The brief
Traffic that can fluctuate dramatically — from roughly 10,000 requests to bursts approaching one million — while operating on fixed, privately hosted infrastructure is the workload Vega was designed for.
Handling high traffic wasn’t the challenge on its own. The system had to absorb sudden traffic spikes without waiting for new compute instances or pods to start, all while operating under a workload where some sensor-data requests could be safely dropped during extreme overload.
Why Vega was built this way
Making fixed infrastructure work harder was the goal.
A difficult operating reality shaped this high-throughput inference system: traffic can arrive in sudden, extreme bursts, while the available compute remains fixed and privately hosted.
Simply adding more servers isn’t always possible in that environment. New capacity may take too long to become available, and scaling after a traffic spike has already arrived may not solve the immediate problem.
A different approach was taken instead. Vega absorbs incoming requests, groups them intelligently, sends them through inference workers in efficient batches, and protects the system when demand exceeds safe capacity.
When infrastructure cannot scale quickly enough, make every available unit of compute do more useful work.
Efficient utilisation of the compute capacity already available is what the system was designed around, rather than relying on reactive autoscaling.
The problem behind Vega
Roughly 10,000 requests to bursts approaching one million — that’s the range this workload could span. Accepting a large number of HTTP requests wasn’t the hard part; keeping the entire system stable when demand changed faster than the infrastructure could respond was.
The service needed to:
- Accept bursts without immediately overwhelming inference workers.
- Use fixed compute as efficiently as possible.
- Keep request handling separate from model execution.
- Prevent queues from growing without control.
- Handle overload without causing a full system failure.
- Support requests that could be safely dropped when capacity was exhausted.
Allowing every request to remain queued until latency and memory usage became unmanageable was riskier, for this sensor-data workload, than a controlled drop.
The architecture
A dynamic request-batching pipeline sits at the core of Vega. Incoming inference requests get placed into a shared queue and grouped into batches before being sent to the model — rather than executing a separate inference operation for every request, multiple requests can share a model forward pass.
The resulting flow is:
Clients → Nginx → FastAPI → Shared Queue → Dynamic Batcher → Inference Workers → Model
Request ingestion stays separate from model execution this way, letting the inference layer process bursts efficiently.
Dynamic batching
Configurable batching constraints determine how Vega dynamically groups incoming requests. A batch can be dispatched when it reaches its maximum size or when its waiting window expires — creating a balance between throughput and latency: requests get an opportunity to share compute, but they aren’t held indefinitely waiting for a full batch.
For workloads where input sizes vary, length-aware batching can also group requests with similar characteristics, reducing unnecessary padding and improving computational efficiency.
Shared inference queue
Redis Streams serves as a shared request queue in the distributed configuration, letting multiple workers or application processes participate in a common inference pipeline rather than maintaining isolated queues.
Traffic fragmentation gets prevented by a shared queue, and inference workers gain a broader pool of pending work from which efficient batches can be constructed.
Load shedding and backpressure
Individual sensor-data requests being droppable when the system reached its safe capacity was a key requirement of the workload. Vega treats this as a deliberate reliability mechanism rather than an unexpected failure.
Queues are bounded, and when the system is under extreme pressure, additional work can be rejected instead of allowing an unbounded backlog to consume resources and cause the entire service to become unstable. Explicit backpressure results:
Normal load → Queue → Batch → Inference
Extreme load → Capacity reached → Controlled request drop
Losing individual requests was preferable, for this workload, to allowing overload to cascade into system-wide failure.
Why we did not use autoscaling
This particular workload wasn’t a good fit for reactive autoscaling. Traffic could increase faster than new pods or compute resources could become available, and the service also needed to operate on private infrastructure — making rapid elastic provisioning less practical.
Getting more useful work from the infrastructure already running is where Vega focused instead of scaling after the spike arrived. Dynamic batching improves compute utilisation, shared queuing coordinates work, and controlled load shedding prevents overload from destabilising the service.
Built for bursty traffic
Average request volume doesn’t describe this workload well enough. Relatively modest traffic could suddenly turn into an extreme burst, so Vega was designed to absorb these bursts through queueing and batching while maintaining predictable behaviour when capacity was exceeded.
A controlled buffer between unpredictable traffic and comparatively expensive model execution is what the inference service becomes as a result.
Private infrastructure
Deployment within private infrastructure, rather than dependence on cloud-based autoscaling, was the design target for Vega. Organisations that need to keep inference workloads inside their own environments or operate under fixed compute constraints find the architecture suitable because of it.
Observability and benchmarking
Performance was treated as an engineering problem that needed to be measured. Benchmarking and monitoring capabilities are part of the project, evaluating throughput, latency, batching behaviour, and overload characteristics.
Batch sizes, waiting windows, queue capacity, and worker configuration can all get tuned around the actual workload and available hardware because of these measurements.
The ~1M-request figure reflects a controlled burst-load test run against the client’s fixed private infrastructure, mirroring the peak-traffic scenario the system was built to withstand.
The engineering principle
A simple systems principle guided how Vega was built:
When you cannot scale the infrastructure fast enough, make every unit of existing compute do more useful work.
Compute utilisation improves through dynamic batching. Workers get coordinated through shared queuing. Backpressure comes from bounded queues. The system gets protected during overload by load shedding. Together, these mechanisms create inference infrastructure designed to remain predictable when traffic becomes extreme.
Project highlights
- Designed for traffic ranging from approximately 10K to 1M requests
- Private-infrastructure deployment model
- Dynamic inference batching
- Redis Streams-based shared request coordination
- Distributed inference workers
- Bounded queues and controlled load shedding
- Backpressure under extreme traffic
- Length-aware request batching
- Nginx request gateway and load balancing
- Monitoring and benchmarking infrastructure
- Python/FastAPI-based inference service