Written by: Nimesh Chakravarthi, Co-founder & CTO, Struct
Key Takeaways
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Most observability platforms like SolarWinds, Datadog, and Dynatrace collect and visualize data but still rely on humans for root-cause analysis, which keeps engineers awake at 3 AM.
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Teams typically spend 30–45 minutes manually correlating logs, traces, and deploys across multiple tools after an alert fires because no platform automates that full investigative workflow.
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Consumption-based pricing across these vendors creates unpredictable costs, and many organizations run overlapping tools while struggling to consolidate without losing needed capabilities.
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OpenTelemetry’s 2026 graduation reduces vendor lock-in risk, yet platforms that depend on proprietary agents still create high switching costs for teams that want flexibility.
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Struct automates your on-call runbook so you receive a ranked root-cause summary in Slack before you open your laptop, closing the manual triage gap that traditional observability platforms leave behind.
Why today’s observability tools still demand manual root-cause work
Modern observability platforms such as SolarWinds, Datadog, Dynatrace, and LogicMonitor focus on collecting and visualizing telemetry. They stop short of closing the loop between a firing alert and a confirmed root cause.
As systems grow larger with cloud-native architectures, microservices, and continuous deployments, manual investigation slows down significantly, so teams bolt on automated correlation and AI-assisted suggestions as a separate layer above their existing tools. Applications in today’s increasingly complex environments have more dependencies than a single person can effectively analyze with traditional monitoring tools.
Without that automation layer, the result is a predictable workflow. An alert fires, an engineer acknowledges it in PagerDuty or Slack, then pivots across Datadog dashboards, AWS CloudWatch logs, Sentry exceptions, and GitHub history to reconstruct what happened. Manual root-cause identification requires operators to determine when a problem occurred via time-series metrics, where it occurred via traces, and why via log messages, often taking hours or days to sift through millions of log messages.
A mid-2025 LogicMonitor survey of 100 VP+ IT leaders found that 41% are satisfied with their platform’s ability to deliver insights. The remaining 59% sit in front of dashboards filled with telemetry but still lack clear answers on root cause. As organizations grow, evidence becomes scattered across tools and teams, and investigating each dataset in isolation hides the interactions that drive failures.
SolarWinds Observability, Datadog, and Dynatrace all surface anomalies and provide dashboards, but surfacing data is not the same as investigating it. None of them automatically execute the investigative steps a senior engineer would take, such as querying correlated log windows, mapping service dependency chains, and cross-referencing recent deploys, to deliver a ranked causal chain before the on-call engineer is fully awake.
2026 platform comparison: deployment, pricing, OpenTelemetry, and MTTR impact
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Platform |
Deployment Model |
Pricing Transparency |
OpenTelemetry Support |
|---|---|---|---|
|
SolarWinds Observability |
Self-hosted or SaaS |
Partial, proprietary agents remain primary collection method |
|
|
Datadog |
SaaS |
Consumption-based, costs scale with hosts, custom metrics, and log ingestion volume, so predictability requires active governance |
|
|
Dynatrace |
SaaS / managed |
DPS (Davis Platform Subscription) consumption model, full-stack pricing varies by entity type |
|
|
LogicMonitor |
SaaS |
Per-device subscription, 67% of organizations are likely to switch platforms within 1–2 years, driven by pricing and AI capability gaps |
Native OTel collector support, vendor distribution available |
MTTR impact is not directly comparable across these platforms on a shared unit because each vendor measures it differently and under different stack configurations. What remains consistent is that organizations using AI for incident management often report MTTR reductions in production environments. That reduction usually comes from an investigation layer that sits on top of the observability platform rather than from the observability platform itself.
See how Struct closes the investigation gap in under 5 minutes with automated root-cause analysis.
Alert workflows in 2026: manual investigation vs automated Struct flow
Manual workflow (any platform, 2026): It is 3:07 AM. PagerDuty fires. The engineer acknowledges, opens Datadog, filters the relevant service dashboard, notices a latency spike, pivots to CloudWatch logs, searches for the correlation ID, finds 40,000 log lines, opens Sentry to check for exceptions, cross-references a recent GitHub deploy, forms a hypothesis, tests it, and then begins remediation if the hypothesis holds. Elapsed time: 30–45 minutes of active investigation before a single fix is attempted.
Automated workflow with Struct: PagerDuty fires. Struct has already intercepted the alert, queried the relevant log windows, correlated the trace IDs, mapped the blast radius, cross-referenced the most recent deploy, and generated a ranked root-cause summary with suggested fixes inside a dynamically generated Slack dashboard. Struct gets you from alert → root cause before you even open your laptop. The engineer reviews a five-minute investigation report and moves directly to remediation.
The difference does not come from the observability platform underneath. The difference comes from whether the investigative work happens automatically or manually. Large-scale customers using Struct report an 80% reduction in triage time, turning a 45-minute investigation into a 5-minute review.
Pricing surprises across consumption-based observability models
Pricing unpredictability has become a consistent operational risk across this category. As of August 1, 2025, SolarWinds eliminated perpetual licensing entirely and now requires mandatory 3-year subscription commitments for all new and renewal licenses. Legacy customers have reported significant renewal increases, including documented cases of a 225% price jump.
SolarWinds SaaS consumption pricing adds further complexity because new monitoring capabilities can increase license consumption if teams do not audit usage before upgrading.
Datadog and Dynatrace operate on consumption models where costs scale with custom metrics, log ingestion volume, and host count. For Seed-to-Series C teams with rapidly growing infrastructure, monthly bills can diverge sharply from initial estimates without active cost governance. 84% of organizations are pursuing or considering tool consolidation, with 46.7% currently running 2–3 observability tools in parallel, so most teams pay for overlapping capabilities across multiple vendors.
OpenTelemetry’s 2026 graduation and evolving vendor lock-in risk
The practical implication for platform selection is clear. Standardizing on OpenTelemetry allows IT teams to change analysis tools without re-instrumenting applications, which reduces risk and saves time while avoiding long-term vendor lock-in from proprietary agents. Platforms that require proprietary agents as the primary collection method create switching costs that compound over time.
89% of financial services teams report OTel compliance as important in selecting observability tools. AWS added native OpenTelemetry metrics support for Amazon CloudWatch and OpenTelemetry-based Container Insights for Amazon EKS in April 2026, and Microsoft has documented OTLP ingestion for Azure Monitor across AKS, virtual machines, and Azure Arc-enabled servers. Cloud providers now treat OTel as core infrastructure rather than an optional integration.
For engineering teams evaluating platforms today, lock-in risk is asymmetric. A platform with strong native OTel support lets you swap backends without re-instrumenting. A platform built on proprietary agents makes migration expensive regardless of how attractive the alternative appears.
2026 buyer checklist for hybrid and cloud-native observability stacks
|
Requirement |
Favor SolarWinds Self-Hosted |
Favor Cloud-Native SaaS (Datadog / Dynatrace) |
Add Automated Investigation Layer (Struct) |
|---|---|---|---|
|
On-premise or air-gapped infrastructure |
✓ |
— |
Evaluate on-prem support requirements |
|
Primarily cloud-native microservices |
— |
✓ |
✓ |
|
OpenTelemetry-first instrumentation |
Partial |
✓ (with caveats) |
✓ (OTel-compatible sources) |
|
Predictable monthly cost under $10K |
Possible at small node counts |
Requires active governance |
Fixed tiers from free to growth |
|
Automated root-cause before human triage |
— |
— |
✓ (core capability) |
|
Sub-10-minute setup |
— |
Hours to days |
✓ |
|
SOC 2 / HIPAA compliance required |
✓ |
✓ |
✓ |
Migration trade-offs and where SolarWinds still fits
SolarWinds Self-Hosted remains a defensible choice for specific scenarios. Teams running significant on-premise or hybrid infrastructure with legacy network devices, such as switches, routers, and on-prem servers monitored via SNMP or WMI, benefit from SolarWinds’ deep protocol support and node-based licensing model at low node counts. Organizations with strict data residency requirements that prohibit log egress to SaaS platforms also have limited alternatives to self-hosted deployment.
Migration away from SolarWinds carries real costs. Teams with years of custom dashboards, alert thresholds, and ITSM integrations face re-implementation work that rarely appears in vendor comparison spreadsheets. Additional Polling Engines for distributed SolarWinds deployments are only available with Enterprise Scale licensing, so teams already at that tier have sunk costs that weaken the economic case for switching.
For most Seed-to-Series C teams, a more productive framing focuses on the missing layer above the existing observability stack rather than on replacing SolarWinds or Datadog outright. The observability platforms collect and visualize data effectively. The gap is the automated investigative step between alert and answer, and that gap exists regardless of which platform sits underneath.
Add automated investigation to your existing stack — Struct integrates with SolarWinds, Datadog, Dynatrace, and every major observability platform you already use.
Frequently asked questions about Struct and automated investigation
Does adding an automated investigation layer mean replacing our existing observability platform?
No. Struct sits on top of your existing stack, such as Datadog, AWS CloudWatch, Sentry, Grafana, or any combination, and uses those platforms as data sources. It does not replace dashboards or alerting configurations. It adds the automated investigative step that runs the moment an alert fires, so engineers receive a root-cause summary rather than raw telemetry.
How does Struct handle poor log quality or missing trace IDs?
Struct’s output quality depends on the telemetry available. Teams already using structured logging, trace IDs, and tools like Sentry alongside cloud logs get the highest-fidelity investigations. If your system lacks basic logging or alerting triggers, automated investigation cannot compensate for missing data. The recommended starting point is any team already using Sentry, Datadog or cloud logs, and Slack-based alerting.
Is the data secure for a Series A or B company with compliance requirements?
Struct is SOC 2 and HIPAA compliant. Logs are accessed and processed ephemerally, and they are not stored persistently. For the vast majority of Seed-to-Series C companies, this compliance posture covers standard requirements. Teams with strict enterprise rules that require full on-premise log processing should evaluate the on-prem sidecar option available on the Enterprise tier.
How long does setup actually take?
Setup takes under 10 minutes for most teams. You authenticate your alert source, such as Slack or PagerDuty, your code repository like GitHub, and your observability context such as Datadog, CloudWatch, or an equivalent tool. Once connected, auto-investigations begin immediately on the next alert. No multi-week deployment or professional services engagement is required.
Can Struct follow our team’s specific on-call runbooks?
Yes. Teams can input custom instructions, correlation ID formats, and existing on-call runbooks directly into Struct. The platform uses composable widgets so specific visual data, such as particular service dashboards or custom metric charts, is always pulled for defined alert types. The investigation output mirrors what a senior engineer familiar with your system architecture would produce.
Neutral summary of trade-offs and when to explore automated investigation
SolarWinds Observability, Datadog, Dynatrace, and LogicMonitor are mature, capable platforms for collecting and visualizing telemetry. Each has clear strengths: SolarWinds for hybrid and on-premise infrastructure depth, Datadog for cloud-native breadth and ecosystem integrations, Dynatrace for enterprise-scale dependency mapping, and LogicMonitor for network-centric environments.
The shared limitation is structural. Current observability tools lack automated root cause analysis that uses machine learning to emulate human investigative steps, forcing reliance on specialized human analysis. Only 41% of IT leaders are satisfied with their tools’ ability to generate actionable intelligence, and that figure has not materially improved despite increased observability spending.
For Seed-to-Series C engineering teams, the operational cost of this gap is concrete. The investigation delays mentioned earlier compound across every alert, pulling senior engineers from product work, leaving junior engineers unable to handle on-call independently, and eroding SLA windows before remediation begins. Struct automatically root-causes engineering alerts by pulling and analyzing metrics, logs, traces, monitors, and code, delivering the investigative output that observability platforms collect data for but do not produce.
Teams evaluating this space should focus on whether a platform delivers a confirmed root cause and blast radius before a human begins triage or delivers data that still requires a human to produce those answers. That distinction separates an observability platform from an investigation platform, and in 2026, fast-growing engineering teams need both.
Start automating investigations in under 10 minutes — let AI handle your next on-call investigation before you even open your laptop.
