Scalability

The application is reasonably scalable for its current scope. The write path, messaging, projection processing, and read path are already separated. This gives scope for horizontal or vertical scaling to meet any load.

API: scales horizontally on App Service. It is largely stateless, so multiple instances can serve commands and queries. When necessary it can be scaled up for heavier workloads.
Cosmos DB event store: scales well for append-heavy workloads, but partition-key design and hot partitions become important as event volume increases.
Service Bus: decouples command processing from projection work and provides buffering during spikes.
Projection Function: can scale independently from the API as message volume increases.
Azure SQL read model: supports efficient query models, but will usually become the first constrained shared resource for read-heavy workloads.
Angular UI: static assets are not a meaningful scalability concern compared with backend services and data stores.

Scaling plan

Area	Likely first limit	Plan
App Service	CPU / instance count on B1	Scale out, then a higher SKU
Data	DTU / connection pool on Basic SQL	Indexing, then a reserved SKU
Auth	Chatty login or session store	Cache identity, avoid per-request secrets
Chatty I/O	Unbounded lists or N+1 queries	Pagination and query budgets

Method to evaluate claims on this page

Using a dedicated load-testing tool should identify bottle-necks. A useful test would include simulating increasing concurrent users creating, approving, and querying blast plans while measuring API latency, throughput and error rates.

Expected first bottlenecks

The first bottleneck I would expect is Azure SQL, particularly if read traffic grows substantially or projection updates become frequent. The current design centralises read-model persistence in a relational database, so query concurrency, connection-pool pressure, indexing, and write contention will matter before the stateless API itself becomes a problem.

The next likely bottleneck is the projection pipeline:

Cosmos event append
    ↓
Service Bus topic
    ↓
Projection Function
    ↓
Azure SQL

If events are produced faster than projections can be written, the Service Bus subscription backlog will grow. This is not a failure as the queue provides buffering but delay in projections will result in stale information in the projection store i.e. plan status may be incorrect when queried. Increasing function concurrency can improve throughput but may produce issues when multiple function instances are projecting (updating) a plan simultaneously.

Areas to monitor

Early indicators may be:

Service Bus subscription active message count
Projection Function execution duration
SQL CPU, DTU/vCore utilisation, query duration, connection pressure and blocking
Cosmos RU consumption, throttling (429) and partition distribution
API response time, request rate and failure rate