Compare
How Lobstack compares.
Against always-on agents, workflow tools and AI gateways. Every claim about them is quoted from their own pages, with the date we read it, and every page says where they are better.
Always-on agents · read 2026-10-01
Agents with a computer of their own.
Grok Bot, Muse and dots work on a vendor’s cloud computer and keep going with yours off. Lobstack’s bots work on your computer and stop before every change.
Grok Bot
Grok Bot gives you named agents on xAI’s cloud that keep working with your laptop shut and stop for sensitive actions. Lobstack runs bots on your own computer and stops before every change.
Read 2026-10-01Compare MetaMuse
Muse is a personal agent for everyday errands, reached in its app or WhatsApp, running on Meta’s secure VM and checking with you before sensitive actions. Lobstack runs bots on your computer for work in your repos and tools, and stops before every change.
Read 2026-10-01Compare OpenAIdots
OpenAI’s dots are always-on agents on OpenAI’s cloud, reached in ChatGPT, Slack or Teams, with over 4,000 apps and approval for sensitive actions. Lobstack runs bots on your own computer and stops before every change.
Read 2026-10-01Compare| Where the work runs | When it stops for you | With your computer off | |
|---|---|---|---|
| Grok Bot | A persistent cloud computer with a browser, files and a terminal, shared by all your Bots. | Sensitive actions, as judged by a separate review model. You can add “Ask first” rules for more. | Keeps working. The cloud computer does not need your laptop. |
| Muse | Muse Secure VM: a dedicated virtual machine that holds the agent and your data. | Sensitive actions, such as sending an email or making a purchase. | Keeps working after you close the app, and comes back when it needs you. |
| dots | A cloud computer of its own, on OpenAI’s side. | Sensitive actions, by default rules plus an auto-review model; approval can sometimes be given in advance. Custom Rules add limits. | Keeps working toward your goals around the clock. |
| Lobstack | On your computer: Windows, macOS or Linux. Prompts go to the model; the rest stays local. | Every step that changes something, by default: a write, a command, a push or a message, with the exact change in front of you. | Nothing runs. Routines run on a schedule only while the app is open. |
Workflow tools · read 2026-10-02
Workflows that run on a server.
Sim, n8n, Zapier and Make run automations you build, on their servers or yours. Lobstack runs bots you describe a job to, on your computer.
Sim
Sim is a hosted or self-hosted place to build agent workflows on a canvas, with cost per run in its logs. Lobstack is a desktop app where bots work on your own computer and stop for your yes before each change.
Read 2026-10-02Compare Workflow tooln8n
n8n is a workflow builder with a large node library that you can host yourself and run around the clock. Lobstack is a desktop app where bots do the work on your computer and ask before each change.
Read 2026-10-02Compare Workflow toolZapier
Zapier connects more apps than anyone in this comparison and runs your Zaps on its own platform, not on your computer. Lobstack is a desktop app where bots work on your computer and ask before each change.
Read 2026-10-02Compare Workflow toolMake
Make builds automations as visual scenarios across thousands of apps, and runs them on Make. Lobstack is a desktop app where bots work on your computer and ask before each change.
Read 2026-10-02Compare| Where it runs | How you build | Approval before an action | |
|---|---|---|---|
| Sim | Sim's cloud, or your own with Docker or Kubernetes. The desktop app is macOS-only. | A visual canvas, a chat, or code. | A Human in the Loop block you add where a run should pause for a person. |
| n8n | n8n Cloud, or your own servers. Self-hosted without a licence key is the free Community edition. | A visual canvas, with code where you need it. | Human review for an AI agent's tool calls: the workflow pauses until someone approves or denies. |
| Zapier | Zapier's hosted platform. Private networks are reached through VPC peering. | Zaps (a trigger, then steps), with Tables, Forms and Canvas alongside. | Human in the Loop: a Request Approval step pauses a Zap for reviewers. Needs a paid account. |
| Make | Scenarios run on Make. An Enterprise on-premise agent reaches apps on your local network. | Scenarios made of modules, in a visual editor. AI Agents are in beta. | A Human in the Loop app, on the Enterprise plan. |
| Lobstack | On your computer: Windows, macOS and Linux. Public beta, not code-signed yet. Prompts go to the model. | Give a bot a job in plain words and it proposes the steps. A finished run can become a routine. There is no visual workflow canvas. | On by default. Every step that changes something waits for your yes, with the exact call in front of you. |
AI gateways · read 2026-09-10
Every gateway routes. Ask what comes back.
Three of these ship complexity routing, and one ships it free and open source. The questions are about what the caller gets back on each response.
OpenRouter
OpenRouter will tell you which models it considered and which one it picked. It will not tell you what the others would have cost.
Read 2026-09-10Compare AI gatewayLiteLLM
LiteLLM computes the counterfactual too. The difference is where it goes: their savings figure reaches the operator, in a rollup, after the fact — ours reaches the caller, on the request, in the response.
Read 2026-09-10Compare AI gatewayPortkey
Portkey is not trying to pick a model for you, and does not pretend to be. It computes cost accurately and keeps it in the dashboard — the response carries four headers and none of them is money.
Read 2026-09-10Compare| Picks a model for you | Tells the caller why | Cost in the response | Prices the road not taken | |
|---|---|---|---|---|
| OpenRouter | Routes | Yes — opt-in header | Yes — usage.cost, always on | No |
| LiteLLM | Routes · beta | Model only; tier stays in logs | Yes — response header | Yes — to the operator, aggregated |
| Portkey | No routing | Your own rules | No — dashboard only | No |
| Lobstack | Yes — complexity tiers with a spend ceiling | Yes — on every response, no opt-in | Yes — response headers | Yes — per request, on the response |
Two qualifications on ours: with stream: true the cost and savings figures move to the final frame, and asking for auto names no model, so the baseline becomes the most expensive one your plan allows and baseline_reason says which you are reading. Both in the metering docs.
The idea underneath
What a routing receipt is.
Savings percentages are the currency of this category, and almost none of them are proved on a single request.