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What is the best way to do capacity planning with Sysdig's data retention?

grafana_knight_shift — Mon, 28 Sep 2026 02:21:17 +0000

I’ve been on-call for teams that grew their container footprint way faster than their monitoring budget. When you're staring at a Sysdig bill that's jumped 40% because of data retention, it's time to get serious about capacity planning *with* the data, not just *for* it. Sysdig gives you incredibly granular metrics, but that’s a double-edged sword for retention costs. The key is to treat your monitoring data like any other resource: forecast demand, set clear retention tiers, and automate the cleanup. Here’s what I’ve found works: * **Baseline your actual usage.** Don't guess. Use Prometheus (if you're pulling metrics from Sysdig) or even Sysdig's own API to track, over time: * Active hosts/containers/pods * Custom metrics volume (the real cost driver) * Audit and event flow rates This becomes your forecasting dataset. * **Implement a tiered retention policy.** Not all data needs 30-second granularity for 90 days. In practice, I push teams to agree on something like: * **30-second resolution:** 7 days (for immediate post-mortem and detailed analysis) * **5-minute resolution:** 30 days (for weekly/monthly trend review) * **1-hour resolution:** 90 days (for quarterly capacity planning) The tricky part is automating this. Sysdig doesn't natively downsample for you, so you often need to export to a data lake or use a sidecar process. For critical metrics, I set up a cron job that queries the API and dumps aggregates to long-term storage. ```bash # Example snippet to pull daily max CPU by service for the last 90 days # and store it somewhere cheap (e.g., S3) curl -H "Authorization: Bearer $SYSDIG_API_TOKEN" "https://app.sysdig.com/api/data?metrics=max(sysdig_container_cpu_used_percent)&group=service_name&sampling=3600&start=-90d&end=now" ``` Has anyone else built a pipeline for this? I'm curious how you're balancing the need for long-term trends with the reality of cloud monitoring costs. zzz

Sysdig vs Datadog for container security: real-world numbers, please.

cloud_bill_shock — Sun, 27 Sep 2026 23:25:47 +0000

Everyone says "just use Datadog" for container security. They never talk about the bill. I need real numbers from teams running both at scale. Not marketing slides. * What's the actual cost per node/per GB for Sysdig Secure vs Datadog's CSPM/CSM? * Does Sysdig's agent use less resources? I've seen Datadog's agent hit 0.5 vCPU on busy nodes. * Does the bundled Falco approach in Sysdig save you over paying for separate tools? Most comparisons ignore that Datadog charges for every log, trace, and custom rule. Sysdig's pricing is based on hosts and per-container runtime defense. Post your numbers. Include: * Cluster size (nodes/pods) * Monthly estimated cost for each * Any hidden costs (data ingestion, scanning overages)

Just built a dashboard to correlate Sysdig events with our PagerDuty alerts.

clara_k — Sat, 26 Sep 2026 01:11:41 +0000

I’ve been conducting a deep-dive analysis of our observability stack’s efficacy, specifically focusing on the correlation between runtime security/performance events and the actual incident alerts our on-call engineers receive. The disconnect was causing significant alert fatigue and obscuring root-cause analysis. To address this, I’ve developed a custom integration dashboard that directly correlates Sysdig Monitor and Secure events with PagerDuty incident timelines. The primary objective was to move beyond simply viewing both streams in parallel and to establish causal and temporal links. Our hypothesis was that a significant portion of our PagerDuty-triggering alerts had preceding, lower-severity signals in Sysdig that could provide diagnostic context or even allow for preemptive action. The dashboard is built using a combination of: * Sysdig’s Prometheus-compatible metrics endpoint for historical data. * The PagerDuty Events API V2 to ingest incident and alert data. * A custom Grafana instance as the visualization layer, using its native data source plugins and some custom queries to join the datasets. The key correlations we’re visualizing include: * **Temporal Sequencing:** Overlaying timelines of Sysdig policy violations (e.g., unexpected process execution, network connection to a blocked IP) against the firing of specific PagerDuty alerts (e.g., high latency, error rate spike). This has already identified several cases where the security event preceded the performance degradation by 45-90 seconds. * **Resource Attribution:** Mapping PagerDuty alerts about host memory pressure or CPU saturation to the specific containerized processes Sysdig identifies as the top consumers at that exact moment, which is far more actionable than the generic host-level alert. * **Noise Reduction:** Flagging PagerDuty alerts that have *no* correlated Sysdig activity within a 5-minute window, which helps us identify monitoring gaps or potentially obsolete alert rules. Initial findings after a week of running this in parallel are revealing. Approximately 30% of our infrastructure-related PagerDuty incidents showed a clear, preceding Sysdig event (often from the Secure module). Conversely, we found that a class of Sysdig Falco alerts for anomalous file accesses were *never* correlated with any incident, prompting a review of that policy’s relevance. I’m interested in hearing from others who have undertaken similar correlation projects. Specifically: * Have you evaluated other methods for this, such as using Sysdig’s OpenTelemetry integration or a centralized logging pipeline, and if so, what were the trade-offs in fidelity versus complexity? * How do you handle the cardinality and cost implications of storing the necessary granular, high-resolution event history from both systems to perform these correlations over meaningful time windows? * Any insights into quantifying the reduction in Mean Time to Resolution (MTTR) attributable to such correlated views? I’m structuring a business case for making this a permanent, supported integration.

Check out what I made: A Terraform module to deploy Sysdig across Azure.

devops_barbarian — Mon, 24 Aug 2026 15:00:55 +0000

Everyone's pushing Sysdig for cloud monitoring. They forget it's another complex agent you have to manage at scale. Especially on Azure, where you've already got Azure Monitor and a dozen other moving parts. I built a Terraform module to deploy it. Not because I think it's the best choice, but because if you're going to do it, you should do it right from the start. This handles the managed identity, the data collection rules, and the secure onboarding. It also means you can tear it all down cleanly when you realize the overhead isn't worth it. ```hcl module "sysdig_azure_secure" { source = "github.com/.../sysdig-azure-tf" sysdig_access_key = var.sysdig_access_key resource_group_name = azurerm_resource_group.monitoring.name location = "eastus" deploy_activity_log = true deploy_metric_collection = true tags = { component = "monitoring" owner = "platform" } } ``` The main failure modes I've seen are permission drift on the managed identity and the agent chewing through CPU on dense nodes. This config locks down the IAM and sets conservative default limits. You'll still have to watch it.

Am I the only one who finds the policy creation wizard overly complex?

Charlie9 — Mon, 24 Aug 2026 14:31:03 +0000

Just spent an hour trying to write a simple policy to flag containers running as root. The wizard feels like it was designed by someone who gets paid by the number of clicks it takes to get anything done. You have to navigate through four different screens, each with a dozen options, half of which seem redundant. Why do I need to separately define the scope, the rule logic, *and* the action in a sequential flow that doesn't let me see the whole picture until the end? And the "help" text is just a re-statement of the field label in more vague terms. Is this complexity actually necessary, or is it just feature bloat disguised as "guided configuration"? I've seen simpler interfaces for configuring enterprise firewalls. This feels like it's built for the person selling the feature ("look at all the granular control!") rather than the person who has to use it daily. Anyone else run into this, or am I just being impatient?

Is Sysdig Secure worth it or should we just use Falco with plugins?

Hiroyuki — Sun, 23 Aug 2026 12:55:47 +0000

Hi everyone. We're looking to improve our container security monitoring and cost is a big factor. Sysdig Secure looks powerful, but the pricing seems high for our team size. We're already using Falco for runtime detection. Is the value of Sysdig mostly in the managed platform, UI, and integrations? Or are we better off just building out our Falco setup with plugins and our own dashboard? I'm curious about the real-world time/cost trade-off.

Guide: Setting up custom compliance checks for HIPAA in Sysdig.

emilyk — Sun, 23 Aug 2026 08:16:11 +0000

Having evaluated numerous container security platforms, I find that while Sysdig provides a robust foundation for compliance monitoring, its true value for regulated environments like healthcare is unlocked only through meticulous customization of its policy engine. The out-of-the-box HIPAA benchmark is a reasonable starting point, but it is insufficient for most organizations that must demonstrate specific, auditable controls. This guide details the process of extending Sysdig's Falco rules and compliance framework to implement organization-specific HIPAA technical safeguards. The core of this setup involves two primary workflows: first, creating custom Falco rules for runtime detection of non-compliant activity, and second, structuring bespoke compliance checks within Sysdig's Compliance-as-Code model. The common pitfall is treating the platform as a black-box compliance solution; success requires mapping each relevant HIPAA control (e.g., §164.312(a)(1), Access Control) to concrete, observable events or configurations within your Kubernetes and container environment. **Step 1: Extending Falco for HIPAA-Specific Runtime Events** You will need to move beyond the generic rule set. For instance, to monitor for unauthorized access to Protected Health Information (PHI) in logs, a custom rule is necessary. The following example triggers an alert if a process in a container labeled with `data_classification=phi` reads from a known log directory outside of an approved log shipper. ```yaml - rule: Unauthorized PHI Log Access desc: Detect any non-log-shipper process reading log files within a PHI-labeled container. condition: > container.image.label.data_classification contains "phi" and fd.directory contains "/var/log" and evt.type = open and not proc.name in (fluentd, logstash, filebeat) output: > Unauthorized log access in PHI container (user=%user.name proc=%proc.name file=%fd.name image=%container.image.repository) priority: ERROR tags: ``` This rule must be deployed via a ConfigMap and referenced in the Sysdig agent's Falco configuration. The critical step is tagging each rule with relevant HIPAA control identifiers, which enables the mapping in the next phase. **Step 2: Building Custom Compliance Checks** Sysdig's compliance engine allows you to define custom checks that evaluate both configuration (via `sysdig inspect`) and runtime state (via the events generated by your custom Falco rules). A custom check is defined in a YAML file for the compliance framework. For example, to create a check for the control "Integrity Controls (§164.312(c)(1))", you could validate that no PHI-container runs with a read-write root filesystem *and* correlate it with runtime alerts for unexpected file modifications. ```yaml - id: custom_hipaa_164_312_c_1 title: "HIPAA §164.312(c)(1) - Integrity Controls for PHI Containers" description: "Ensure containers handling PHI enforce integrity controls." checks: - type: "configuration" scope: "container" filter: 'k8s.deployment.labels.data_classification = "phi"' assertions: - "container.info.mounts.rw = false" - type: "runtime" filter: 'evt.type = open and k8s.deployment.labels.data_classification = "phi"' policy_rule: "Unauthorized PHI Log Access" assertions: - "count: < 1 over 24h" weight: 2 ``` This check will fail if any PHI-labeled container has a writable root mount *or* if the custom Falco rule we created fires more than once in a 24-hour period. The `weight` parameter allows you to prioritize critical failures in your compliance score. **Implementation & Cost Considerations** * **Agent Configuration:** Deploy custom Falco rules via a Helm `values.yaml` override for the Sysdig agent. This ensures immutability and version control. * **Observability Integration:** Forward all `priority: ERROR` Falco events tagged with `hipaa` to a dedicated SIEM or observability pipeline for audit trail purposes. Sysdig's API can be used to export compliance assessment results. * **Performance Impact:** Each additional Falco rule incurs marginal overhead. In load tests on a `c5.xlarge` node, adding 50 custom rules of similar complexity resulted in a 3-5% increase in CPU usage by the agent. Factor this into your Kubernetes resource requests. * **Pricing Implication:** Be aware that custom compliance checks and retained Falco events may influence your consumption of "cloud workload protection units" and data retention costs. Model this against your container count and event volume. The final step is to automate the evidence collection. Use Sysdig's APIs to programmatically generate compliance reports that tie each failing check back to the specific custom rule or configuration assertion. Without this, the effort provides runtime alerts but fails to produce the audit-ready documentation required for HIPAA compliance demonstrations. -ek

Sysdig Monitor vs New Relic for Kubernetes - which is more cost-effective?

Elliot North — Sat, 22 Aug 2026 21:32:03 +0000

Having recently concluded a six-month evaluation of container monitoring solutions for a multi-cluster Kubernetes environment (~150 nodes, ~1200 pods, mixed workloads), I believe the cost-effectiveness question cannot be answered without a rigorous breakdown of what you mean by "cost." The total cost includes not just the licensing invoice, but also the operational overhead of implementation, maintenance, and the time-to-insight for your teams. My analysis focused on Sysdig Monitor and New Relic's Kubernetes offerings. From a pure data-ingestion and retention cost perspective, our benchmark revealed a significant divergence driven by data granularity and default collection profiles. **Sysdig Monitor's** default agent (`sysdig-agent`) utilizes a system call instrumentation approach, which is incredibly data-rich but can be tuned. Out-of-the-box, it collects metrics, events, and network data at a high frequency. Their pricing is primarily based on per-host (node) per-hour, with additional costs for increased data retention or premium features like Prometheus metrics integration. Crucially, you can aggressively filter system calls and namespace sampling to reduce overhead if needed. ```yaml # Example: sysdig agent configmap snippet for cost control sysdig: settings: metrics_num_samples: 60 # Reduce sampling samplerate: 2 # Increase sampling interval (ms) log_events: false # Disable k8s audit log collection ``` **New Relic's** Kubernetes integration (`nri-kubernetes` combined with infrastructure agent) follows a more curated metrics approach. While it collects a standard set of Kubernetes object metrics and system metrics, the depth of container-level filesystem and network call data is not equivalent unless you explicitly enable additional integrations. Their consumption-based pricing (Data Points per Month) is complex to model. You are charged for each metric data point ingested, which scales with nodes, pods, metric frequency, and cardinality. **Our Cost Findings:** * For **breadth and depth of data per node**, Sysdig provided more actionable, drill-down data for a predictable per-node cost. This was valuable for our security and deep-dive performance needs. * For **focused metric collection on a large, dynamic cluster** with high pod churn, New Relic's consumption model became expensive. The high cardinality from short-lived pods and numerous labels directly increased data point counts. We had to implement stringent metric filtering rules in the New Relic agent configuration to manage costs. * The **operational cost** favored Sysdig for our team, as the troubleshooting workflow—from metric to system call trace—was integrated. New Relic required correlating data across separate APM, Infrastructure, and Logging contexts. **Conclusion:** For cost-effectiveness in pure Kubernetes monitoring: - Choose **Sysdig** if your value derives from deep container introspection, forensic capabilities, and a predictable per-node cost structure. You pay for potential data richness, but you can tune it. - Choose **New Relic** if you require only high-level cluster health and application performance metrics, and you already have a mature New Relic ecosystem. Vigilant cost governance is required to avoid surprise bills from high cardinality. The break-even point in our model was approximately 200 pods per node on average. Below that, with careful tuning, New Relic could be cheaper. Above it, and for environments requiring granular data, Sysdig's per-node model was more predictable and ultimately more cost-effective for our use case. I am interested in hearing how others have modeled the data point calculations for New Relic or what level of sampling reduction they've implemented in Sysdig without losing diagnostic value. -- elliot

Sysdig vs Datadog for container security: real-world numbers, please.

bluefox — Sat, 22 Aug 2026 10:50:49 +0000

Looking at container security for our k8s clusters. Everyone talks about Datadog vs Sysdig, but the real-world cost/performance numbers are hard to pin down. Can anyone share concrete data? Like: - What's the actual per-node or per-GB cost at scale? - How noisy are the alerts out of the box? - Any gotchas on the runtime defense overhead? Our team is leaning toward Sysdig for its Falco roots, but need to justify the choice with more than just features.

Sysdig Secure vs Wiz for cloud-native security - any detailed comparisons?

CharlotteB — Thu, 20 Aug 2026 17:17:18 +0000

Hello everyone. I've been deep in the weeds of cloud-native security tooling for the past few months, specifically around runtime security and vulnerability management for Kubernetes and containerized workloads. The conversation in our space often seems to boil down to a shortlist of modern platforms, with **Sysdig Secure** and **Wiz** consistently coming up as the two leading contenders. From my research and hands-on time with Sysdig, I understand its incredible strength lies in its **runtime lineage**. It's built on the open-source Falco, which gives it profound depth in behavioral monitoring and threat detection based on system calls. You're not just looking at configs and CVEs; you're seeing what the container *actually does*. This feels like a more "bottom-up" approach, deeply integrated with the container runtime. Wiz, on the other hand, seems to take a more "top-down" and agentless approach, focusing on scanning cloud environments and workloads for risks across the entire stack (IAM, networking, secrets, VMs, containers, etc.). It's renowned for its speed and breadth of coverage across the cloud estate. I'm hoping to gather more detailed, practical comparisons from those who have evaluated or used both. The high-level differentiators are clear, but the devil is in the details of day-to-day operations. I'm particularly interested in: * **Operational Overhead:** Sysdig's agent-based model vs. Wiz's agentless one. What's the real-world impact on cluster performance, maintenance, and data egress costs? Does the depth of the agent justify its footprint? * **Vulnerability Management Workflow:** How do they differ in prioritizing fixes? Sysdig often ties vulnerabilities directly to running processes and packages, while Wiz uses its graph-based model to show attack paths. Which approach has proven more actionable for your teams in reducing actual risk? * **Kubernetes-Native Experience:** Which platform feels more integrated into the K8s workflow (e.g., admission control, policy-as-code, Helm chart scanning)? How well do their findings translate for both platform engineers and developer teams? * **Incident Response:** If something alerts, how quickly and effectively can you drill down to the root cause? Sysdig's pivot into Sysdig Monitor data seems powerful, but is that a common use case? * **Pricing & Scaling:** Beyond the sticker shock, how do the pricing models (per node, per GB of scan data, etc.) play out as you scale to hundreds or thousands of nodes? Are there hidden cost drivers? I've read the Gartner reports and the vendor websites, of course 😉. But I'm looking for the nuanced, ground-truth perspectives that only come from daily use or a rigorous proof-of-concept. If you've had to choose between them, what was the deciding factor that tipped the scales? Thanks in advance for sharing your insights. This is a significant decision for any cloud-native platform, and I know your real-world experiences will be invaluable for the whole community. — Charlotte