ChatGPT$1.5M+
Additional ARR generated in 3 months with coaching and enablement agents
Mike WittigChief Information Officer, Gusto
View storyGumloop vs ChatGPT Work: Which is best for you?
ChatGPT Work takes on long, multi-step projects inside ChatGPT. Gumloop is an agent platform for any model, any workflow, and all your teams.
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 ChatGPT Work can't
Slack agents under your own brand and admin policy
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.
Custom MCP integrations from plain-English prompts
Describe the integration you need, and Gumloop generates a custom MCP integration usable by every agent in your org.
Event triggers that span multiple apps
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.
Run any model, from any provider
Pick the best model for each task, or let Auto pick for you. Gumloop includes 35+ models from Anthropic, OpenAI, Google, xAI, DeepSeek, Moonshot (Kimi), Qwen, MiniMax, and Z.ai (GLM) in the subscription, with BYOK for your existing model contracts, fallback model selection, and AI proxy routing.
Agents and deterministic workflows
Gumloop agents can call deterministic workflows for work that shouldn't be improvised. The workflow runs the logic; the agent decides what to do with the result.
Agents that ship dashboards, decks, and docs
Every file a Gumloop agent creates is versioned and permissioned, with its own URL you can host on your company's domain or lock behind a password. Dashboards pull live data from your tools and write back to them, using the agent's connections so teammates don't have to connect their own.
Gumloop vs. ChatGPT Work
Both Gumloop and ChatGPT have agents. Here's how they differ when the agents have to orchestrate workflows, run on any model, and pass your security review.
 ChatGPT Work | ||
|---|---|---|
| Platform and ease of use | ||
| Platform and ease of use | ||
| Agents and workflows in one product | ||
| Multi-agent orchestration | ||
| Multi-builder collaboration on the same agents | ||
| Code execution sandbox | ||
| White-labeled per-team Slack agents | ||
| AI capabilities | ||
| AI capabilities | ||
| Bring your own key (BYOK) for models | ||
| Multi-model (Anthropic, OpenAI, Google, DeepSeek, etc.) | ||
| Automatic model routing per message, across providers (Auto mode) | ||
| AI proxy routing | ||
| Reusable skills library | ||
| Agents can author new integrations | ||
| Build custom MCP integrations with natural language | ||
| Schedule triggers | ||
| Event triggers in apps | ||
| Multi-condition triggers across apps | ||
| Web monitoring as a first-class trigger | ||
| Agents can produce files (artifacts) | ||
| Unified file system for all agent outputs | ||
| Files persist past agent session | ||
| Shared dashboards that run on the agent's connected accounts, so viewers don't connect their own | ||
| External and public sharing of artifacts with admin policy controls | ||
| Password-protected artifacts on your own domain | ||
| Pricing | ||
| Pricing | ||
| Major LLM models included in subscription | ||
| Premium enrichment tools included (Apollo, Exa, Firecrawl, DataForSEO, etc.) | ||
| Predictable cost as agent-run volume scales | ||
| Security, compliance, and governance | ||
| Security, compliance, and governance | ||
| SOC 2 Type II | ||
| SSO/SAML + SCIM | ||
| Zero Data Retention (ZDR) addendum | ||
| VPC deployment of the agent platform | ||
| Customer-managed encryption keys with full agent functionality | ||
| Per-tool RBAC inside agent execution | ||
| Per-file RBAC | ||
| Per-step audit of every agent action and tool call | ||
| Allow/deny lists for AI providers | ||
| Natural-language audit queries across tool calls | ||
| Tracing of proxied MCP servers | ||
| Support and enablement | ||
| Support and enablement | ||
| Dedicated CSM (on enterprise) | ||
| 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 ChatGPT Work
Agents your team builds and improves together
ChatGPT Workspace Agents now let invited teammates edit a shared draft, but simultaneous edits aren't merged, and sharing, channel setup, and custom MCPs stay with the owner. Gumloop supports multi-builder collaboration on the same agents and workflows, with version history and rollback across contributors.
Run any model, in your stack, with full governance
Pick the right model for each task (Anthropic, OpenAI, Google, xAI, DeepSeek, Moonshot (Kimi), Qwen, MiniMax, and Z.ai (GLM)), or let Gumloop's Model Router choose per message so you never overpay for simple work. They're all included in your Gumloop subscription, with BYOK for your existing model contracts and AI proxy routing for the controls your security team needs. Allow-lists, automatic model fallbacks, and per-tool spend caps all live in the same governance plane. ChatGPT Work runs only on OpenAI's GPT models, on OpenAI's keys.
Observe every tool call and control every credential
Both Gumloop and ChatGPT support SOC 2 Type II, SSO, and SCIM. Only Gumloop adds Gumstack: per-tool RBAC inside agent execution, per-step audit traces (including the shell commands, browser steps, and tool calls ChatGPT's Compliance API doesn't fully capture), natural-language audit queries across users, agents, and services, and VPC deployment.
Gumloop vs. ChatGPT Work
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