OpenFrame vs Atera: How Fae, Mingo, and Robin Compare

Every AI ticketing system aimed at MSPs makes the same promise: tickets close themselves, technicians stop drowning, margins recover. Atera has pushed that promise hardest with Robin, its autonomous AI technician, and it now guarantees the outcome in writing. OpenFrame takes a different route: two purpose-built agents instead of one generalist, running on open infrastructure you can self-host. This post compares Fae, Mingo, and Robin on architecture, autonomy, security scope, and price, using published numbers from both vendors.

TL;DR

Pricing for both, September 2026.

Two Purpose-Built Agents vs One Generalist

The architectural difference drives everything else here. Atera built Robin as a single agent that greets the end user, triages the ticket, attempts resolution, and assists the technician when it escalates. One brain, every job. Atera describes Robin working from intake through escalation, and that framing is accurate: Robin is the front door and the workbench at once.

OpenFrame splits the work in two. Fae is the client-facing agent, running as a desktop app that sits in the system tray on the endpoint. It picks up the request the moment an employee reports a problem, runs diagnostics, and resolves the ticket without waiting for a human. Mingo is the technician-facing agent: a persistent sidebar that reads the last five screens you were on, digs through device telemetry, drafts remediation, and executes after a technician approves.

The split matters because the two jobs carry opposite risk. A password reset or a stuck print queue should run at full autonomy, and making a client wait 40 minutes for a human to bless a routine fix wastes everyone's time. A registry edit across 200 endpoints should not run at full autonomy. A single agent serving both jobs has to compromise somewhere: it asks permission too often on easy work, or it moves too freely on risky work. Two agents let each side run at the autonomy the job deserves.

One practical note that comes up in demos: Fae is renameable. Michael Assaf, Flamingo's CEO, puts it plainly: "We don't call it Fae anymore because you can call it whatever you want." If you white-label client-facing support, the agent carries your name, not ours.

What Fae Handles on the Client Side

Fae works the queue nobody enjoys. Flamingo publishes the per-task economics on its pricing page, and the client-side rows are the ones that shape a help desk's day:

Those are published figures, not a private benchmark, and the last row is the interesting one. Escalation itself is a task, and Fae does it with context attached, so the technician starts from evidence instead of "have you tried turning it off and on again." Fae escalates automatically after an issue is reported unresolved three times, which caps how long a client can sit in a loop with an agent that isn't getting there.

Early customer results point the same direction, with the caveat that some of them are still forecasts. Utah Tech Repair's case study reports early testing that projects 50% of routine tasks automated and 30% faster response times once they finish migrating at the end of their current contract term. Projected, not banked. Treat it as a direction of travel and ask for updated numbers before you buy on it.

What Mingo Does for Technicians

Mingo is the agent your technicians drive, and it reaches further into security than the client-side agent does. Point it at a misbehaving endpoint and it runs the session a tech would: pulls logs, checks recent changes, correlates events, proposes a fix. The technician reviews, approves, and Mingo executes. Approvals are tiered and batched, every action is logged for audit, and guardrails run as global templates with per-tenant and per-customer overrides.

That approval model is a design choice, not a missing feature. You answer to clients for every change on their infrastructure, and "the AI did it" is not an incident report anyone accepts. The approval flow means every action traces to a technician's decision.

The published task economics for Mingo cover the work that eats afternoons:

Note what the bottom three rows are: security work. Phishing containment, device isolation, CVE remediation, firewall review, and leaked-credential checks all sit in Mingo's task list, which is the practical meaning of "IT and security" rather than IT alone.

LNC Data's case study is the measured counterpart: technicians handling 20% more tickets without adding headcount, 30% faster average resolution on common issues, and roughly 8 to 10 hours a week of troubleshooting time returned to the team. Their techs use Mingo to run backend commands and cut the back-and-forth. Same team, more done.

Robin: What Atera Built

Robin deserves a fair reading, because Atera shipped real capability. Robin recognizes intent from natural language, works with end users on its own, and runs remediation through Atera's RMM. In its May 2026 announcement, Atera says Robin is capable of resolving 92% of technical issues autonomously over time, and it backs the pitch with a term almost nobody else writes down: Robin resolves 50% of an enterprise's tier-1 and complex tier-2 tickets within the first 90 days of onboarding, or Atera waives all fees. The same release promises a 72-hour proof of concept that brings Robin live in a real environment.

Robin's reach is wide. It takes requests through Microsoft Teams, Slack, email, and third-party ITSM tools, so end users never leave the apps they already live in. It carries cloud management skills across email, storage, identity, and security tasks. Atera reports more than 13,000 customers across 120+ countries, which means rough edges get sanded fast and community answers are easy to find.

The open question is the operating model around it. Robin's meter runs per end user, an allowance is bundled into the higher Power and Superpower tiers, and Atera doesn't publish the rate past that allowance. We've dug through the tiers in our Atera pricing analysis. A guarantee on resolution rate is a real risk-reducer. A guarantee on the bill is a different thing, and that one isn't on offer.

ConnectWise Sidekick is the other name in every one of these conversations, and it belongs in a different bucket: a copilot layered onto the ConnectWise stack, priced as an extra, aimed at assisting a technician inside a workflow built for humans. Copilots draft the reply. Agents close the ticket. That distinction is the line to draw through every product page in this category, and vendors blur it deliberately.

The Comparison, Feature by Feature

Three rows deserve expansion: the proactive one, the pricing one, and the source one.

Proactive Work Beats Reactive Queues

Robin is primarily ticket-triggered. Someone reports a problem, Robin picks it up. That model leaves detection sitting with your clients' patience. The tickets nobody files, the disk at 94%, the service that has been flapping since Tuesday, wait until they become outages.

OpenFrame goes after that queue before it forms. Scheduled scripts run on cron or interval with criteria-based auto-assignment, so a device that comes online into a bad state gets handled without anyone noticing it was broken. SQL-style live queries across the fleet through osquery answer "which machines have this problem" in one pass, and Mingo turns a fleet-wide query into a one-minute job at $0.08 instead of a 20-minute manual sweep. Fewer inbound tickets, rather than faster inbound tickets, is the number that moves margin. Atera is moving this direction with its self-healing messaging, but its published proof today centers on resolution after intake, not prevention before it.

The Pricing Math, Worked Out Loud

Here is where the models separate, and the interesting difference is not which one is cheaper. It is which one you can calculate.

OpenFrame charges $1 per device per month with 10 million AI tokens included, or $0.80 per device on annual billing with 25 million tokens. Past the included allowance, AI is metered pay-as-you-go, and you watch consumption in the platform. Flamingo's own investor materials model $5 to $6 per device per month in tokens at heavy use, so a shop running the agents hard should budget for the meter rather than assume the $1 covers everything. Anyone telling you the base price is the whole price is selling, not counting.

Atera charges per technician, roughly $149 to $219 per tech per month at list on annual billing as of September 2026, with Robin metered per end user on top and no published rate past the bundled allowance.

Run a 500-device, 5-technician shop through both. OpenFrame is $500 a month for tooling on monthly billing, or $400 on annual, plus a token meter you can forecast from published per-task costs: a password reset is $0.03 of AI against $12.00 of technician time, a full device diagnosis is $0.57 against $90.00. Atera is $745 to $1,095 a month at list, plus a Robin meter you cannot forecast at all, because the number isn't published.

That is the actual difference. Both platforms meter AI. One publishes what each task costs before you sign; the other asks you to book a call. For an MSP billing clients per device, per-device costs also pass through to client pricing cleanly, while per-tech pricing plus a per-user AI meter doesn't map to how you invoice.

Open Infrastructure vs a Closed Stack

The row that never appears in vendor feature grids is whether you can read the code. OpenFrame's RMM agent is open source, auditable, and forkable, and the platform unifies MeshCentral for remote access with Fleet MDM and osquery for device management and live queries. You can self-host the whole thing through the OpenFrame CLI, on your own cluster, and self-hosted deployments can disable AI entirely if a client contract requires it.

That matters for two reasons beyond ideology. Open infrastructure is what takes the software bill down, because you stop paying rent on components that already exist as good open-source projects. And it changes what a security review looks like: a client asking what runs on their endpoints gets source code instead of a trust-us datasheet.

The agents are built into that infrastructure rather than layered over it, which is also why Mingo can reach across RMM, MDM, queries, policies, and ticketing in a single session. OpenFrame's Gen1 covers large areas of RMM, MDM, monitoring, automation, remote access, patch management, security monitoring, and ticketing. Large areas, not the whole of any one of them. Atera's stack is closed, hosted, and complete in its own lane, and for plenty of shops that trade is fine.

You don't have to take the vendor's word for any of this, ours included. MSPs pulled OpenFrame apart in public on r/msp, and the thread opens skeptical before the hands-on reports arrive:

Where Robin Wins Today

Credit where it's due, because a comparison you can trust has to name the other side's advantages.

Robin's multi-channel intake is ahead. If your clients live in Teams and Slack and you want zero behavior change from end users, Robin meets them there now. Its cloud tenant management covers identity, email, and storage work that OpenFrame lists on the roadmap rather than shipping today. The 50% resolution guarantee with a 72-hour proof of concept is a genuine risk-reducer for a cautious buyer, and it is the strongest commercial term anyone in the RMM space currently offers. And 13,000 customers means the integration patterns you need are already documented somewhere.

If those things top your list and per-technician pricing suits your ratio of techs to endpoints, Atera is a defensible pick. Picking the tool that fits your operation is the whole point of vendor research.

Lock-In Is a Feature Decision Too

One line item never shows up in feature grids: the exit. OpenFrame runs month-to-month with a 14-day trial, no card required, and the source is public, so leaving means exporting data from software you could keep running yourself. Atera's agreements run annually, and Robin's value compounds the switching cost, because the more your desk leans on an agent, the more expensive the door gets.

Neither model is wrong, but they price risk differently. A multi-year commitment in a category reinventing itself every two quarters is a bet that today's leader is still right in 2028. A month-to-month platform has to re-earn the business every 30 days. Ask which incentive you'd rather have pointed at your vendor.

How to Pressure-Test Any AI Ticketing System

Whether you land on OpenFrame, Atera, or neither, run the same three tests before you sign.

• Resolution rate on your ticket mix. Ask the vendor to define which tickets count as eligible for autonomous resolution, then map that definition onto your last 90 days. A 92% rate on a narrow eligible set can mean well under 50% across everything tier-1 and tier-2. The denominator is the whole game.
• Price you can compute. Calculate your monthly all-in from published pricing at your device and technician count, including the AI meter. If a number is missing, treat the missing number as the real number.
• Cost of leaving. Read the contract term, the data export path, and what breaks on day one after cancellation. An agent that resolves half your queue is wonderful right up until it becomes the reason you can't renegotiate.

One more test worth an hour: give the trial agent a real ticket from your backlog, not the vendor's sample scenario. A messy VPN ticket from an annoyed client at 4:55 pm on a Friday tells you more than any benchmark slide.

The Call for Your Desk

Three profiles, three answers. If your clients demand Teams-native support and you need identity and cloud tenant tasks automated today, Robin fits, and the guarantee softens the risk of an unpublished meter. If you're a per-device MSP that wants AI costs you can forecast per task, infrastructure you can audit or self-host, and separate agents for the client side and the technician side, OpenFrame fits. If you want a copilot inside a stack you already run, ConnectWise Sidekick is that, priced as an extra. For the wider field, we compared the agents shipping right now in our AI help desk software breakdown.

Hold every agent to the same test: tickets closed with zero human touch, at a price you can compute from the pricing page. One of these platforms lets you run that math before the sales call.