Migrate from Avigilon to Spot AI

Migrate from Avigilon to Spot AI

Once you have decided to move off Avigilon, the enterprise video security line from Motorola Solutions, the real question is how to do it cleanly. The good news is that a migration to Spot AI is software-led, not a construction project. Your cameras are ONVIF hardware, and Spot AI is camera-agnostic, so most of your existing fleet keeps working, legacy analog runs through the IVR, and the cutover is phased so cameras never stop recording. There is no rip-and-replace.

This guide lays out the migration end to end: what carries over, a four-phase plan with realistic timeline, effort, and risk, how your existing Avigilon recordings are handled, and a checklist you can run the project from. One number frames why teams pair a migration with a move to real-time response: the median annual wage for a US security guard was $42,470 in May 2025, and round-the-clock coverage multiplies that base once shifts, benefits, and overtime are added (Source: U.S. Bureau of Labor Statistics). Migrating to a platform that watches every camera continuously is a chance to change that math, not just swap software.

What carries over when you migrate

The biggest misconception about leaving Avigilon is that it means tearing cameras off the wall. It does not. Because Spot AI is camera-agnostic and works over ONVIF, the physical layer you already paid for mostly stays in place, and the migration is a change of software and workflow rather than of hardware.

• Your IP cameras. Avigilon cameras are ONVIF hardware, and Spot AI works with any ONVIF IP camera a business already owns, so your Avigilon fleet keeps running under Spot AI. The same is true of the other makes on your sites (Axis, Hanwha, Pelco, and others), so a mixed estate consolidates onto one platform.
• Legacy analog cameras. Older analog cameras connect through the IVR (Intelligent Video Recorder), so a site does not have to be fully IP before it can move.
• Your cabling and mounts. The network cabling, PoE, and camera mounts you funded stay in place. Migrating reuses your largest hardware investment instead of writing it off.
• Your evidence. Recordings held in ACC remain available under their existing retention policy and can be exported before you decommission the on-premises servers (covered in detail below).

What changes is the recorder and the data model. Instead of recording servers you size, buy, and refresh, Spot AI uses a hybrid edge-to-cloud design: the IVR keeps full-resolution video in the facility and only metadata crosses the network, which keeps the deployment fast and PCI-clean. You get remote, portfolio-wide access without shipping full video off site and without maintaining a server room per location. For the wider category context, the enterprise video security guide walks through how these architectures differ.

The migration path at a glance

Here is the whole move in one view, so you can hold any project plan against it. The table leads with the four phases and the timeline, effort, and primary risk of each, with how that risk is handled. Timelines are typical ranges and depend on the number of sites, cameras, and networks involved.

Phase 1: Assess your Avigilon estate

The migration starts with a clear picture of what you have. The goal of this phase is to know exactly which cameras carry over unchanged, which analog cameras route through the IVR, and where your recordings and retention obligations sit today.

What you do: inventory every camera by site, make, model, and connection type, and confirm ONVIF support for the IP cameras. Document your current ACC and Alta footprint, including recording servers, storage, retention windows, and any integrations such as access control. Name the trigger driving the move, whether it is a license renewal, a server refresh, an ACC upgrade, or a run of missed incidents, and put a date on it, because that date is your natural migration window. If you are still comparing options at this stage, the Spot AI vs Avigilon comparison and the best Avigilon alternatives in 2026 lay out the field.

Common pitfall: discovering an unmapped camera or an unsupported analog stream mid-cutover. The fix is to complete the inventory now, so legacy analog is planned onto the IVR before any site moves.

Phase 2: Pilot Spot AI on live cameras

Before you commit the portfolio, prove the platform on your own footage. A pilot removes the guesswork that a spec sheet leaves behind and gives your team a reference deployment to model the rollout on.

What you do: pick one representative site and connect a subset of its existing cameras to Spot AI, running alongside Avigilon rather than in place of it. Validate the things that matter in daily use: detection accuracy on your scenes, active deterrence with talk-down and strobes, natural-language video search, and how quickly non-technical staff can find footage themselves. Because the pilot runs on cameras you already own, it usually stands up in days, not months.

Common pitfall: judging the platform on features alone. Judge it on your cameras, your lighting, and your incident types, and let the pilot site set the acceptance criteria for every site after it.

Phase 3: Cut over site by site

This is the phase teams worry about most, and it is the one the plan is built to de-risk. The principle is simple: nothing goes dark. Each site keeps recording on Avigilon while Spot AI is brought online alongside it, and coverage only moves once the site is confirmed live.

What you do: sequence sites by priority, often starting with the highest-risk or highest-cost locations. For each site, bring the existing cameras onto Spot AI, confirm every camera is streaming and recording to the IVR, verify agents and deterrence are active, then retire the parallel Avigilon coverage for that site. Because the change is software-led on cameras you already own, most sites go live in days, and the rollout is measured in configuration time rather than construction time. There is no rip-and-replace and no portfolio-wide downtime window.

Common pitfall: switching a site off Avigilon before Spot AI is confirmed live there. Keep both running in parallel per site until the site passes its checks, so there is never a coverage or evidence gap.

Phase 4: Tune agents and deterrence

A migration is not finished when the last camera moves. The final phase turns a working deployment into a tuned one, so the platform earns its keep rather than adding noise.

What you do: refine which Video AI Agents run where, set no-go zones and schedules per site, and calibrate active deterrence so talk-down, strobes, and sirens fire on the right events. Where you need a detection that is specific to your operation, use Iris to build a custom one. Review the first few weeks of alerts with your team and adjust thresholds so the system flags what matters and stays quiet otherwise. This is also where the operational shift lands: the same cameras that used to record for later review now detect in context and deter in the moment.

Common pitfall: leaving generic settings in place and accepting alert noise. A short, deliberate tuning pass in the first two to four weeks is what separates a system people trust from one they mute.

What happens to your existing Avigilon recordings

Evidence continuity is the part of any migration that security and legal teams press on, so it deserves a clear answer. Nothing is deleted the day you switch, and your existing recordings stay under your control.

Your ACC recordings continue to live on your on-premises recording servers and storage under the retention policy you already run, right up until you choose to decommission that hardware. That gives you a window to handle historical footage deliberately rather than under pressure. Before you retire the servers, use ACC's native export to pull any recordings you need to keep for open investigations, insurance claims, or chain-of-custody obligations, and store them according to your evidence policy. The exact export formats and retention windows depend on your own ACC configuration, so confirm them against your current setup during the assess phase.

Going forward, new video records to the IVR at each site. Because the IVR keeps full-resolution footage in the facility and sends only metadata to the cloud, your ongoing retention stays on site and PCI-clean, while search and review happen from anywhere. For a deeper look at how modern platforms handle recording and retention, see how video intelligence software works.

Your migration checklist

Use this as the backbone of the project plan. Each item maps to one of the four phases above.

1. Inventory every camera by site, make, model, and connection type, and confirm ONVIF support for the IP fleet.
2. Document your current ACC and Alta footprint: recording servers, storage, retention windows, and integrations.
3. Name the trigger and the date driving the move, and use it to set the migration window.
4. Stand up a pilot on one representative site using a subset of existing cameras, running alongside Avigilon.
5. Set acceptance criteria on the pilot: detection accuracy, deterrence, search speed, and ease of use for non-technical staff.
6. Sequence sites by priority and cut over one at a time, keeping Avigilon recording until each site is confirmed live.
7. Export and preserve any ACC recordings under legal hold or open investigation before decommissioning on-premises servers.
8. Tune agents, zones, and deterrence per site over the first two to four weeks, and use Iris for custom detections.
9. Model the multi-year total cost, including licenses or subscription, server and storage refresh avoided, and guard hours the platform can offset.

What teams see after migrating

The pattern across migrations is consolidation plus speed: many disconnected local systems collapse into one portfolio view, and the time to find and act on an incident drops sharply, often on the cameras that were already on the wall. Storage Asset Management runs Spot AI across about 50 virtually-managed facilities with no traditional on-site staff, deployed on its existing camera infrastructure without new hardware. At one previously targeted site, the system detected an intruder at about 1 a.m., alerted police who arrived while the incident was still in progress, and the site had no repeat break-ins afterward. Separately, Tidewater Fleet Supply unified 3 distribution centers and 14 retail locations onto a single dashboard by reusing existing cameras, avoiding roughly $250 to $500 per camera in upgrade costs across a Florida-to-Virginia footprint. These are customer-reported outcomes, and results depend on the site.

The threat environment is why many teams pair the migration with a move to real-time response rather than a like-for-like software swap. Retailers reported an 18% increase in the average number of shoplifting incidents per year in 2024 versus 2023, and a 17% increase in threats or acts of violence during theft events over the same period (Source: National Retail Federation). Detecting intent in context and acting while an incident happens, rather than filing a clip afterward, is the capability a migration is a chance to add.

"I almost think of Spot AI as another member of my team."

Lee Kunkel, Director of Virtual Operations, Storage Asset Management

Plan your move

A disciplined migration beats a rushed one, and the resources here help you scope it. Start with the full Spot AI vs Avigilon comparison for the side-by-side, read why teams switch from Avigilon to Spot AI for the trigger moments and objections, and use the Avigilon pricing guide to model the incumbent cost you are migrating away from. If you are still building a shortlist, the best Avigilon alternatives in 2026 and the wider best video management software guide put Spot AI in context.

The fastest way to plan your migration is to see Spot AI run on your own cameras and sites. Book a demo with Spot AI to watch an AI Security Guard work on the cameras you already own, and to get a phased migration plan mapped to your footprint and your next Avigilon renewal.

Frequently asked questions

How do you migrate from Avigilon to Spot AI?

Migration runs in four phases: assess your camera estate, pilot Spot AI on live cameras at one site, cut over site by site while Avigilon keeps recording, and tune agents and deterrence afterward. Because Spot AI is camera-agnostic and works over ONVIF, the move is software-led rather than a hardware replacement. Most individual sites go live in days, and the phases can overlap across a larger portfolio.

Can I keep my existing Avigilon cameras when I move to Spot AI?

Yes. Avigilon cameras are ONVIF hardware, and Spot AI is camera-agnostic, so your existing IP fleet keeps working after the migration. Legacy analog cameras connect through the IVR. That means most teams avoid a rip-and-replace and reuse the cameras, cabling, and mounts they already funded.

What happens to my existing Avigilon (ACC) recordings during the migration?

Your ACC recordings stay on your on-premises servers under their current retention policy until you decommission that hardware, so nothing is deleted when you switch. Before retiring the servers, use ACC's native export to preserve any footage under open investigation or legal hold. Going forward, new video records to the IVR on site, keeping full-resolution footage in the facility.

Will migrating from Avigilon cause downtime or a rip-and-replace?

No. The cutover is phased, so each site keeps recording on Avigilon while Spot AI is brought online alongside it, then coverage moves only once the site is confirmed live. Because the change is software-led on cameras you already own, there is no portfolio-wide downtime window and no rip-and-replace.

How long does an Avigilon to Spot AI migration take?

Assessment and a pilot typically take about one to two weeks each, and each site usually cuts over in days once the pilot has set the pattern. A single-site move can be complete in a couple of weeks, while a large multi-site portfolio rolls out over a longer window because sites are sequenced by priority rather than moved all at once. Tuning continues for the first few weeks after each site goes live.

About the author

Joshua Foster is an IT Systems Engineer at Spot AI, where he focuses on designing and securing scalable enterprise networks, managing cloud-integrated infrastructure, and automating system workflows to enhance operational efficiency. He is passionate about cross-functional collaboration and takes pride in delivering robust technical solutions that empower both the Spot AI team and its customers.