[{"data":1,"prerenderedAt":1148},["ShallowReactive",2],{"blog-\u002Fblog\u002F2026\u002F03\u002Fai-usecases-in-factory":3,"featureCatalog":219,"changelog-titles":613,"blog-all-for-related":1147},{"id":4,"title":5,"authors":6,"body":8,"cta":195,"date":198,"description":199,"extension":200,"features":201,"image":202,"lastUpdated":201,"meta":203,"navigation":209,"path":210,"release":201,"seo":211,"sitemap":212,"stem":213,"subtitle":214,"tags":215,"tldr":201,"video":201,"__hash__":218},"blog\u002Fblog\u002F2026\u002F03\u002Fai-usecases-in-factory.md","5 Places Smart Factories Are Already Using AI",[7],"sumit-shinde",{"type":9,"value":10,"toc":184},"minimark",[11,15,18,21,24,27,30,35,38,41,44,53,56,60,63,66,87,90,93,97,100,103,110,114,117,120,135,143,147,150,153,162,165,168,172,175,178,181],[12,13,14],"p",{},"The factory floor wasn't exactly an early adopter of artificial intelligence.",[12,16,17],{},"It's a world built around physical processes: tolerances, throughput, shift schedules. The automation that arrived decades ago was powerful but rigid. Machines that did exactly what you programmed them to do, nothing more.",[12,19,20],{},"That's changed.",[12,22,23],{},"AI is now embedded in manufacturing operations in ways that are easy to miss. Not in the headline-grabbing robots, but in the systems quietly running underneath: predicting failures, catching defects, optimizing energy loads, and compressing deployment timelines.",[12,25,26],{},"The question isn't whether AI has reached the factory floor. It has. The question is where it's actually doing useful work.",[12,28,29],{},"Here are five answers.",[31,32,34],"h2",{"id":33},"predictive-maintenance-on-cnc-machines","Predictive Maintenance on CNC Machines",[12,36,37],{},"Manufacturers lose $50 billion a year to unplanned downtime. Here's what that actually looks like: a CNC machine goes down at 2am, the part isn't in stock, the order misses its window, and the schedule takes three days to recover.",[12,39,40],{},"The problem was never detection. Factories have had sensors for decades. It was interpretation. A temperature spike means nothing without knowing what's normal for that machine, on that material, at that feed rate. Threshold-based alarms can't know that. A model trained on months of operational data from that specific machine can.",[12,42,43],{},"Predictive maintenance learns the baseline and watches for drift. Bearing wear shows up as a frequency shift in vibration data. Spindle imbalance leaves a signature in motor current. None of these are visible on the floor, but all of them are readable in the data, 24 to 72 hours before failure.",[12,45,46,47,52],{},"Harley-Davidson has been running vibration-based predictive maintenance on plant equipment for longer than most manufacturers realize. But you don't need that scale. We built an ",[48,49,51],"a",{"href":50},"\u002Fblog\u002F2026\u002F02\u002Fmotor-anomaly-detector-ai\u002F","AI vibration anomaly detector for industrial motors"," using an autoencoder trained on healthy vibration data, running inference directly in Node-RED. The model learns what normal looks like. When that changes, you get a warning with time to act.",[12,54,55],{},"That's the shift. Not better alarms, but a system that knows the difference between a machine running hard and a machine running out of time.",[31,57,59],{"id":58},"visual-quality-inspection","Visual Quality Inspection",[12,61,62],{},"Manual inspection has a hard ceiling. Put someone at a line running 200 units a minute for four hours and their defect detection rate drops. That's not a training problem. It's physiology.",[12,64,65],{},"Computer vision doesn't have that ceiling. A camera at the inspection station sees every unit, not a sample, at full line speed, catching surface defects, dimensional drift, solder bridges, and weld inconsistencies. In semiconductor fabrication, where a particle smaller than a human hair destroys yield, vision-based inspection isn't an upgrade. It's the only option that makes sense at volume.",[12,67,68,69,75,76,81,82,86],{},"BMW's ",[48,70,74],{"href":71,"rel":72},"https:\u002F\u002Fwww.press.bmwgroup.com\u002Fglobal\u002Farticle\u002Fdetail\u002FT0449729EN\u002Fartificial-intelligence-as-a-quality-booster?language=en",[73],"nofollow","AIQX platform"," runs camera and sensor-based AI quality checks across every plant globally. At Spartanburg, it monitors roughly ",[48,77,80],{"href":78,"rel":79},"https:\u002F\u002Fwww.automotivemanufacturingsolutions.com\u002Fsmart-factory\u002Fquality-visions-ai-inspection-systems-that-learns-from-the-line\u002F2623126",[73],"half a million weld studs daily",". At Regensburg, the ",[48,83,85],{"href":71,"rel":84},[73],"GenAI4Q system"," generates a custom inspection checklist for each of 1,400 vehicles built daily, adapting to every model variant in real time.",[12,88,89],{},"There's a compounding effect worth noting: every defect caught becomes labeled training data. The model improves continuously. The longer it runs, the harder it is to beat.",[12,91,92],{},"Human inspectors still matter for root cause and edge cases. But the first pass, the one that runs on every unit at line speed, is not a job for human eyes anymore.",[31,94,96],{"id":95},"shorter-path-from-pilot-to-production","Shorter Path from Pilot to Production",[12,98,99],{},"Manufacturers consistently underestimate integration time. The technology decisions get made, the use case is clear, and then the project stalls for months. The pilot ran on clean exported data. Production means live data from PLCs running proprietary protocols, historians not designed for real-time access, and network segments that exist for good reasons. Most IIoT initiatives don't fail at the algorithm layer. They fail at the plumbing.",[12,101,102],{},"That gap is expensive. A deployment that takes six months to reach production has a very different ROI profile than one that takes six weeks. And it's precisely here that AI is changing the economics of development. Engineers are generating integration flows from plain language descriptions, tracing protocol mismatches that would have previously burned days, and producing documentation as they build rather than long after. The unglamorous middle work moves faster.",[12,104,105,109],{},[48,106,108],{"href":107},"\u002Fdocs\u002Fuser\u002Fexpert\u002F","FlowFuse Expert"," is built for exactly this. AI assistance embedded directly in the Node-RED editor: inline code completions as you write, next-node predictions as you build, and a chat interface that understands your actual flows and live debug output. Describe the logic you need and it writes the function node. Something behaving unexpectedly? Load your flow and debug logs as context and work through it together. Fewer hours lost to problems that have already been solved a hundred times before.",[31,111,113],{"id":112},"energy-and-hvac-optimization","Energy and HVAC Optimization",[12,115,116],{},"Most smart factory conversations never get to the building itself. That's a mistake.",[12,118,119],{},"HVAC is one of the largest energy costs in a manufacturing facility and one of the least optimized. A factory's thermal load shifts constantly: which lines are running, what materials are being processed, outdoor temperature, occupancy. Rule-based systems deal with that complexity by setting conservative margins everywhere and leaving them there. It works, but you pay for it on every energy bill.",[12,121,122,123,128,129,134],{},"AI learns the actual behavior of the facility and stops defending against conditions that aren't happening. Yokogawa deployed reinforcement learning for HVAC in its semiconductor plant in Japan. In that facility, cleanroom HVAC accounted for roughly ",[48,124,127],{"href":125,"rel":126},"https:\u002F\u002Fwww.isa.org\u002Fintech-home\u002F2022\u002Foctober-2022\u002Ffeatures\u002Fcase-study-ai-based-autonomous-control",[73],"30 percent of total energy consumption",", a figure consistent with, though on the lower end of, what's reported across the semiconductor industry. The result was a ",[48,130,133],{"href":131,"rel":132},"https:\u002F\u002Fmy.avnet.com\u002Fsilica\u002Fresources\u002Farticle\u002Fai-takes-on-growing-role-in-hvac-system-efficiencies\u002F",[73],"3.6 percent reduction"," in total consumption. Modest on paper, significant at scale, and it improves as the model keeps learning.",[12,136,137,142],{},[48,138,141],{"href":139,"rel":140},"https:\u002F\u002Fdeepmind.google\u002Fdiscover\u002Fblog\u002Fdeepmind-ai-reduces-google-data-centre-cooling-bill-by-40\u002F",[73],"DeepMind's data center work"," gets more attention, but that's a controlled, single-purpose environment. A factory is harder. Yokogawa is the more relevant proof of concept.",[31,144,146],{"id":145},"worker-safety-and-ergonomics-monitoring","Worker Safety and Ergonomics Monitoring",[12,148,149],{},"Musculoskeletal disorders are the most common workplace injury in manufacturing. A bad lift, an awkward reach, a sustained bent posture on a repetitive task: the injury doesn't happen dramatically. It accumulates over weeks, then shows up as a compensation claim and a gap on the line.",[12,151,152],{},"Traditional ergonomics assessment catches this late. A consultant observes a workstation, writes a report, and by the time recommendations are implemented, workers have been loading their joints wrong for months.",[12,154,155,156,161],{},"Pose-estimation AI runs overhead cameras continuously and scores every movement in real time against established ergonomic risk frameworks, flagging a bend angle that will cause a back injury long before it materializes. In one ",[48,157,160],{"href":158,"rel":159},"https:\u002F\u002Frsisinternational.org\u002Fjournals\u002Fijrsi\u002Farticles\u002Fai-powered-ergonomics-enhancing-workplace-safety-through-posture-detection\u002F",[73],"manufacturing pilot study",", researchers reported roughly a 25 percent reduction in workplace injuries, with posture compliance improving once workers received real-time feedback. Results will vary by facility, workstation type, and baseline injury rates.",[12,163,164],{},"The privacy question deserves a direct answer: these systems track skeletal keypoints, not faces or identities. What gets logged is joint angle data, not footage of individuals.",[12,166,167],{},"What makes this different from a safety poster is that it doesn't rely on the worker remembering. The system watches every repetition, on every shift.",[31,169,171],{"id":170},"where-to-start","Where to Start",[12,173,174],{},"The five use cases covered here aren't experiments. They're running in production facilities today, on real lines, delivering measurable results. The gap between manufacturers who've deployed AI and those still evaluating it is growing, and it compounds.",[12,176,177],{},"But starting doesn't require a complete digital transformation. Pick one problem that's costing you money right now: unplanned downtime on a critical machine, a defect rate you can't get below, an energy bill that doesn't reflect how efficiently you actually run. That's your first use case.",[12,179,180],{},"The common thread across all five use cases is the same bottleneck: getting operational data to a model reliably and acting on what it returns. That's the integration problem most deployments stall on. FlowFuse is built around solving exactly that. You connect to your PLCs, deploy your AI model, and trigger actions all within the same flow, which is why it's where most manufacturers start and where they keep building.",[12,182,183],{},"The manufacturers seeing results didn't wait for the perfect conditions. They started with one line, proved the value, and expanded. That's still the fastest route from where you are to where you want to be.",{"title":185,"searchDepth":186,"depth":186,"links":187},"",4,[188,190,191,192,193,194],{"id":33,"depth":189,"text":34},2,{"id":58,"depth":189,"text":59},{"id":95,"depth":189,"text":96},{"id":112,"depth":189,"text":113},{"id":145,"depth":189,"text":146},{"id":170,"depth":189,"text":171},{"title":196,"description":197},"Start With One Use Case. Build From There.","Whether it's predictive maintenance, quality inspection, or energy optimization, FlowFuse connects your OT data to AI models without a multi-year transformation.","2026-03-24","Most manufacturers are still debating AI adoption. These five use cases are already running in production, cutting downtime, scrap, energy costs, and injury rates.","md",null,"\u002Fblog\u002F2026\u002F03\u002Fimages\u002Fai-use-case.png",{"keywords":204,"excerpt":205},"AI in manufacturing, smart factory AI, predictive maintenance CNC machines, visual quality inspection AI, computer vision manufacturing, IIoT AI use cases, factory floor AI, HVAC energy optimization manufacturing, AI worker safety ergonomics, manufacturing AI examples, Node-RED IIoT integration, unplanned downtime manufacturing, AI anomaly detection industrial motors, Industry 4.0 AI",{"type":9,"value":206},[207],[12,208,14],{},true,"\u002Fblog\u002F2026\u002F03\u002Fai-usecases-in-factory",{"title":5,"description":199},{"loc":210},"blog\u002F2026\u002F03\u002Fai-usecases-in-factory","Where AI Is Actually Working on the Factory Floor",[216,217],"flowfuse","ai","G1wmjjzKTXTohTzapKYCfQ7frr5kYUMmKpY-k63u3xs",{"id":220,"extension":221,"meta":222,"sections":223,"stem":611,"__hash__":612},"featureCatalog\u002Ffeature-catalog.yml","yml",{},[224,274,375,437,514,593],{"id":225,"title":226,"features":227},"ai-automation","AI & Automation",[228,237,247,257,263,269],{"id":229,"title":230,"description":231,"docsLink":107,"changelog":232,"tiers":236},"flowfuse-expert-ai","FlowFuse Expert AI","Build industrial apps with agents, and query the state of the factory with an agent. Adapt your current hardware and machines so agentic work can be done against them, without ripping and replacing what's already on the plant floor.",[233],{"url":234,"release":235},"\u002Fchangelog\u002F2026\u002F02\u002Fff-expert-update-banner\u002F","2.28",{"edge":209,"hub":209,"fleet":209},{"id":238,"title":239,"description":240,"docsLink":241,"changelog":242,"subfeature":209,"showOnPricing":245,"tiers":246},"flowfuse-expert-support-mode","Support Mode","Chat-based assistance for FlowFuse and Node-RED, including Node-RED instance management through natural language.","\u002Fdocs\u002Fuser\u002Fexpert\u002Fchat\u002F#support-mode",[243],{"url":244,"release":235},"\u002Fchangelog\u002F2026\u002F02\u002Fff-expert-debug-log-context\u002F",false,{"edge":209,"hub":209,"fleet":209},{"id":248,"title":249,"description":250,"docsLink":251,"changelog":252,"subfeature":209,"showOnPricing":245,"tiers":256},"flowfuse-expert-application-building","Application Building","Describe what you want to build and FlowFuse Expert assembles it on your workspace, adding tabs, wiring nodes, and configuring properties.","\u002Fdocs\u002Fuser\u002Fexpert\u002Fchat\u002F",[253],{"url":254,"release":255},"\u002Fchangelog\u002F2026\u002F05\u002Fexpert-application-building\u002F","2.30",{"edge":209,"hub":209,"fleet":209},{"id":258,"title":259,"description":260,"docsLink":261,"subfeature":209,"beta":209,"showOnPricing":245,"tiers":262},"flowfuse-expert-insights-mode","Insights Mode","Connects FlowFuse Expert to MCP servers in your Node-RED instances, enabling real-time data queries and actions through a single chat interface.","\u002Fdocs\u002Fuser\u002Fexpert\u002Fchat\u002F#insights-mode",{"edge":209,"hub":209,"fleet":209},{"id":264,"title":265,"description":266,"docsLink":267,"tiers":268},"mcp-servers","Agentic Operations","Expose your Node-RED flows as tools an AI agent can call directly, so agents can query the state of the factory or trigger actions without custom integration work.","\u002Fnode-red\u002Fflowfuse\u002Fmcp\u002F",{"edge":209,"hub":209,"fleet":209},{"id":270,"title":271,"description":272,"tiers":273},"onnx-integration","ONNX Integration","Run trained machine learning models directly in your flows, including on edge hardware, without sending data out to an external inference service.",{"edge":209,"hub":209,"fleet":209},{"id":275,"title":276,"features":277},"build","Build",[278,284,290,300,306,312,316,322,328,336,342,346,350,359,367],{"id":279,"title":280,"description":281,"docsLink":282,"tiers":283},"edge-development","Edge Development","Develop and test Node-RED flows directly on edge devices with a remote editor proxy.","\u002Fdocs\u002Fdevice-agent\u002Fquickstart\u002F",{"edge":209,"hub":245,"fleet":209},{"id":285,"title":286,"description":287,"docsLink":288,"tiers":289},"private-npm-registry","Custom Node-RED Nodes","Create and manage your own private npm registry for Node-RED nodes, so you can share custom nodes across your team and devices without publishing them publicly.","\u002Fdocs\u002Fuser\u002Fcustom-npm-packages\u002F",{"edge":209,"hub":209,"fleet":209},{"id":291,"title":292,"description":293,"docsLink":294,"changelog":295,"tiers":299},"flowfuse-tables","FlowFuse Tables","A managed PostgreSQL database for every application, so you can store and query structured data without standing up and maintaining your own database.","\u002Fdocs\u002Fuser\u002Fff-tables\u002F",[296],{"url":297,"release":298},"\u002Fchangelog\u002F2026\u002F07\u002Fexpert-tables-automation\u002F","2.33",{"edge":209,"hub":209,"fleet":209},{"id":301,"title":302,"description":303,"docsLink":304,"tiers":305},"persistent-files","File Storage","Store and retrieve files from your Node-RED flows, with automatic replication and backup across your devices and hosted instances.","\u002Fdocs\u002Finstall\u002Ffile-storage\u002F",{"edge":209,"hub":209,"fleet":209},{"id":307,"title":308,"description":309,"docsLink":310,"showOnPricing":245,"tiers":311},"persistent-context","Persistent Context","In-memory values defined in a Node-RED flow persist across project restarts and upgrades.","\u002Fdocs\u002Fuser\u002Fpersistent-context\u002F",{"edge":209,"hub":209,"fleet":209},{"id":313,"title":314,"showOnPricing":245,"tiers":315},"static-assets","Static Assets",{"edge":209,"hub":209,"fleet":209},{"id":317,"title":318,"description":319,"docsLink":320,"tiers":321},"team-library","Team Library","Set up standard nodes and flows that can be shared with all team members across your organisation.","\u002Fdocs\u002Fuser\u002Fshared-library\u002F",{"edge":209,"hub":209,"fleet":209},{"id":323,"title":324,"description":325,"docsLink":326,"tiers":327},"personalised-multi-user-dashboards","Personalised Multi-User Dashboards","Build applications that provide unique data to each logged-in user using personalised multi-user dashboards.","https:\u002F\u002Fdashboard.flowfuse.com\u002Fuser\u002Fmulti-tenancy.html",{"edge":209,"hub":209,"fleet":209},{"id":329,"title":330,"description":331,"changelog":332,"subfeature":209,"showOnPricing":245,"tiers":335},"dashboards-view","Dashboards View","Browse and open every dashboard across your team from a dedicated Dashboards view, at both team and application level, without leaving FlowFuse.",[333],{"url":334,"release":298},"\u002Fchangelog\u002F2026\u002F07\u002Fteam-and-application-dashboards\u002F",{"edge":209,"hub":209,"fleet":209},{"id":337,"title":338,"description":339,"docsLink":340,"tiers":341},"blueprints-converge","Blueprints","Ready-made starting points for your apps, from cross-team Converge templates to OT and IT specific blueprints.","\u002Fdocs\u002Fuser\u002Fconcepts\u002F#blueprint",{"edge":209,"hub":209,"fleet":209},{"id":343,"title":344,"subfeature":209,"showOnPricing":245,"tiers":345},"blueprints-ot-apps","Blueprints - OT APPS",{"edge":209,"hub":245,"fleet":209},{"id":347,"title":348,"subfeature":209,"showOnPricing":245,"tiers":349},"blueprints-it-apps","Blueprints - IT APPS",{"edge":245,"hub":209,"fleet":245},{"id":351,"title":352,"description":353,"changelog":354,"showOnPricing":245,"tiers":358},"immersive-editor-snapshots","Snapshot Details in Immersive Editor","View and manage snapshot details directly inside the immersive editor without leaving your editing session.",[355],{"url":356,"release":357},"\u002Fchangelog\u002F2026\u002F03\u002Fsnapshot-detail-modal-immersive-editor\u002F","2.29",{"edge":209,"hub":209,"fleet":209},{"id":360,"title":361,"description":362,"changelog":363,"showOnPricing":245,"tiers":366},"immersive-editor-drawer","Customisable Immersive Editor Drawer","Pin, move, resize, or full-screen the immersive editor drawer. Your preferences are remembered between sessions.",[364],{"url":365,"release":255},"\u002Fchangelog\u002F2026\u002F04\u002Fimmersive-editor-drawer\u002F",{"edge":209,"hub":209,"fleet":209},{"id":368,"title":369,"description":370,"changelog":371,"showOnPricing":245,"tiers":374},"embedded-editor-tab-title","Embedded Editor Browser Tab Title","The browser tab title updates to reflect the active Node-RED canvas tab when working in the embedded editor.",[372],{"url":373,"release":357},"\u002Fchangelog\u002F2026\u002F03\u002Fembedded-editor-tab-title\u002F",{"edge":209,"hub":209,"fleet":209},{"id":376,"title":377,"features":378},"deploy","Deploy",[379,385,394,400,406,412,418,424,429],{"id":380,"title":381,"description":382,"docsLink":383,"tiers":384},"hosted-instances","Cloud Instances","Run Node-RED instances managed and hosted by FlowFuse.","\u002Fdocs\u002Fuser\u002Fintroduction\u002F#creating-a-node-red-instance",{"edge":209,"hub":209,"fleet":209},{"id":386,"title":387,"description":388,"docsLink":389,"changelog":390,"tiers":393},"edge-devices","Edge Instances","Deploy and mange your Node-RED instances on edge PLCs and gateways, with full visibility and control from the cloud.","\u002Fdocs\u002Fdevice-agent\u002Fintroduction\u002F",[391],{"url":392,"release":235},"\u002Fchangelog\u002F2026\u002F02\u002Fdevice-agent-nodejs-options\u002F",{"edge":209,"hub":245,"fleet":209},{"id":395,"title":396,"description":397,"docsLink":398,"tiers":399},"custom-hostnames","Custom Hostnames","Access your Node-RED application via your own domain name.","\u002Fdocs\u002Fuser\u002Fcustom-hostnames\u002F",{"edge":245,"hub":209,"fleet":245},{"id":401,"title":402,"description":403,"docsLink":404,"tiers":405},"mqtt-broker","MQTT Broker","Manage and create MQTT clients to transport data for efficient messaging and communication within your applications.","\u002Fdocs\u002Fuser\u002Fteambroker\u002F",{"edge":209,"hub":245,"fleet":209},{"id":407,"title":408,"description":409,"docsLink":410,"showOnPricing":245,"tiers":411},"project-nodes","Project Nodes aka seamless project comms","FlowFuse Project Nodes enable the passing of data and messages between your Node-RED projects.","\u002Fdocs\u002Fuser\u002Fprojectnodes\u002F",{"edge":209,"hub":209,"fleet":209},{"id":413,"title":414,"description":415,"docsLink":416,"tiers":417},"devops-pipelines","DevOps Pipelines","Set up different environments for development, testing, and production Node-RED instances to support a full software delivery lifecycle.","\u002Fdocs\u002Fuser\u002Fdevops-pipelines\u002F",{"edge":209,"hub":209,"fleet":209},{"id":419,"title":420,"description":421,"docsLink":422,"tiers":423},"git-integration","Git Integration","Back up your flows to a remote Git repository through a DevOps Pipeline. Supports GitHub and Azure DevOps repositories.","\u002Fdocs\u002Fuser\u002Fdevops-pipelines\u002F#git-repository-stage",{"edge":245,"hub":209,"fleet":245},{"id":425,"title":426,"description":427,"docsLink":422,"subfeature":209,"showOnPricing":245,"tiers":428},"git-integration-github","GitHub","Push and pull snapshots to GitHub repositories through DevOps Pipeline Git Stages.",{"edge":245,"hub":209,"fleet":245},{"id":430,"title":431,"description":432,"docsLink":422,"changelog":433,"subfeature":209,"showOnPricing":245,"tiers":436},"git-integration-azure","Azure DevOps","Push and pull snapshots to Azure DevOps repositories through DevOps Pipeline Git Stages.",[434],{"url":435,"release":357},"\u002Fchangelog\u002F2026\u002F03\u002Fazure-dev-ops-gitops\u002F",{"edge":245,"hub":209,"fleet":245},{"id":438,"title":439,"features":440},"operate-maintain","Operate & Maintain",[441,447,453,458,466,470,474,479,485,491,496,502,506,510],{"id":442,"title":443,"description":444,"docsLink":445,"tiers":446},"snapshots","Snapshots & Version History","Automatic snapshots on remote devices and hosted instances, plus a full version history timeline so you can roll back to any prior state.","\u002Fdocs\u002Fuser\u002Fsnapshots\u002F",{"edge":209,"hub":209,"fleet":209},{"id":448,"title":449,"description":450,"docsLink":451,"subfeature":209,"showOnPricing":245,"tiers":452},"auto-snapshot-remote","Auto Snapshot (Remote)","Automatically capture a snapshot every time a remote instance is deployed, so you always have a recoverable history of what was running on each device.","\u002Fdocs\u002Fuser\u002Fsnapshots\u002F#auto-snapshots",{"edge":209,"hub":209,"fleet":209},{"id":454,"title":455,"description":456,"docsLink":451,"subfeature":209,"showOnPricing":245,"tiers":457},"auto-snapshot-hosted","Auto Snapshot (Hosted)","Automatically capture a snapshot every time a hosted instance is deployed, so you always have a recoverable history of what was running.",{"edge":209,"hub":209,"fleet":209},{"id":459,"title":460,"description":461,"changelog":462,"subfeature":209,"showOnPricing":245,"tiers":465},"snapshot-comparison","Snapshot Comparison","Compare two snapshots side-by-side with a navigable diff view. 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SSO",{"path":691,"title":692},"\u002Fchangelog\u002F2024\u002F01\u002Fstreamlined-device-assignment","Streamlined Device assignment",{"path":694,"title":695},"\u002Fchangelog\u002F2024\u002F02\u002Fdevice-auto-snapshot","Device Auto Snapshots",{"path":697,"title":698},"\u002Fchangelog\u002F2024\u002F02\u002Fdevice-instance-audit-logs","Device Instance Audit Logging",{"path":700,"title":701},"\u002Fchangelog\u002F2024\u002F02\u002Fdevice-onboarding-improvements","Device Onboarding Improvements",{"path":703,"title":704},"\u002Fchangelog\u002F2024\u002F02\u002Fdevice-pricing-change","Pricing change for Devices",{"path":706,"title":707},"\u002Fchangelog\u002F2024\u002F02\u002Finstance-auto-snapshots","Instance Auto Snapshots",{"path":709,"title":710},"\u002Fchangelog\u002F2024\u002F02\u002Fpostgresql-upgrade","PostgreSQL Version update",{"path":712,"title":713},"\u002Fchangelog\u002F2024\u002F03\u002Fbearer-token-authentication","Bearer Token Authentication for Node-RED 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