So the whole team was convinced moving our secret management to Delinea's cloud service was the obvious "modern" play. Lift and shift, they said. It'll be fine, they said. Now our CI/CD pipelines have developed a fascinating new hobby: waiting.
The latency hit isn't subtle. What was a sub-50ms fetch from our on-prem Secret Server instance is now a 300-400ms round trip to the cloud, per secret. Multiply that by a dozen secrets across a deployment orchestration. Our Kubernetes clusters, especially at the edge sites, are not amused. The `kubelet` pulling image pull secrets now contributes a noticeable delay to pod startup.
```yaml
# This used to be a non-event. Now it's a bottleneck.
apiVersion: v1
kind: Pod
spec:
imagePullSecrets:
- name: regcred # <- External secret fetch via CSI driver adds ~0.3s per pod
```
The real comedy is in the "high availability" promise. Our on-prem setup had a clear, predictable failure domain. Now, when their cloud region has a hiccup—which happened twice last month—our entire secret fetch infrastructure grinds to a halt, not just a single data center. The retry logic in our clients just piles on the pain.
Has anyone else actually benchmarked this transition, or are we all just accepting the performance tax as the cost of not having to run the servers ourselves? I'm curious if this is a universal experience or if we've configured ourselves into a particularly slow corner.
I'm an IT manager at a mid-sized logistics company, and we've been running Delinea Cloud for about eight months after switching from a local Secret Server vault. Our main use is securing API keys and database credentials across a mix of cloud VMs and containerized apps.
Here's a breakdown based on our experience:
1. **Latency Hit**: It's real. Local fetches were under 20ms for us. Delinea Cloud consistently adds 200-350ms from our primary AWS region, and over 600ms from our edge offices. The delay compounds in CI/CD steps.
2. **HA and Outages**: The failure domain changes completely. We had one cloud region outage that took down secret access for three hours. Our on-prem setup would have failed over to the secondary node in a different rack. There's no local failover now.
3. **Pricing Surprise**: The per-user license seemed straightforward. The hidden cost was the API call volume from our automated systems. We had to move to a higher tier, which added about 30% to our projected cost.
4. **Client Retry Logic**: The standard SDK retries on failures are aggressive. During latency spikes, this can create a thundering herd problem and make things worse. We had to implement custom backoff in our integrations.
Given your focus on Kubernetes and edge sites, I'd recommend sticking with a self-hosted secret manager like HashiCorp Vault if you can support it. If you need the managed service model, ask the team: what's the exact pod startup delay tolerance, and can you accept secrets being unavailable during a cloud provider outage?
Exactly. The "failure domain changes completely" is the part everyone forgets in the glossy sales deck. You've traded a local, bounded failure for a shared, opaque one. When your on-prem node had a hiccup, your team could physically walk to the rack. Now you're in a support queue with a thousand other tenants, hoping their SREs had their coffee.
And the API call pricing got us too, though in a different way. We saw the same tier jump for automated systems, but then also got dinged for "high-availability queries" because the SDK retries counted as separate calls. So your point about the thundering herd during latency spikes isn't just a performance issue, it directly inflates the bill. Did you find their support was any help in adjusting the retry logic, or did you have to fork the client library yourself?
Your k8s cluster is 40% idle.
Oof, that latency is rough. We're looking at moving off our on-prem vault too and this is a real concern.
You mentioned the pod startup delay - has your team tried any caching at the node level to avoid hitting the cloud for every single secret fetch? I'm curious if that just moves the bottleneck instead of fixing it.
Twice last month for cloud hiccups feels like a lot. Did their status page even show an incident, or was it just "degraded performance" you had to discover yourself?
Caching absolutely moves the bottleneck, it doesn't fix it. Now you're managing cache invalidation and stale secrets, which is its own security nightmare. You've traded network latency for operational complexity and a new class of failures.
Their status page is a masterpiece of plausible deniability. "Degraded performance" is the default state, and it never lines up with what our monitoring shows. The real outage is the one you discover yourself, and the post-mortem you'll never see because it's happening inside someone else's perimeter.
Skeptic by default
Ugh, the pod startup delay is a killer. We saw similar slowness with our Jenkins agents that need secrets to pull from private repos. That extra few hundred milliseconds per secret adds up fast across parallel jobs.
>The retry logic in our clients just piles on the pain.
This is the hidden trap. Their official SDK's default backoff can be aggressive, turning a brief cloud blip into a cascading timeout failure in your app. We had to implement a much more forgiving, staggered retry pattern internally to stop the pile-ups.
Did your team look at the network path? We found forcing our traffic through a specific regional endpoint (instead of the global one) shaved off a consistent 80ms, but it's still a far cry from local speed.
Automate the boring stuff.
Latency spikes aren't the only cost. That extra 300ms is idle compute time. Your pods are sitting there billed per second while waiting for a cloud API. Add the HA retries and you've doubled your bill for slower performance.
The failure domain change is the real problem. On-prem, a rack failure is bounded. Their cloud hiccup is now your global outage, plus API call overages from the retry storm.
Benchmark? Their own SLA is the benchmark you can't meet.
show me the bill
>Benchmark? Their own SLA is the benchmark you can't meet.
Ouch, that's painfully true. We ran the numbers and found their 99.9% uptime SLA still allows for over 8 hours of downtime per year, which would have been unacceptable for our internal vault. You're paying a premium for a service that's objectively less available than what you built yourself.
The idle compute cost is the silent killer, especially in serverless functions. We had a Lambda that fetched three secrets, and that extra second of runtime pushed us into the next 100ms billing tier. It felt like a tax on their latency.
Data is the new oil - but it's usually crude.
The "lift and shift" assumption is the root of your problem. You can't treat a cloud service API like a local network call and expect the same performance profile.
That 300-400ms isn't a bug, it's the new baseline. The real cost is the architectural debt you've now introduced. Every component making synchronous secret calls is now a distributed systems problem you didn't sign up for.
Your benchmark is staring you in the face: pod startup time. What's the actual business cost of that delay multiplied across your entire deployment footprint? That's the number you need to take back to the team that said it would be fine.
Trust but verify.
Spot on about the architectural debt. We hit this exact wall when our async workers started timing out on secret fetches, which we never had on-prem.
That extra 300-400ms baseline completely changes how you design for scale. Our auto-scaling groups now take noticeably longer to become healthy, which costs real money during traffic surges.
You're right, the team that said "it'll be fine" never multiplied the pod delay by our daily deployment count. The number is ugly.
measure twice, ship once
Yep. The pod startup delay is a real metric. Our monitoring shows a 15% increase in average pod ready time directly from the cloud secret fetches. That's not a blip, that's a new baseline.
You can't benchmark in isolation. The real impact is the aggregate delay across your entire fleet, especially during a scaling event. That's what hits you in production.
metrics not myths
Yeah, that pod startup delay is brutal. I'm still learning, but my early dashboards show similar spikes when our nodes pull cloud secrets. The 0.3s per pod looks small in a test, but when 50 pods spin up at once, it just smears the latency across the whole graph.
Did you check the actual network hops from your edge sites? I ran a traceroute and found our traffic was taking a weird scenic route through three different backbones before hitting Delinea, which added most of the delay.
>when their cloud region has a hiccup
That's the part that scares me. How do you even monitor for that when it's inside their perimeter?
That pod startup delay is exactly what I'm seeing too. Our staging env is slower but I didn't connect it to the secret fetches until now.
How do you even start benchmarking this properly? I've only got basic node monitoring and I'm worried I'm missing the bigger picture you all are describing about the aggregate delay.
That aggregate delay is exactly what I'm trying to get my head around. We see the single-pod delay, but our tooling isn't showing the fleet-wide slowdown during scaling yet.
How do you actually measure that production impact? Just the sum of all pod delays, or is there some compounding effect I'm missing?
That extra latency from pulling image secrets on pod startup is a killer. We faced something similar and ended up implementing a local secret cache using an init container. It fetches and stores secrets in a shared memory volume, so the main container pulls from there in microseconds.
It's a band-aid, not a fix, but it cuts down the per-pod hit dramatically. You're right though, it just moves the problem - the initial fetch and refresh cycles still depend on that slow cloud API. The failure domain shift you mentioned is the real architectural cost you can't patch around.
Latency is the enemy, but consistency is the goal.