PinPole is the only platform with pre-deployment traffic simulation across AWS, GCP, and Azure. One workflow — Design, Simulate, Optimise, Deploy — on a single canvas. Catch throttling limits, quota gaps, and cost overruns at design time, not in production.
Lambda concurrency was too low for your launch spike. It throttled
at 4× expected load. You found out when PagerDuty fired at 2am and the on-call
engineer spent three hours tracing a cascading timeout back to a limit you could
have changed in five minutes at design time.
DynamoDB WCU was $3,200 over your estimate at actual traffic. The pricing calculator
didn't model your write pattern — and this applies equally to GCP and Azure cost tools. The architect's back-of-envelope
didn't either. The CFO's question at the next monthly review was pointed.
The API Gateway timeout was 29 seconds. Your Lambda timeout was 30.
Under load, the p99 response time exceeded the Gateway limit. The errors looked
like Lambda failures. They weren't. You fixed both, in production, during a
release freeze.
PinPole catches those mistakes before you deploy.
Drag services from AWS, GCP, or Azure onto the canvas and wire them together. PinPole validates compatibility and connection direction in real time — only architecturally valid integrations are allowed. Invalid wiring is blocked before it is created, not after it is deployed. The same four-step workflow — Design, Simulate, Optimise, Deploy — works identically across all three clouds.
Lambda, Cloud Run, Azure Functions. DynamoDB, Firestore, Cosmos DB. SQS, Pub/Sub, Service Bus. Mix services across providers on a single canvas.Select a traffic pattern. Set your RPS range. Run the simulation. PinPole models how each service in your architecture behaves under that load — concurrency limits, throughput caps, queue depths, timeout chains — and surfaces the results as per-node metrics in real time.
After each simulation, PinPole's recommendations engine analyses your architecture and returns findings categorised by severity. Every recommendation includes the specific service, the specific problem, and the specific fix.
A configuration that will cause a failure or significant cost overrun under the simulated load. Requires action before deployment.
Lambda / event-processor (AWS)A configuration that is suboptimal for the simulated load but will not cause a failure at current settings. Review before deployment.
Firestore / events-collection (GCP)An observation about the architecture that may be relevant depending on your requirements.
Azure Functions / async-handler (Azure)Every recommendation can be applied with one click. PinPole updates the affected node's configuration and re-queues a simulation to validate the change. The previous simulation result is preserved in execution history.
When the architecture is ready, deploy directly to your AWS, GCP, or Azure account. The same four-step workflow — Design, Simulate, Optimise, Deploy — works identically across all three clouds. PinPole never stores long-lived credentials. Every deployment step is logged in the audit trail.
ST, UAT, or Production. Select your target cloud provider — AWS, GCP, or Azure. Multi-environment configuration is set once per workspace.STS AssumeRole for AWS, Workload Identity Federation for GCP, Managed Identity for Azure. No secrets leave your browser session.Terraform HCL or
AWS CDK, or Pulumi from any canvas state — before or after simulation. The export reflects the exact canvas configuration at the moment of export, including any changes applied from recommendations.
AWS STS AssumeRole, GCP Workload Identity Federation, and Azure Managed Identity for cross-account access. No long-lived credentials are stored or transmitted beyond the active session. Provider-specific configuration is documented in the deployment guide.
I ran a Spike simulation at 8× our projected peak. Lambda concurrency hit the limit at 4× actual load. We reconfigured provisioned concurrency, reran the simulation, and confirmed the fix before we touched the deployment pipeline. That sequence would have been a production incident.
We modelled a DynamoDB table for a new event processing pipeline. The recommendation flagged that provisioned capacity at our simulated peak would cost $2,100 per month more than on-demand. We switched before provisioning. The saving was visible in the first AWS bill.
We replaced draw.io, the AWS Pricing Calculator, and three separate load testing scripts with one canvas session. The architecture diagram is the simulation config. The simulation config is the IaC. There is no translation step between them.
We'd been AWS-only for four years. When we evaluated GCP for our data pipeline the big unknown was cost — we had no feel for Cloud Run or Firestore pricing under real load. PinPole let us simulate the GCP architecture at our actual traffic before we committed to a single resource. The cost step alone justified the tool.
Pre-deployment traffic simulation across AWS, GCP, and Azure is absent from every one of them.
Load testing tools — k6, Gatling, JMeter — require deployed infrastructure. PinPole simulates traffic against architecture designs on any cloud, before a single resource is provisioned.
See competitive comparisons →No cloud account required on Free or Pro. No credit card to start. Every paid plan includes a 14-day free trial with full feature access.
For engineers evaluating PinPole or building personal projects.
Open canvas — it's freeFor senior engineers and architects who need the full simulation and deployment workflow.
Start Pro trialFor platform and DevOps engineering teams collaborating on shared infrastructure.
Start team trialFor regulated enterprises with multi-account environments, compliance requirements, and security review processes.
Talk to usNo lock-in contracts. Upgrade, downgrade, or cancel anytime. Charges are prorated when you upgrade mid-cycle.
No cloud account required. No infrastructure spend.
Design on the canvas across AWS, GCP, or Azure. Simulate at launch traffic, apply the recommendations, and deploy — or export to Terraform and run through your own pipeline.