Resolve AI is live · custom pricing · last verified 26 Oct 2026 · viability healthy
Resolve AI
Resolve AI is an AI SRE platform whose agents go on-call, triage alerts and investigate incidents for you.
The most complete agentic AI SRE build we've reviewed, and the customer roster (DoorDash, Coinbase, Zscaler) is the kind of proof that matters here. Pick it when on-call fatigue and multi-hour investigations already cost real money at scale. Expect to budget time for a credit forecast before you sign.
Verdict generatedVerified 4d ago · liveness 81/100 · cite: rightaichoice.com/tools/resolve-ai
- Engineering teams with high alert volume spread across many microservices
- Ops orgs targeting large MTTR cuts and fewer engineers stuck in war rooms
- Mid-to-large companies already running Datadog, PagerDuty and Kubernetes
- Teams that want runbook, wiki and chat knowledge captured into agent workflows
- Seed-stage startups with no structured on-call rotation or incident history
- Teams with no observability or incident tooling to connect
- Orgs that only want simple alert routing without agentic investigation
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Skip Resolve AI if you run a small service footprint with no formal on-call rotation and no observability stack for agents to reason over, or if you only want alert routing.
Credits are consumed by background work as well as by investigations, so always-on agents monitoring deploys and running scheduled tasks draw down your balance even on quiet days.
Resolve AI is priced for mid-to-large ops organizations with real alert volume; small teams should compare against simpler alerting-plus-assistant tools that charge per seat, while platform vendors selling seat-based AI SRE typically still require a sales conversation at this scale. Credits track agent work rather than tokens, which helps predictability but still needs a workload forecast before you commit.
In short
Resolve AI — Resolve AI is an AI SRE platform whose agents go on-call, triage alerts and investigate incidents for you. Best for Engineering teams with high alert volume spread across many microservices, Ops orgs targeting large MTTR cuts and fewer engineers stuck in war rooms, Mid-to-large companies already running Datadog, PagerDuty and Kubernetes. Pricing is on request (custom quotes).
No head-to-head comparison for Resolve AI yet — the alternatives below are the closest matches we list.
What is Resolve AI?
Resolve AI — Resolve AI is an AI SRE platform whose agents go on-call, triage alerts and investigate incidents for you. Best for Engineering teams with high alert volume spread across many microservices, Ops orgs targeting large MTTR cuts and fewer engineers stuck in war rooms, Mid-to-large companies already running Datadog, PagerDuty and Kubernetes. Pricing is on request (custom quotes).
Resolve AI is an AI SRE platform that puts autonomous agents on-call alongside your engineers. Connect it to the observability, alerting and infrastructure tooling you already run, and agents take the page, triage the alert, sweep telemetry, code and infra for evidence, then hand your engineer a root-cause hypothesis rather than a raw page. The vendor's numbers: up to 80% downtime reduction, 5x faster MTTR and 75% higher on-call productivity. Customer stories cite 72% faster investigation at Zscaler and 30% fewer engineers in war rooms at DoorDash. The product splits into four lanes: Primary on-call (triage and mitigate, escalating with full context), Incident resolutions (causal timelines that identify root cause and restore service in minutes), Issue remediation (production context handed to coding agents so recurring problems get fixed at source) and Production automation (background SRE agents that monitor deploys, detect drift and run operational tasks, or your own custom builds). Underneath sits Hivemind, which tracks current production state, alerts and recent changes so investigations don't restart from zero. The stack pairs Resolve AI's own domain models with frontier models, tuned for cost per task. Docs expose an API, MCP server and Skills, and security covers SSO, SCIM, RBAC, redaction, encryption and audit logs. Where it sits: Resolve AI layers over your PagerDuty and Datadog rather than replacing them, and the four-lane architecture with a state layer is a deeper build than the chat-over-alerting AIOps tools it competes with.
Key features
- Primary on-call agents triage every alert and escalate to the right owner with full context
- Autonomous incident investigation with root-cause identification and mitigation
- Causal timelines linking code changes, infrastructure events and telemetry
- Agent teams investigate in parallel across specialized hypotheses
- Background agents monitor deploys, detect drift and run recurring operational tasks
- Production context piped into coding agents so recurring issues get fixed at source
- Hivemind tracks production state, alerts and recent changes in real time
- Incident channels where engineers and agents investigate together in Slack and Teams
- Build custom SRE agents via MCP, Skills and the Resolve REST API
- Captures tribal knowledge from chats, runbooks, wikis and past incidents
- Credit-based consumption covering chats, triage, investigations and background tasks
- Anthropic MCP server for connecting agents to production tooling
- SAML single sign-on, SCIM directory sync and RBAC admin controls
- Sensitive data redaction, encryption, retention controls and audit logs
- No external model training — customer data stays scoped to your org
Behind the verdict
Most AI-for-ops pitches are a chat box bolted onto PagerDuty. Resolve AI is not that, and the difference shows up in where the agents sit: on the page itself, in the incident channel, and in the background watching deploys for drift. We'd reach for this when your on-call rotation is drowning and investigations regularly run into hours across many microservices. The four lanes map to real SRE work rather than one happy path, and Hivemind keeping production state warm is the part that stops every investigation restarting from a blank slate. Pass if you have no structured on-call rotation or incident history to learn from. There's nothing for the agents to reason over, and you'll pay for capability you can't use. Teams that only want cleaner alert routing should buy routing, not agents. The closest alternative is building agents in-house on top of your own observability data. Resolve AI's own argument, published on their blog in July, is that DIY agents fail at investigating alerts and incidents. Treat that as vendor framing, but the underlying point holds: causal timelines across code, infra and telemetry are tedious to build and easy to get subtly wrong. Where it bites: pricing runs on credits consumed as agents do defined work, so your bill tracks investigation volume. Forecast it against real incident load before signing, not against a quiet quarter. Also budget for adoption. Agents that escalate with full context only earn engineer trust if the context is right, and tribal knowledge capture from runbooks, wikis and past incidents takes a few weeks to pay off.
Limitations
- Resolve AI is not a simple alerting tool.
- It assumes a mature ops stack — observability, incident management and a real on-call rotation — to be effective; without that history the agents have little to learn from.
- The platform requires cloud connectivity to your production systems, so offline and air-gapped environments are out.
- Pricing is credit-based and scoped by quote: credits map to defined agent work such as chats, alert triage, investigations, incidents and background tasks, so you must forecast agent workload before you commit.
- Specific context-window and rate limits are not published.
- Agent output is a root-cause hypothesis and proposed fix that your engineers still validate.
as of 2026-10-16
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Is Resolve AI still active?
Resolve AI is live · custom pricing · last verified 26 Oct 2026 · viability healthy.
Viability score
How well maintained and how widely used is Resolve AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What's new in Resolve AI
Checked 6 days agoAcross the latest 1 update: 1 community discussion.
What do users say about Resolve AI?
What people actually say about Resolve AI — is it worth it?
We scanned public community sources for Resolve AI on Aug 18, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about Resolve AI— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 26 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Resolve AI 23 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed who it suits, summary, features, our verdict, description
- — changed link health
- — changed pricing page
- — changed SSL certificate, our verdict, summary, who should skip it, features, description, who it suits
- — changed link health
- — changed hidden costs, switching out, limitations, use cases, switching in, summary, description, who it suits, who should skip it, setup time, pricing fit, our verdict, features
Showing the 6 most recent of 23 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Resolve AI cost in 2026?
Resolve AI: Pricing is on request (custom quotes). Pricing last verified 26 Oct 2026.
Where the pricing makes sense
The company stage and team size where Resolve AI's pricing actually pencils out — and where peers do it cheaper.
Resolve AI is priced for mid-to-large ops organizations with real alert volume; small teams should compare against simpler alerting-plus-assistant tools that charge per seat, while platform vendors selling seat-based AI SRE typically still require a sales conversation at this scale. Credits track agent work rather than tokens, which helps predictability but still needs a workload forecast before you commit.
Has Resolve AI's pricing changed?
No plan price for Resolve AI has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Resolve AI?
Pick Resolve AI if you are Engineering teams with high alert volume spread across many microservices, Ops orgs targeting large MTTR cuts and fewer engineers stuck in war rooms, Mid-to-large companies already running Datadog, PagerDuty and Kubernetes, Teams that want runbook, wiki and chat knowledge captured into agent workflows.
Look elsewhere if you are Seed-stage startups with no structured on-call rotation or incident history, Teams with no observability or incident tooling to connect, Orgs that only want simple alert routing without agentic investigation, Lean platform teams unwilling to forecast credit consumption for agent work.
Skip it if Skip Resolve AI if you run a small service footprint with no formal on-call rotation and no observability stack for agents to reason over, or if you only want alert routing.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Resolve AI actually fits — and what changes day-one when you adopt it.
An alert fires at 02:00 from Prometheus via PagerDuty. Resolve AI's Primary on-call agent takes the page, pulls evidence from Datadog and Kubernetes, and posts a root-cause hypothesis into the Slack incident channel.
Outcome: The engineer opens Slack to a hypothesis and evidence attached instead of a raw alert, cutting the first 30 minutes of manual digging.
A production outage triggers an incident channel in Microsoft Teams. Resolve AI builds a causal timeline linking a recent GitHub deploy, infrastructure events and telemetry, then proposes a mitigation.
Outcome: The team spends less time in the war room and restores service faster, with the timeline captured for the postmortem.
A flaky issue keeps reappearing. Issue remediation pushes the production context into the developer's coding agent, which drafts the fix as a pull request in GitHub.
Outcome: The recurring problem gets fixed at source rather than mitigated again next week.
Use cases
- Delegate on-call alert triage to agents so engineers stop being paged for noise.
- Co-investigate production incidents with agent-driven root cause analysis and causal timelines.
- Automate health checks, deploy monitoring and drift detection with background agents.
- Encode runbooks and tribal knowledge as repeatable incident response workflows.
- Cut war-room headcount by letting agents work incident channels alongside PagerDuty and Slack.
- Push production context into your developers' coding agents so fixes land as PRs.
- Run scheduled production tasks like reporting and health checks without an engineer.
- Triage and remediate vulnerabilities by severity against an SLA.
Setup time & first value
How long it actually takes to get something useful out of Resolve AI — broken out by persona, not the marketing-page minute.
Connect your observability, incident and code tools, then let the agents learn from runbooks, wikis and past incidents — for a team already on Datadog, PagerDuty and Kubernetes, first value usually arrives within the first alerts after connection. Plan for an access review and a credit forecast alongside the technical setup. Teams without existing observability or a defined on-call rotation will
Switching to or from Resolve AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a manual on-call rotation: connect your existing observability and PagerDuty setup, let agents learn from past incidents and runbooks, and start in shadow mode on triage.
- →From a chatbot-on-alerting tool: keep your alert routing and layer Resolve AI's investigation modes and causal timelines on top.
- →From an in-house DIY investigation agent: Resolve AI's blog post on six reasons DIY agents fail at alert investigation is the natural starting point for the build-versus-buy case.
- →From a plain PagerDuty-only workflow: connect Slack or Microsoft Teams, enable Primary on-call triage, and keep PagerDuty as the routing layer.
- ↗To plain alerting: keep PagerDuty or your existing paging tool running inline as the routing layer while you evaluate.
- ↗To seat-based AI SRE tooling: export incident timelines, team knowledge and runbooks captured by Resolve AI before you cut over.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Resolve AI”, and we withheld 6: 6 could not be judged, because “Resolve AI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Resolve AI.
Official links
What are the best Resolve AI alternatives?
The closest alternatives to Resolve AI we list are Adps AI, superlog and Sazabi. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Adps AI | Freemium | Pricing not published | — |
| superlog | Freemium | Pricing not published | — |
| Sazabi | Paid | Pricing not published | — |
Alternatives to Resolve AI
View all
Adps AI
AI-native SRE platform whose specialised agents detect, diagnose, and remediate cloud, Kubernetes, and CI/CD incidents — including IT operations alerts by
Tools that pair well with Resolve AI
Common stack mates teams adopt alongside Resolve AI, with the specific reason each pairing earns its keep.
Adps AI
AI-native SRE platform whose specialised agents detect, diagnose, and remediate cloud, Kubernetes, and CI/CD incidents — including IT operations alerts by
superlog
Superlog runs AI automations that triage production alerts, answer support questions, and open fix pull requests in your repo.
Sazabi
Sazabi is AI-native observability: conversational debugging, autonomous alerts, and coding agents that open fix PRs.
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