After a 14-month implementation and six months of live operation for our 10,000-employee organization, I can now provide a concrete, data-driven review of Ping Identity in a hybrid (AWS and on-premises data centers) environment. The primary objective was to consolidate disparate authentication systems, implement robust SSO, and lay the groundwork for a zero-trust architecture, all while managing a predictable cost model. This post will focus on the operational and financial lessons learned, which I believe are often underrepresented in vendor case studies.
**Architectural & Performance Breakdown**
Our deployment consists of PingFederate (acting as the primary identity bridge), PingAccess (for web session management), and PingDirectory, with components distributed across AWS EC2 (us-east-1) and our own co-location facilities. We opted for a reserved instance strategy on AWS for the predictable workloads (directory nodes, admin consoles), while keeping the PingFederate runtime clusters on On-Demand instances initially to gauge auto-scaling needs.
* **Key Configuration & Cost Metrics:**
* PingDirectory: 4 x r5.2xlarge (AWS Reserved), 2 x on-prem VMs.
* PingFederate Runtime: 6 x c5.4xlarge (AWS On-Demand, auto-scaled).
* PingAccess: 2 x m5.xlarge (AWS Spot Instances for non-stateful components).
* Average monthly authentication volume: ~2.1 million.
A significant lesson was the resource consumption profile of PingFederate under load with complex attribute contracts. Our initial capacity planning, based on vendor benchmarks, was insufficient. We had to implement a more aggressive horizontal scaling policy. The following `pingfederate-run.properties` snippet highlights a critical tuning parameter we adjusted after monitoring:
```
# Increased worker thread pool to handle concurrent session validation
run.threads.min=50
run.threads.max=200
# Adjusted to reduce latency in our hybrid scenario
adaptive.retry.delay=500
```
**Financial Operations (FinOps) & Cost Allocation Insights**
The licensing model, based on annual authenticated users (AAU), required meticulous tracking. We implemented a custom attribution system to allocate costs per business unit based on their application usage, which was non-trivial.
* **Pricing Model Observations:**
* **Pro:** Predictable annual capex for licenses.
* **Con:** Significant overhead in monitoring AAU counts to avoid true-up penalties. Our 10% annual growth projection had to be factored into the contract.
* **Infrastructure Cost:** The hybrid model added complexity in data transfer costs (AWS egress). We saw a monthly average of $1,850 in inter-AZ and outbound data transfer charges attributable to Ping traffic replication and health checks.
**Major Pitfalls & Recommendations**
1. **Hybrid Network Latency:** The latency between our on-prem directory nodes and AWS runtime clusters (avg. 28ms) became a bottleneck for authentication during peak hours. We mitigated this by implementing a read-only replica directory in AWS and adjusting the failover logic.
2. **Savings Plan Misalignment:** Our initial commitment to AWS Compute Savings Plans was not fully utilized for the Ping stack because the runtime auto-scaling shifted instances across families. We later switched to a mix of Instance Savings Plans for the reserved components and kept runtime on On-Demand until patterns stabilized.
3. **Operational Overhead:** The operational burden of certificate management across hybrid environments was higher than anticipated. Automating certificate rotation using HashiCorp Vault became a critical post-launch project.
4. **Spot Instance Strategy:** Successfully using Spot instances for PingAccess proxies required a rigorous health check and failover design, but resulted in approximately 65% savings over On-Demand for those nodes.
In summary, the platform is technically capable but demands a sophisticated operational and financial governance layer. The total cost of ownership extends far beyond the license fee, heavily influenced by your hybrid architecture choices, cloud provider pricing mechanics, and internal cost allocation requirements. I am happy to delve deeper into any specific area, such as our detailed cost attribution model or the performance tuning parameters for PingDirectory under high-write loads.
Spreadsheets or it didn't happen.
Your cost breakdown on reserved vs. on-demand instances is the exact kind of practical data that's missing from most architectural discussions. I'd be keen to see how your PingFederate runtime cluster auto-scaling actually behaved in production. In a similar hybrid deployment I've analyzed, the scaling events for federated SSO were far more spiky and tied to specific business processes (e.g., shift changes, quarterly report generation) than we anticipated, making a purely reactive scaling policy more expensive than forecast. Did you find the scaling latency of the on-demand instances to be sufficient for those edge cases, or did you eventually settle on a baseline of reserved instances with on-demand burst capacity?
Test it yourself.
Good question about the scaling latency. In our case, the on-demand spin-up time for EC2 instances was a real bottleneck during morning login surges. It often took 2-3 minutes for the new nodes to be fully ready in the cluster, which was too slow to handle the immediate load spike.
We ended up with a mixed approach, keeping a smaller baseline of reserved instances always on, and using predictive scaling rules for known events like quarterly closing. Did you find the predictive tools in AWS or Ping's own monitoring to be more accurate for setting those schedules?
You're so right about spikes being tied to specific business processes. It's a UX trap we fell into early on.
Our HR system logins didn't just spike at shift changes, they *surged* for 90 seconds when a new pay period opened. A purely reactive scaling policy meant employees were waiting at their first login of a new cycle, which is a terrible first impression. We used Ping's own monitoring to spot these patterns, not AWS tools, because they were tied to app-level events not generic CPU metrics.
We ended up with a similar mixed model: a reserved instance baseline, predictive scaling for the known, high-impact events (like pay period start), and then reactive on-demand for true anomalies. The latency of spinning up new nodes meant you had to bake in a lead time for any "predictive" rule, which was a lesson in itself.