Delegate YAML

Delegate YAML

Delegate YAML

Flightcrew helps engineers catch configuration issues before they’re incidents. No more blindspots, no more tuning. Actually magic numbers.

Flightcrew helps engineers catch configuration issues before they’re incidents. No more blindspots, no more tuning. Actually magic numbers.

Flightcrew helps engineers catch configuration issues before they’re incidents. No more blindspots, no more tuning. Actually magic numbers.

We’ve found 2 Availability risks for the Payments deployment

Here are a few things you can try:

Update HPA - Scaling Behavior


Update resource and requests HPA scaling behavior to prevent OOMs.

Update HPA - Custom Scaler


Scale on QueueLength to allow Payments pods to preemptively scale up when traffic increases.

Check Upstream Services


Timeouts may be related to the upstream Catalog deployment, which recently began using twice as many DB connections per request

Okay confirmed. What can I do?

Can you generate a PR to resize the HPA? I’ll take a look at the others once we’re stable

Timeouts have increased 20% w/w


408 timeouts have increased during peak weekday traffic

Pods are within 10% of Memory limit


You’ve defined a utilization SLO of <90% for Payments. Utilization crossed this threshold three times last week

We’ve found 2 Availability risks for the Payments deployment

Here are a few things you can try:

Update HPA - Scaling Behavior


Update resource and requests HPA scaling behavior to prevent OOMs.

Update HPA - Custom Scaler


Scale on QueueLength to allow Payments pods to preemptively scale up when traffic increases.

Check Upstream Services


Timeouts may be related to the upstream Catalog deployment, which recently began using twice as many DB connections per request

Okay confirmed. What can I do?

Can you generate a PR to resize the HPA? I’ll take a look at the others once we’re stable

Timeouts have increased 20% w/w


408 timeouts have increased during peak weekday traffic

Pods are within 10% of Memory limit


You’ve defined a utilization SLO of <90% for Payments. Utilization crossed this threshold three times last week

We’ve found 2 Availability risks for the Payments deployment

Here are a few things you can try:

Update HPA - Scaling Behavior


Update resource and requests HPA scaling behavior to prevent OOMs.

Update HPA - Custom Scaler


Scale on QueueLength to allow Payments pods to preemptively scale up when traffic increases.

Check Upstream Services


Timeouts may be related to the upstream Catalog deployment, which recently began using twice as many DB connections per request

Okay confirmed. What can I do?

Can you generate a PR to resize the HPA? I’ll take a look at the others once we’re stable

Timeouts have increased 20% w/w


408 timeouts have increased during peak weekday traffic

Pods are within 10% of Memory limit


You’ve defined a utilization SLO of <90% for Payments. Utilization crossed this threshold three times last week

Delegate YAML

Flightcrew helps engineers catch configuration issues before they’re incidents. No more blindspots, no more tuning. Actually magic numbers.

We’ve found 2 Availability Risks for the Payments deployment

Here are a few things you can try:

Update HPA - Scaling Behavior


Update resource and requests HPA scaling behavior to prevent OOMs.

Update HPA - Custom Scaler


Scale on QueueLength to allow Payments pods to preemptively scale up when traffic increases.

Check Upstream Services


Timeouts may be related to the upstream Catalog deployment, which recently began using twice as many DB connections per request

Okay confirmed. What can I do?

Can you generate a PR to resize the HPA? I’ll take a look at the others once we’re stable

Timeouts have increased 20% w/w


408 timeouts have increased during peak weekday traffic

Pods are within 10% of Memory limit


You’ve defined a utilization SLO of <90% for Payments. Utilization crossed this threshold three times last week

Fix issues before they become incidents

Fix issues before they become incidents

Fix issues before they become incidents

Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.

Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.

Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.

Fix issues before they become incidents

Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.

Smart scaling on Kubernetes

Smart scaling on Kubernetes

Smart scaling on Kubernetes

Smart scaling on Kubernetes

Autoscaling becomes easy when you let Flightcrew worry about things like KEDA logic, Karpenter lifecycles and cold-start optimization.

Autoscaling becomes easy when you let Flightcrew worry about things like KEDA logic, Karpenter lifecycles and cold-start optimization.

Autoscaling becomes easy when you let Flightcrew worry about things like KEDA logic, Karpenter lifecycles and cold-start optimization.

Autoscaling becomes easy when you let Flightcrew worry about things like KEDA logic, Karpenter lifecycles and cold-start optimization.

Smarter platform workflows

Smarter platform workflows

Smarter platform workflows

Smarter platform workflows

Personalize abstractions and simplify maintenance with a living database of configuration intelligence.

Personalize abstractions and simplify maintenance with a living database of configuration intelligence.

Personalize abstractions and simplify maintenance with a living database of configuration intelligence.

Personalize abstractions and simplify maintenance with a living database of 'correct' configuration.

Engineering teams use Flightcrew to make
every pull request more reliable

Engineering teams use Flightcrew to make every pull request more reliable

Engineering teams use Flightcrew to make
every pull request more reliable

Engineering teams use Flightcrew to make
every pull request more reliable