Elastic & Auto-Scaling

One architecture. Elastic capacity.

When a legacy PACS hits its limit, enterprises run separate instances, each with its own database and infrastructure. Sirona serves multi-site, multi-state practices on one elastic architecture that adds capacity as studies arrive.

See Sirona in Action

How Sirona is Different

Legacy systems grow by adding instances

Many legacy PACS architectures reach a ceiling as volume grows. After that, the only option is sharding: running multiple separate instances of the same software, each with its own database, its own servers, its own maintenance burden. Each instance is another system to manage, patch, monitor, and keep in sync. Sirona's cloud-native architecture avoids this. Compute scales out automatically as load rises, so the platform grows from a single site to a multi-state enterprise on one architecture, with a single worklist and a single pane of glass.

Infrastructure that scales itself

Kubernetes orchestration, automatic capacity provisioning, and stateless microservices, designed to add capacity without manual intervention.

Kubernetes Orchestration

Horizontal pod autoscaling adjusts service replicas based on real-time CPU, memory, and request load. No manual scaling, no capacity planning.

Automatic Capacity Provisioning

When the platform needs more infrastructure, capacity is added automatically and released when demand drops.

Stateless Microservices

Every backend service — viewer, reporter, worklist, DICOM ingestion — is stateless and horizontally scalable. Add replicas without coordination.

Multi-AZ Redundancy

Services run across multiple AWS Availability Zones. If an availability zone goes down, traffic routes to healthy zones automatically.

Global CDN Delivery

Images are delivered over AWS CloudFront's global edge network (700+ edge locations). Scaling reads doesn't require scaling origin servers, because the CDN carries the load.

Elastic AI Compute

AI inference workloads auto-scale independently from core services. Deploy new models without impacting viewer or reporter performance.

Scaling that works like cloud should

Architecture

One shared platform, each practice's data kept separate

Every Sirona customer runs on the same elastic infrastructure, with each practice's data logically separated and access scoped to its organization. That is why a 50-radiologist practice benefits from the same infrastructure investment as a 500-radiologist health system without paying for dedicated hardware. When a legacy platform needs a second instance to grow, every instance is another system to patch, monitor, and keep in sync.

Each practice's data logically separated and scoped to its organization

Shared elastic compute pool, so resources go where demand is

One architecture as volume grows, with no second instance to stand up

No sharding, no instance splitting, no multi-system management

Operations

Volume spikes that the platform absorbs automatically

Surgery days at a hospital system. Monday morning telerad backlogs. A new site onboarding 200,000 historical studies. An acquisition that doubles your exam volume. These are the scenarios where fixed-capacity PACS struggles. Sirona detects increased load and adds capacity automatically, provisioning new compute when existing capacity is used up. The DICOM ingestion pipeline, the viewer services, and the worklist query engine all scale independently based on their own load.

Horizontal autoscaling responds to load automatically

New compute is provisioned automatically as capacity fills

Each microservice scales independently — ingestion, viewing, reporting

Historical study migration runs as background jobs without impacting live reads

Economics

A per-study subscription replaces hardware CapEx

On-premise PACS requires you to buy servers for your projected peak load plus headroom, then depreciate that hardware over several years while much of it sits idle. Sirona flips this model. Compute scales up during peak hours and down overnight, and you pay one, per-study subscription price, not for compute, so idle capacity is never your cost. There's no hardware to procure, no refresh cycle to plan, no CapEx budget to defend. When your practice adds sites, radiologists, or modalities, there is no lead time and no procurement.

Capacity scales up at peak and down overnight; your price is per study

No server procurement, no hardware depreciation, no refresh cycles

Add sites or volume with no infrastructure lead time

Eliminates CapEx for PACS infrastructure entirely

AI at Scale

Elastic compute makes AI-native radiology possible

Running AI such as Auto-Impressions, AI Prior Summaries, and spine labeling on every study takes compute that scales with volume and model complexity. On-premise GPU clusters are fixed-capacity: you can run three algorithms today, but what about the next one? Sirona's elastic architecture provisions AI compute on demand. When a new AI model is enabled, the platform allocates the resources it needs. When volume surges, AI processing scales alongside clinical workflows. That compute architecture is what makes AI-native radiology practical.

AI inference scales independently from viewer and reporter services

New model deployment doesn't require hardware procurement

AI compute provisioned on demand, with no fixed-capacity hardware to buy

Room to add AI models without a hardware project

Scale without a second system

architecture as you grow, with no second instance to run

99.95%

uptime SLA, with 99.99% measured historically

Multi-AZ

redundancy with automatic failover through every scaling event

servers to buy, maintain, or refresh

Growth without infrastructure constraints

Read the partnership announcement

“This partnership marks the beginning of the cloud-native era in radiology software globally. Sirona has delivered an architecture that is fundamentally different from anything else in the market today — Sirona is unified, and cloud-native from the ground up. For teleradiology, that architectural distinction is existential, not incremental.”

Andy Donaldson

CTO, Everlight Radiology

“In 12 months on Sirona we went from zero to more than a million exams a year. That growth was only possible because we deployed our own AI on top of the Sirona platform — our radiologists consistently read X-rays 20–40% faster on workflows we could not have built on any other platform. You cannot build a modern practice on a legacy PACS — and Sirona is the only modern enterprise-grade platform in radiology today.”

Rustin Rassoli

Founder & CEO, Epsilon Health

Read the ERA case study

FAQs

What does 'sharding' mean and why is it a problem?

Sharding means running multiple separate instances of PACS software because a single instance can't handle the volume. Each shard needs its own database, servers, maintenance, and monitoring, which multiplies complexity and cost. Sirona runs every customer on a single elastic architecture, so growth never means standing up another instance.

How many exams can Sirona handle?

Sirona's architecture scales horizontally, adding compute automatically as volume grows, so growth does not mean standing up a second instance. Sirona has more than 12 million procedures per year under contract.

How fast does auto-scaling respond to demand spikes?

The platform detects increased load and adds capacity automatically, provisioning additional compute when existing capacity is used up. There is no ticket to file, no hardware to order, and no maintenance window.

Do I need to plan for capacity in advance?

No. Sirona's auto-scaling handles capacity automatically. You don't need to forecast exam volumes, request additional servers, or plan upgrade windows. The platform scales with your actual demand in real time.

How does elastic compute support AI workloads?

Sirona's AI services, such as Auto-Impressions and spine labeling, scale independently from core clinical services. When a new AI model is deployed or volume increases, the platform provisions additional compute automatically. No GPU procurement, no fixed-capacity constraints.

What happens during an M&A integration that doubles volume?

The platform scales automatically. Historical study migration runs as background jobs without impacting live reading performance. New exam volume from the acquired practice flows through the same elastic architecture. There is no hardware procurement step, no capacity planning meeting, and no lead time.

How does cloud pricing compare to on-premise hardware costs?

Sirona is priced as a per-study subscription, not as compute. On-premise hardware is provisioned for peak plus headroom, then depreciates while much of it sits idle. With Sirona there is no server procurement, refresh cycle, power, or cooling to budget for, and no staff time spent managing hardware.