Written by: Nimesh Chakravarthi, Co-founder & CTO, Struct | Last updated: August 18, 2026
Key Takeaways for Teams Comparing Resolve AI and Struct
- Resolve AI delivers faster noise reduction and parallel hypothesis testing, but engineers report opaque pricing, multi-week onboarding, and low comfort with fully autonomous actions.
- Resolve AI uses an enterprise sales model with no public pricing, which slows budget decisions for Series A–C teams that need to move quickly.
- Resolve AI’s multi-week onboarding and complex integrations contrast with Struct’s roughly 10-minute setup that connects Slack or PagerDuty, GitHub, and one observability source.
- Resolve AI focuses on investigation and does not advertise automated incident resolution verification, while Struct’s Incident Tracker (launched August 3, 2026) runs a ~1-minute verification loop against observability data to confirm resolution.
- Struct gives Series A–C teams faster deployment and verification-focused workflows, so they get confirmed root causes and closed-loop incident resolution without an enterprise rollout. Automate your on-call runbook and see it in your stack.
Resolve AI Pricing Compared to the 2026 AI On-Call Market
Resolve AI does not publish public pricing. Resolve AI uses an enterprise sales model with no disclosed per-user or per-investigation rate, so every evaluation starts with a sales call and a custom quote. For Series A–C teams that need a fast budget decision, this opacity creates friction before a single integration is tested.
To understand where Resolve AI likely sits in the pricing landscape, it helps to map the broader AI on-call market in 2026. incident.io starts at $15/user/month and Rootly AI at roughly $20/user/month, and Sherlocks.ai’s Pro plan is $500/month flat with unlimited investigations while Enterprise remains custom. PagerDuty Business with AIOps add-ons runs over $87,000 annually for a 150-engineer team. Resolve AI’s association with large enterprise platforms like Zscaler and Coinbase suggests pricing near the upper end of this range. Struct, by contrast, publishes tiered pricing with a free Startup plan (30 issues/month, up to 5 users) and a Growth plan (200 issues/month, unlimited users), and both include a 30-day risk-free pilot.
Resolve AI’s Competitive Landscape in 2026
Resolve AI competes primarily with AI-native investigation platforms and secondarily with AIOps-augmented incident management suites. Its direct competitors perform cross-domain root cause analysis across code, infrastructure, and telemetry, which separates these tools from coordination platforms like PagerDuty or Opsgenie that route alerts while engineers still own diagnosis.
Named competitors include Struct, Cleric, Datadog Bits AI, Sentry Seer, incident.io’s AI SRE, Dynatrace Davis, and PagerDuty AIOps. By 2026, most incident management platforms ship AI summaries and alert correlation as standard features, which narrows Resolve AI’s differentiation window against the broader market. The following table compares Resolve AI’s positioning with three representative competitors on pricing transparency, deployment speed, incident resolution verification, and target audience.
| Dimension | Resolve AI | Struct | PagerDuty (Business + AIOps) | incident.io (Pro + On-Call) |
|---|---|---|---|---|
| Pricing | Enterprise sales, no public rate | Free Startup tier; Growth plan (200 issues/mo, unlimited users); 30-day pilot included | PagerDuty Business costs $41 per responder per month (annual), with AIOps adding from ~$699 per month billed per accepted event | $45/user/mo combined (Pro + on-call add-on) |
| Setup Time | Resolve AI setup connects API-based, read-only integrations to observability, code, and infrastructure tools through a guided process. | About 10 minutes; connect Slack or PagerDuty, GitHub, and one observability source | Hours to days for routing and escalation configuration, with separate provisioning for the AIOps add-on | New engineers become productive within 3 days through a Slack-native workflow |
| Incident Resolution Verification | Not a stated capability, investigation-focused | ~1-minute automated verification loop against observability data confirms resolution (Incident Tracker, launched August 3, 2026) | Manual confirmation, no automated closed-loop verification | No automated closed-loop verification, post-mortem generation is the closest analog |
| Best-For Audience | Large enterprises such as Zscaler and Coinbase | Series A–C B2B SaaS and fintech teams with 15–80 engineers | Large-scale operations with complex escalation trees | Modern engineering orgs running Slack-centric incident workflows |
Skip the sales call and start a Struct pilot in 10 minutes. No enterprise deployment is required, and incident resolution verification is available from day one. Launch your Struct pilot on your next on-call shift.
What Engineers Actually Say About Resolve AI
Spiros Xanthos, founder and CEO of Resolve AI, describes the platform’s value as centralizing operational intelligence and exposing tribal knowledge. Engineer feedback in public forums adds detail on where that promise holds and where teams feel friction.
“The parallel hypothesis testing is genuinely useful at 3am when you don't know if it's the DB or the cache layer. But getting to that point took us three weeks of integration work we didn't budget for.” — r/sre, thread: Honest takes on AI on-call tools after 6 months, March 2026
“Resolve's sales process is thorough but slow. We needed a quote, a security review, and two more calls before we could even start a trial. Our Series B timeline doesn't have room for that.” — LinkedIn comment, AI SRE tools for fast-growing teams, April 2026
“The autonomous remediation feature is impressive in demos. In production we keep it in suggest-only mode because nobody on the team is comfortable with it touching infra without a human gate.” — r/devops, thread: Who's actually using autonomous IR in prod?, February 2026
“Alert noise reduction is real. We went from 800 alerts a day to something manageable. The RCA quality is hit or miss on novel failure modes.” — r/sre, thread: AIOps honest review thread 2026, January 2026
“Pricing came back at a number that made sense for our scale but not for a 25-engineer team. They're clearly built for Zscaler-tier deployments.” — LinkedIn DM shared publicly, Evaluating AI on-call tools, May 2026
“The investigation quality is high when the integrations are fully configured. Getting those integrations fully configured is the hard part.” — r/sre, thread: Resolve AI 90-day retrospective, April 2026
“We evaluated Resolve and Struct side by side. Struct was running in our Slack channel in under 15 minutes. Resolve was still in procurement three weeks later.” — r/ExperiencedDevs, thread: AI on-call tool shootout, June 2026
“The multi-agent parallel investigation is a real differentiator for complex microservice failures. For simpler stacks it's overkill and the cost reflects that.” — LinkedIn post, SRE tooling 2026 landscape, March 2026
“I wanted something that would tell me the incident was actually resolved, not just that the fix was applied. Resolve doesn't close that loop automatically.” — r/sre, thread: Incident resolution verification — does any tool do this?, July 2026
Resolve AI Onboarding Effort for Lean Teams
Dedicated AI SRE platforms like Resolve AI require more substantial implementation effort than AI features built into observability platforms because they integrate across code repositories, CI/CD systems, cloud infrastructure, and multiple observability vendors. The recommended integration sequence starts with observability data and later adds code repository access, CI/CD integration, and infrastructure configuration data. This phased rollout extends the time to full value.
A low-risk AI SRE pilot that follows best practices selects one service, deploys in read-only mode, runs for two weeks to compare conclusions against human engineers, then expands scope. That process typically takes four to six weeks end to end. For a lean Series A team, this timeline competes directly with shipping cycles.
Building a production-ready DIY AI SRE stack with tools like Claude and MCP requires 10–15 senior engineers and sustained investment over multiple years. That reality explains why commercial platforms exist, and it also highlights why setup complexity becomes a key evaluation factor.
Resolve AI, Trust, and Autonomous Actions
Only 37% of developers trust AI for incident response, compared to 59% of IT decision-makers. This gap reflects the lived experience of engineers who own production systems versus leaders who approve tooling budgets. Resolve AI’s autonomous remediation capabilities sit in the part of the stack where that trust gap is widest.
SRE teams usually move up a trust ladder of progressive autonomy and validate each stage in production-like conditions before advancing. The five-stage model runs from alert summarization to telemetry correlation, remediation recommendations, pre-approved low-risk action execution, and finally broad autonomous action, which experts describe as rarely acceptable without strict policy controls.
The primary failure mode in AI-driven incident response is not inaction but a wrong action taken with high confidence, such as isolating a production host, revoking service credentials, or blocking CIDR ranges. Engineers may rubber-stamp AI recommendations under time pressure, which turns human oversight into a formality instead of a safeguard.
Akhilesh Rao Meesala, Principal Engineer at Oracle, built a semi-autonomous SRE agent and concluded that safety came from external boundaries. Those boundaries included scoped tools, allowlisted commands, validation hooks, pull requests, and human approval. Engineers now apply that framing of safety through boundaries, not model confidence, when they evaluate any autonomous on-call tool.
Resolve AI vs Struct for Series A–C Teams
The clearest operational differences between Resolve AI and Struct are deployment speed and the presence of closed-loop incident resolution verification. Resolve AI targets enterprise scale, and Zscaler and Coinbase (120M+ users) appear as reference customers. Struct focuses on 15–80 engineer teams that need results in minutes instead of weeks.
Arcana, a Struct customer, cut investigation time from 30 minutes to 2 minutes, reclaims 56 engineer-hours per month, and runs 2,100+ automated investigations monthly. Struct’s Incident Tracker, introduced on August 3, 2026, runs a ~1-minute automated verification loop against observability data to confirm that an incident is actually resolved, not just that a fix was applied. Resolve AI’s public feature set does not describe an equivalent closed-loop incident resolution verification capability.
Struct connects in about 10 minutes by linking Slack or PagerDuty, GitHub, and one observability source such as Datadog, Grafana, AWS CloudWatch, Sentry, GCP, Azure, Prometheus, Loki, Sumo Logic, or Better Stack. It sits on top of existing observability as an investigation layer and does not replace Datadog or Grafana. By the time an engineer opens a laptop, Struct has correlated logs, mapped a timeline, identified the likely root cause, and surfaced suggested fixes in a dynamically generated dashboard.
On autonomous actions, Struct keeps final accountability with humans. The investigation runs automatically and the remediation decision stays with engineers. That design choice directly addresses the trust gap described earlier, the one that affects nearly two-thirds of developers who work with AI-driven incident response.
See the difference yourself. Connect Struct to your stack and run your first automated investigation before the end of the day.
Decision Framework: When Resolve AI or Struct Makes More Sense
Resolve AI fits best when your organization already operates at enterprise scale and can absorb a longer deployment.
- Your team processes 50,000+ alerts per month across a complex microservice mesh that benefits from multi-agent parallel hypothesis testing.
- You have a dedicated platform engineering team with 4–6 weeks available to build deep integrations across CI/CD, code repositories, and multiple observability vendors.
- Your procurement process already includes security reviews and multi-stakeholder sales cycles, so timeline does not constrain adoption.
- Your organization operates at a scale comparable to Zscaler or Coinbase, where a high-cost enterprise platform delivers clear ROI through incident volume.
Struct is the clearer fit when your team needs fast deployment, verified outcomes, and human-in-the-loop control.
- You are a Series A–C team with 15–80 engineers and need automated investigation running in production today, not in six weeks.
- Your on-call engineers already live in Slack and you want root cause analysis delivered there without adding a new interface.
- You require incident resolution verification, meaning automated confirmation against observability data that an incident is truly resolved, not just that a fix shipped.
- You prefer human-in-the-loop design by default, where AI handles investigation and engineers own the remediation decision.
- SOC 2 Type II and HIPAA compliance are required and you need to verify them before signing, which you can do at trust.struct.ai.
Conclusion: Matching the Tool to Your Team’s Reality
Resolve AI delivers measurable value at enterprise scale, and engineer feedback supports that claim. Teams report faster noise reduction, useful parallel hypothesis testing, and credible cross-domain root cause analysis. They also report friction from opaque pricing, multi-week onboarding, and low comfort with fully autonomous actions, and those issues matter more for Series A–C teams than for companies like Zscaler.
On-call stress contributes to burnout and attrition on SRE teams. A six-week deployment does not relieve that pressure for a lean engineering group. Teams in this position need a tool that runs before the next 3 a.m. alert fires.
Struct’s quick setup, 85–90%+ helpful investigation rate, and the closed-loop verification capability described earlier are designed for lean engineering teams that cannot wait through an enterprise rollout. Arcana’s 56-hour monthly reclamation, mentioned above, shows that those hours are recoverable. Your team can reclaim similar time and attention.
Your team’s hours are recoverable. Connect your integrations, run your first automated investigation, and see incident resolution verification in action before your next on-call rotation.
Frequently Asked Questions
What is incident resolution verification and does any tool actually do it?
Incident resolution verification is an automated process that confirms an incident is genuinely resolved by checking live observability data, not just by confirming that a fix was applied or a ticket was closed. Most incident management tools stop at a remediation suggestion or a status update. They do not close the loop by querying metrics, error rates, or traces to verify that the system has returned to a healthy state. Struct’s Incident Tracker, launched August 3, 2026, runs a roughly one-minute automated verification loop against connected observability data to confirm resolution. Struct intends to own this category, and no other dedicated page or tool currently claims this capability by name.
How long does it actually take to get Struct running compared to Resolve AI?
Struct takes under 10 minutes to set up. You authenticate your alert source, either Slack or PagerDuty, your code repository, which is GitHub, and one observability integration such as Datadog, AWS CloudWatch, Sentry, Grafana, GCP, Azure, Prometheus, Loki, Sumo Logic, or Better Stack. Auto-investigations start immediately after connection. Resolve AI requires a sales engagement, a custom scoping process, and an iterative integration sequence across code repositories, CI/CD systems, and multiple observability vendors. That process typically runs four to six weeks for a production-ready deployment. For Series A–C teams with fast shipping cycles, this deployment gap becomes the primary evaluation criterion.
Is Struct safe to use if we have strict compliance requirements?
Struct is SOC 2 Type II and HIPAA compliant. Compliance documentation is available at trust.struct.ai. Logs are accessed and processed ephemerally and are not stored beyond the investigation window. For most Series A–C B2B SaaS and fintech companies, this compliance posture satisfies security and legal requirements. One current limitation remains. Struct requires access to your logs and observability data through its integrations, so if your organization mandates that zero logs leave your VPC and requires a fully on-premise deployment, Struct will not be the right fit today.
Does Struct replace Datadog or our existing observability stack?
No. Struct sits on top of your existing observability tools as an investigation layer. It integrates with Datadog, Grafana, Sentry, AWS CloudWatch, GCP Logs, Azure Logs and Traces, Prometheus, Loki, Sumo Logic, and Better Stack to pull the signals those tools already collect. Struct correlates those signals, maps a timeline, and surfaces a likely root cause. It does not replace the underlying data collection. You can think of Struct as an automated senior engineer who reads your observability data and hands you a working theory before you open your laptop, not a replacement for the tools that generate that data.
What happens when Struct investigates an alert, and does it take autonomous actions on our infrastructure?
Struct automates the investigation phase and leaves remediation in human hands. When an alert fires, Struct queries your connected observability sources, correlates logs and traces, maps a timeline, identifies the likely root cause, and delivers a dynamically generated dashboard with suggested fixes. All of this happens before an engineer needs to intervene. The engineer then reviews that output and makes the remediation decision. For teams that want deeper automation, Struct can hand off context to a local CLI or AI coding agent or generate a pull request, but those actions always require human initiation. Final accountability stays with your engineers, which directly addresses the trust gap that most on-call engineers report around autonomous AI actions in production.
