Written by: Nimesh Chakravarthi, Co-founder & CTO, Struct
Key Takeaways for 2026 Incident Pricing
- Resolve247 is a customer-support chatbot without AI incident management or on-call investigation capabilities, so it does not fit engineering teams that need automated triage and root cause analysis.
- incident.io, Rootly, and Zenduty use different pricing models and setup timelines, from one day to several weeks, with varying levels of AI triage support in their plans.
- Struct stands out with a roughly 10-minute deployment, an 80% triage time reduction, and SOC 2 Type II plus HIPAA compliance across all tiers, including a 30-day risk-free pilot.
- Setup speed directly affects total cost of ownership, because platforms that need weeks of configuration delay MTTR improvements, while Struct enables automated investigations on day one.
- Teams ready to replace manual log-hunting with automated runbook execution can start a 30-day risk-free pilot with Struct today.
The following sections break down pricing and capabilities for each major platform, starting with incident.io, then moving through Rootly, Zenduty, and other notable tools before detailing Struct’s approach.
incident.io Pricing 2026: Base Platform Plus On-Call
incident.io structures pricing around a base platform fee with an optional on-call add-on, so teams can start with incident management and add alerting later. The table below shows how these costs combine across tiers.
| Tier | Base Price | On-Call Add-On | All-In (Annual) |
|---|---|---|---|
| Basic | Free (≤5 users) | 1 schedule included | Free |
| Team | $15/user/mo | +$10/user/mo | $25/user/mo |
| Pro | $25/user/mo | +$20/user/mo | $45/user/mo |
| Enterprise | Custom | Custom | Custom |
A 20-user team on the Team + on-call plan costs approximately $500/month on annual billing. The Pro tier bundles AI-generated postmortems, unlimited workflows, and status pages with no separate AI SKU. Full enterprise setup averages 1–2 days with opinionated defaults, though complex workflow migrations can take longer. One incident.io customer (Favor) reported a 37% MTTR reduction after consolidating onto the platform. For teams that need automated investigation rather than only workflow orchestration, run your first automated investigation with Struct’s 30-day risk-free pilot.
Rootly Pricing 2026: Combined IR and On-Call Plans
Rootly now focuses on combined incident response and on-call plans, replacing earlier entry tiers with a more consolidated lineup. The table below highlights the current structure and where AI fits.
| Tier | Price | Users | Notes |
|---|---|---|---|
| IR Essentials | Removed in 2026 | N/A | Replaced with new plans (Advanced, Business, Enterprise) |
| On-Call Essentials | Removed in 2026 | N/A | Replaced with new plans |
| IR + On-Call | Starts at $20/user/mo | Unlimited | Combined capabilities |
| Enterprise | Custom | Unlimited | Dedicated support, SLAs |
Rootly offers IR and on-call capabilities starting at $20/user/month. Rootly removed its IR Essentials tier (previously $20/month) in 2026 and replaced it with new plans (Advanced, Business, Enterprise). Startup discounts apply for companies under 100 employees, under $50M raised, and less than five years old. Rootly’s AI SRE agent surfaces probable root causes with confidence scores inside Slack, but FireHydrant-class implementations (comparable complexity) typically require 2–3 weeks of setup. Teams that cannot absorb that ramp time can get to value in under 10 minutes with Struct.
Zenduty Pricing 2026: Budget-Friendly On-Call Focus
Zenduty targets cost-conscious teams that need strong on-call routing and escalation but limited AI. The table below outlines how its free and paid tiers compare.
| Tier | Price | Users | Notes |
|---|---|---|---|
| Free | $0 | ≤5 | Basic on-call scheduling |
| Pro | Competitive | Unlimited | Advanced escalation, conditional routing |
| Enterprise | Custom | Unlimited | SSO, audit logs, dedicated support |
Zenduty is a cost-competitive option for teams that prioritize advanced escalation logic and conditional routing. The trade-off is a narrower AI feature set, because Zenduty does not offer automated root cause investigation or dynamically generated dashboards. Teams hitting SLA windows under 60 minutes often find manual triage still consumes most of the resolution time. To remove that manual first pass, eliminate manual triage with automated investigations from Struct.
Other Notable AI Incident Tools and Add-Ons
PagerDuty charges $21/user/month (Professional) and $41/user/month (Business) on annual billing. AI capabilities are not bundled, because AIOps costs $699/month and PagerDuty Advance (GenAI) costs $415/month as flat monthly add-ons. Full PagerDuty implementations typically require 2–8 weeks due to configuration complexity and training requirements.
FireHydrant (acquired by Freshworks on January 1, 2026) prices its paid plans starting at $25/responder/month billed annually (with a free tier for small teams). AI summaries, triage, and retrospectives are bundled across tiers rather than gated at Enterprise. Setup typically requires 2–3 weeks.
Grafana OnCall is included in Grafana Cloud tiers and offered free for small teams, providing data-native alerting integrated with Grafana dashboards. Note that Grafana OnCall OSS was archived on March 24, 2026, so self-hosted deployments are no longer maintained.
Having examined the competitive landscape, the next section details Struct’s pricing structure and the value that differentiates it from the platforms above.
Struct Pricing & Value for Engineering Teams
| Tier | Issues/Mo | Users | Key Features |
|---|---|---|---|
| Startup | 30 | Up to 5 | Native + web investigations, code agent handoff |
| Growth (Popular) | 200 | Unlimited | All Startup features + Build agent |
| Enterprise | Custom | Unlimited | Dedicated support, sidecar/on-prem, volume discounts |
Struct customers working at large scale with many services report an 80% reduction in triage time, which turns a 30–45 minute manual investigation into a sub-5-minute review. Struct deploys in five to ten minutes, integrates with leading observability platforms, Slack, GitHub, Linear, and Claude Code, and is fully SOC 2 Type II and HIPAA compliant. Every tier includes a 30-day risk-free pilot with white-glove onboarding. Run your first automated investigation today.
Setup Time & TCO Comparison Across Platforms
Setup time directly affects when teams start seeing MTTR improvements and lower manual triage costs. The table below shows how deployment timelines and AI pricing differ across platforms, which shapes total cost of ownership.
| Platform | Setup Time | AI Triage Reduction | AI Add-On Cost |
|---|---|---|---|
| Struct | ~10 minutes | 80% | Included |
| incident.io | 1–2 days | Up to 80% (platform claim) | Included in Pro+ |
| PagerDuty | 2–8 weeks | Varies | $699–$1,114+/mo add-on |
| FireHydrant | 2–3 weeks | Not published | Bundled |
| Rootly | ~1–2 weeks | Not published | Included |
A single hour of IT downtime costs the average mid-size or large enterprise more than $300,000. Every week of delayed setup keeps manual triage in place. IrisAgent reports that organizations using AI for incident management commonly see 40–70% MTTR reduction within 6–18 months when paired with process changes and data improvements. A platform that takes six weeks to configure delays those gains by six weeks, while Struct’s fast setup means the first automated investigation runs the same day.
The next section uses these setup and TCO differences to frame which Struct tier fits common team profiles.
How to Choose the Right Struct Tier
SLA windows under 60 minutes: Manual triage averaging 30–45 minutes consumes the entire SLA budget before a fix even starts. Struct’s automated investigation compresses that window to a few minutes, so teams under strict SLA obligations need a tier that can handle their full alert volume without manual first passes. That requirement makes the Growth tier (200 issues/month, unlimited users, Build agent) the practical minimum, because it delivers root cause context before an engineer opens a laptop.
Alert volume under 30 issues/month: The Startup tier (30 issues/month, up to 5 users) covers early-stage teams and provides the same core automated investigation capability. It offers a low-friction entry point for seed-stage companies that want to establish on-call discipline before headcount scales.
Compliance requirements (SOC 2 / HIPAA): All Struct tiers carry the compliance certifications mentioned earlier. Teams in fintech or healthcare that cannot route logs through non-compliant vendors can deploy Struct without a security review exception. The Enterprise tier adds sidecar and on-prem support for organizations with strict VPC egress policies.
Onboarding speed for new engineers: Engineering teams lose time per incident to coordination overhead from tool sprawl before troubleshooting begins. When runbooks are encoded directly into the investigation flow, that coordination step largely disappears, because junior engineers receive a reliable, contextualized starting point for every alert without asking senior staff where to look first.
High alert volume with custom integrations: The Enterprise tier provides custom issue volume, dedicated support, and volume discounts for teams running hundreds of services with bespoke observability stacks.
Conclusion: Pricing, Speed, and Fit
The 2026 AI incident management market spans per-user seat fees, usage-based tiers, and flat-rate AI add-ons, yet the criteria that matter most to engineering teams are investigation speed, setup time, and total cost of ownership. Resolve247 does not belong in this evaluation, because it is a customer-support chatbot without on-call investigation capabilities.
Among purpose-built platforms, incident.io and FireHydrant offer strong workflow orchestration but require days to weeks of configuration. PagerDuty’s AI capabilities carry significant add-on costs. Zenduty is cost-competitive but limited in automated root cause analysis. Struct’s differentiation is the combination of a fast setup, the triage-time reduction validated at production scale, SOC 2 Type II and HIPAA compliance, and a 30-day risk-free pilot that removes procurement risk.
For engineering managers at Seed-to-Series C companies who need to reduce 3 AM manual log-hunting, protect SLA windows, and unblock product velocity without a multi-week implementation project, a direct integration provides the fastest practical path. Run your first automated investigation in under 10 minutes.
Frequently Asked Questions
What is the difference between Resolve247 and an AI incident management platform like Struct?
Resolve247 is a customer-support chatbot designed to handle end-user queries and deflect support tickets. It does not integrate with observability platforms like Datadog, AWS CloudWatch, or Sentry, and it does not perform automated root cause analysis on engineering alerts. Purpose-built AI incident management platforms like Struct connect directly to alerting channels, log sources, and code repositories. When an alert fires, Struct automatically investigates, correlates logs, maps a timeline, and identifies the root cause before an engineer is paged. The two product categories serve entirely different use cases and should not be evaluated against each other.
How does Struct’s setup compare to competitors in practice?
Struct setup involves three authentication steps: connecting an issue source (Slack or Linear), a code repository (GitHub), and an observability context (Datadog, AWS CloudWatch, GCP Logs, or similar). Once connected, auto-investigations activate immediately. There is no custom workflow configuration, no training sessions, and no professional services engagement required for the initial deployment. By contrast, PagerDuty implementations typically require 2–8 weeks due to escalation policy configuration, integration mapping, and team training. FireHydrant and Rootly implementations run 2–3 weeks, while incident.io’s opinionated defaults compress that to 1–2 days. Struct’s rapid setup means the first automated investigation runs the same day the account is created.
Is Struct appropriate for teams with strict data security requirements?
Struct is SOC 2 Type II and HIPAA compliant, which covers the compliance requirements of many Seed-to-Series C companies in fintech, healthcare, and SaaS. Logs and telemetry data are accessed and processed ephemerally, and they are not stored persistently by Struct. The Enterprise tier adds sidecar and on-prem deployment support for organizations with strict VPC egress policies that prohibit log data from leaving internal infrastructure. Teams that require zero-egress, fully air-gapped deployments should evaluate the Enterprise tier and discuss architecture requirements directly before committing.
How does Struct handle alert fatigue and noise reduction?
Struct investigates every configured alert automatically, regardless of volume. For each alert, it determines whether the issue is a transient false positive, a minor blip, or a customer-impacting outage, and surfaces that assessment directly in Slack before a human intervenes. This removes the binary choice engineers currently face, where they either acknowledge and investigate manually or ignore and risk missing a real incident. Struct also performs intelligent deduplication across noisy channels and proactively monitors for high-severity signals that may be buried in alert volume. Engineers then engage only when human judgment or action is genuinely required, rather than spending time triaging every alert from scratch.
Can Struct follow our existing on-call runbooks?
Yes. Struct accepts custom instructions, correlation ID formats, and full on-call runbook text as direct configuration inputs. When an alert fires, Struct follows those operational procedures during its automated investigation, querying the specific log fields, checking the specific services, and applying the diagnostic steps your senior engineers would apply manually. Composable widgets allow teams to guarantee that specific visual data, such as particular dashboards, metric charts, or relevant trace IDs, is always surfaced for defined alert types. The automated investigation output then reflects your system’s architecture and your team’s institutional knowledge, rather than a generic AI response.
