Govern agents on and off the platform | ||
| Runtime tool-call policies (block/flag before execution) | ||
| Custom roles with per-user restrictions and caps | ||
| Per-step audit of every agent action and tool call | ||
| Zero Data Retention (ZDR) / incognito mode | ||
| In-platform real-time activity dashboard | ||
| Org-wide AI model/provider allow/deny lists | ||
| Org-wide fallback model defaults | ||
| Automated PII redaction | ||
| On-site enablement (on enterprise) | ||
Customer stories
“Everyone does things that they don’t want to do, and I think Gumloop is a great way to eliminate those things and be able to focus on the things that are strategic.”
Erik TchinnosianBusiness Operations, fal
View storyWhy teams choose Gumloop over Relevance AI
Agents that create and host live, shareable apps
Ask Relevance AI for a dashboard and it writes the code, then hands you a checklist. It's on you to set up hosting, configure credentials, and deploy. Gumloop agents build the artifact and host it. Get a live, shareable page, dashboard, or app with credentialed access to your real data, in one prompt.
A full policy engine
Relevance AI's RBAC controls who can see, edit, and run agents. Gumloop adds the layer enterprises actually ask about: control over what agents are allowed to do. Block sensitive tool calls before they run, set org-wide model allow/deny lists and fallbacks, restrict users with custom roles, and flip on incognito mode so data is never stored at all.
Control every agent and deploy in your own cloud
Both Gumloop and Relevance AI support SOC 2 Type II, SSO, RBAC, and audit logs. But Gumloop also monitors and controls data use across every AI agent in your organization (even ones built outside Gumloop) and can be deployed in your own VPC. Relevance AI governs only its own agents and runs multi-tenant only, with single-tenant still on its roadmap.
Gumloop vs. Relevance AI
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