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At peak our site slows down or falls over

Try thisWatch both sides under the same traffic spike.

I want this
Scaling under load
650 req/s
Fixed server
1 server, fixed capacity
Web server
Healthy
Response
×1
Response107 ms
Errors0
Autoscaling cloud
instances follow the load
Autoscaler
Healthy
2× instances
×2
Response83 ms
Errors0
Downtime0 min
Uptime100%
Plus and minus zoom, zero shows the whole demo, arrows move the zoomed demo.
  • 2,000requests per second at the demo peak
  • 1 → 8instances as the load grows
  • 1fixed-capacity server for comparison

The demo runs on made-up data. Figures describe the demo set-up, not client results.

I want this
What it solves

Capacity that follows the load and costs you can estimate in advance

Handle a traffic spike without rewriting your application.

Black Friday, an article that spreads, a campaign: visitors arrive all at once. A fixed-capacity server gets overwhelmed; cloud infrastructure adds instances and removes them again after the peak. The demo runs the same spike on both sides: a single server on the left, a cloud that scales with the load on the right.

Who it is forE-shops, media, campaigns with burst traffic

Delivered as part ofCloud and infrastructure

The difference the demo shows

Without scaling: 503 errors, customers leaving, a scramble at night. With scaling: the load is detected, instances are added and response times stay steady.

Head of engineering

You pay for the capacity you actually use, night-time firefighting drops and the infrastructure is ready for growth.

How it worksStep by step

  1. Forecasts the peak

    Combines history, the campaign calendar, ad spend and queue length. It forecasts load ahead, not once things slow down.

  2. Adds capacity

    Starts additional instances before the peak. Stateless services come up at once; databases have reserved capacity.

  3. Spreads the load

    Load balancing and edge caching spare the origin. Faulty instances are taken out without waiting.

  4. Keeps response times steady

    Response times stay steady even at several times the normal load. Users do not notice the peak.

  5. Scales back after the peak

    Instances shut down after the peak and the infrastructure returns to normal capacity. In the morning you see what the peak cost.

What it doesWhat it needs in production

  • Scaling ahead of time

    Load is forecast from history and live signals such as campaigns or queue length. Capacity is added before the peak, not during it.

  • A standby region

    Running in two or more regions, with automatic failover when one goes down.

  • Sized to actual use

    Regular comparison of usage against provisioned capacity. Recommendations on where to cut, where to add and where reservations pay off.

  • Containers and Kubernetes

    Sensible defaults from day one, without months of learning before the platform is reliable.

  • Zero-downtime releases

    A new version goes to a small share of traffic first. If the error rate rises, it rolls back automatically.

  • Observability from day one

    Logs, metrics and tracing wired in from the start, so an incident can be traced.

  • Cost reporting

    Cost by service and environment, with trend and forecast. Management gets one page, engineers get the detail.

  • Availability targets and alerts

    Each service has a defined availability target. Alerts come when it approaches the limit, not at every wobble.

Who it is forWhere it makes sense

  • E-shops

    Black Friday, Christmas, sales. Capacity is added along the expected curve, not in a panic, and you know the cost in advance.

  • Media and publishing

    An article that spreads, a new video, breaking news. Edge caching and a scaled origin so the first wave does not bring the site down.

  • Growing software companies

    From hundreds to millions of users. A standby region, zero-downtime releases and cost reporting so growth does not eat the margin.

  • Public sector

    Predictable surges: election night, results pages, filing deadlines. Infrastructure that holds without a war room.

  • Online gaming and live events

    Peaks around matches, payouts and leaderboards. Response times under control even at several times the normal load, with a standby region ready.

  • API providers and fintech

    Latency-sensitive interfaces with bursty client traffic. Scaled compute and queues that absorb the bursts.

IntegrationsRuns on what you already have

  • AWS
  • Microsoft Azure
  • Google Cloud Platform
  • Kubernetes (EKS / AKS / GKE)
  • Cloudflare
  • Vercel
  • Fastly
  • Terraform / Pulumi
  • Datadog / Grafana
  • Custom observability stack

The list is not exhaustive. We connect systems that are not here as long as they have an interface.

Want this in your business?

A no-obligation call with someone who builds these. We go through your brief and say what is realistic and what is not.

Book a consultation