Avassa’s Edge AI Solution: Unlock Edge AI at Scale
Run AI where your data is created. Avassa deploys, manages, and secures containerized applications and AI models across thousands of distributed sites, turning trained models into real-time analytics on the factory floor, in the store, and on the move. Beyond MLOps. Without Kubernetes.
What is Edge AI and why does it matter now
Edge AI means running AI models directly on hosts at the edge of the network, close to where data is generated, instead of sending everything to a central cloud. The payoff is immediate: faster decisions, lower bandwidth, better data privacy, and resilience when the network drops.
That’s why applications and Edge AI have moved from experiment to mainstream. It’s how enterprises turn cameras, sensors and machine data into Edge AI for real-time analytics. The hard part isn’t building a model. It’s running and updating that model reliably across hundreds or thousands of locations. That’s the problem Avassa solves.
Combining edge and AI creates a real competitive advantage. But operationalizing it across distributed sites is where most projects stall.
Why running Edge AI and applications at scale is hard
Operational complexity
Every site can have different hardware and network conditions, so a deployment that works in one place fails in another. Models need frequent updates and retraining to stay accurate. And you have to monitor the whole fleet over unreliable, low-bandwidth links.
The limits of traditional tools
MLOps frameworks and Kubernetes are great for building models and running them in the cloud. They were never designed for large fleets of small, sometimes-disconnected edge sites. They lack local autonomy, secure updates, GPU/device discovery and real-time visibility across diverse infrastructure.
Closing the gap
It takes an Edge Platform that extends modern DevOps and MLOps practices all the way to the physical world.
Avassa for Edge AI: the full model lifecycle, automated
Avassa deploys, monitors, updates and secures containerized AI models across distributed edge environments and manages the complete model lifecycle, not just the first deployment. From version control and configuration to continuous updates and observability, your models stay accurate, secure and high-performing in production.
Because models ship as standard containers, Avassa plugs straight into your existing CI/CD and MLOps workflows. Each model runs as a self-contained unit with all dependencies included, such as an inference engine, behaving consistently across mixed edge hardware.
The result: faster iteration, less manual work, confident operations at scale.
How Avassa manages Edge AI deployments
Avassa makes Edge AI deployments easy. You can push a trained model to every relevant site with one click, leveraging GPU discovery, model-serving endpoints, and networking configured automatically. No manual per-site setup, no separate MLOps tooling required for the rollout step itself.
What matters for AI at the edge
Automated model deployment
Push newly trained models to every relevant site at once.
Model-serving endpoints, configured for you
Ingress networking and API components (e.g. FastAPI) set up automatically.
Automatic GPU & device discovery
Avassa finds GPUs, cameras and sensors per host and places the right components on the right hardware.
The whole bundle, not just the model
Inference engines, adaptors, ML libraries, analytics and APIs deployed together as one application.
Offline resilience
Fault-tolerant, self-healing edge clusters that keep inferring with no cloud connection.
Edge-native pub/sub bus
Collect, filter, enrich and aggregate sensor and model data at the source.
Observability
Real-time health and remote troubleshooting.
Intrinsic security
All application data and traffic protected, even on stolen hosts or sniffed networks. Our secrets manager is fully distributed, making certificates and secrets available locally.
Combined with the MLOps tooling of your choice, Avassa is your gateway to distributed Edge AI
Deploy and operate distributed Edge AI applications and trained models with the pipelines your teams already run.
Request free trialBuilt for Physical AI
Physical AI is AI that senses and acts in the real world. Robots, autonomous vehicles, machines and smart infrastructure that perceive, decide and move in real time. It is one of the fastest-growing frontiers of Edge AI, and it lives entirely at the edge: latency, autonomy and safety leave no time for a cloud round-trip.
Avassa is purpose-built for exactly this. It places perception and control models on hosts with the right GPUs and sensors, keeps them running autonomously when the network is down, and lifecycle-manages them safely across an entire fleet of robots, vehicles or production lines.
One platform for every workload at the edge
Edge AI rarely arrives on its own. The same platform that runs your models also runs the rest of your edge estate: MQTT brokers, log collectors, POS and store systems, industrial IoT gateways, protocol adaptors, VMs for not-yet-containerized software. This is where Avassa’s industrial and general-edge heritage matters.
Industrial & IIoT ready
Bridges OT/IT, handles air-gapped and intermittently connected sites, integrates with Linux + hardware combinations and your choice of OS (e.g. Debian/Ubuntu, Red Hat, Wind River, Yocto).
Remote management & automated OS upgrades
Onboard hosts, deploy and patch fleets fully remotely, at scale.
Online & offline autonomy
Self-healing clustering for business-critical applications regardless of connectivity.
Physical + cyber security
Lock down theft-prone sites, protect data in flight and at rest, minimize blast radius with site-local keys and need-to-know secrets.
Multi-tenancy
Let multiple teams or third parties share edge infrastructure safely.
Rich MCP server
Enabling full agentic operations of your edge sites and applications.
Avassa vs. traditional edge platforms
| Feature | Avassa Edge Platform | Traditional edge platforms |
|---|---|---|
| AI & device support | Auto-discovers GPUs, cameras and sensors; places model components on the right hardware; configures serving endpoints | Manual hardware mapping; little native GPU/device awareness |
| Application flexibility | Multi-container applications + VMs with built-in DNS, secrets and app-aware networking | Either single-container/Compose-basic, or heavy Kubernetes not built for the edge |
| Deployments | Rolling & canary updates across large fleets; handles offline/intermittent sites | Limited fleet automation; weak offline support |
| Availability | Fully autonomous sites, local clustering, auto-restart & migration without the cloud | Often depend on cloud connectivity to function |
| Scalability | Tens of thousands of sites from one control plane | Struggle beyond a few hundred sites |
| Security & compliance | Distributed AAA, secrets manager, micro-segmentation, encryption, key rotation | Often lack distributed secrets and full AAA, instead relying on central secrets and certs |
| Integration | Unified REST API + CLI; any OCI container | Partial APIs; custom packaging; loosely-integrated add-ons |
Edge AI use cases across industries
Manufacturing — predictive maintenance, QA & personal safety
Catch equipment issues before failure; reduce downtime.
Retail — smart customer & shelf analytics
Real-time, in-store vision analytics that work offline.
Healthcare — AI-assisted diagnostics
Localized, low-latency decision-making on-site.
Autonomous & robotics — real-time perception & control
Split-second physical-AI decisions at the device.
Smart infrastructure — traffic & surveillance AI
Low-latency processing on-site, insight to the cloud.
Built for real-world edge environments
See Edge AI on Avassa in action
Edge AI solution walkthrough
How to lifecycle-manage AI models at the edge, from a trained model to a running inference endpoint on every relevant site.
Book a live demo
GPU management for AI at the edge
How Avassa discovers GPUs across mixed hardware and places model components on the hosts that can actually run them.
Book a live demo
Hear it from a customer
Before including Avassa as part of our delivery, we had to manually install, version, and troubleshoot our LiDAR devices which quickly became very time-consuming as we began to scale. With Avassa, we are able to deliver our product as-a-service and lifecycle manage the distributed software in a centralized, remote, and secure fashion. This helps us significantly reduce overhead and instead focus on creative competitive new features for our customers.
Learn more about Ida Rehnström
Flasheye went from a standalone product to scalable software-as-a-service
How a LiDAR analytics company replaced manual, per-site rollouts with centrally managed, lifecycle-managed edge deployments.
Read the Flasheye story
Frequently asked questions
What is Edge AI, and how does it differ from cloud AI?
Running models at the network edge, close to the data, for faster processing, better privacy and lower latency than centralized cloud AI.
What challenges does AI at the edge solve?
Network latency, privacy and compliance, and unreliable connectivity — it enables real-time decisions and localized processing.
What is edge AI model management?
Deploying, updating, monitoring and maintaining models on distributed edge devices so they stay accurate and secure. Avassa automates the whole lifecycle.
How does Avassa help with Edge AI deployment?
It’s purpose-built for container applications at scale: autonomous operation, remote lifecycle management, GPU and device discovery, secure secrets, and fine-grained placement.
Does it integrate with my MLOps / CI/CD?
Yes. Declarative, version-controllable specs drop into existing pipelines for deploys, updates and rollbacks.
Which industries benefit most?
Manufacturing, retail, healthcare, logistics, telco and robotics — anywhere data is generated in distributed locations.
How do you secure Edge AI operations?
Encrypted communication, secure secrets distribution, tenant isolation, sealed local storage and audited access.
Can I run Edge AI without Kubernetes?
Yes — Kubernetes-level automation without the overhead. Lightweight, runs on-site and offline, centrally controlled via the Control Tower.
Start unlocking Edge AI at scale
Request a free trial and run Avassa on your own Edge AI applications and models — or book a demo at a time that suits you.
Request free trial