Resolve AI Alternatives 2026: Real Prices, 3 Tested in a Lab

The main alternatives to Resolve AI are other hosted AI SRE agents that publish their pricing (Cleric from $700 a month, Datadog Bits Investigation at about 6.5 AI credits per investigation; NeuBird lists 1 credit per investigation but no price per credit) and open source agents you run yourself (Aurora and HolmesGPT, with K8sGPT as a cluster scanner). Resolve AI sells through sales: its pricing page is a request form and its credits per action are set in each contract. The open source agents are the only ones here we could test ourselves: on 19 injected Kubernetes faults, Aurora was graded correct in 34 of 38 runs and HolmesGPT in 31 of 38, and HolmesGPT was about 3x faster.

Arvo builds Aurora and wrote the lab scenarios. We cannot install Resolve AI or the other hosted products, so nothing below is a test of them; every claim about them links the vendor's own page, read on 2026-10-21.

What is Resolve AI?

Resolve AI is a hosted AI SRE platform. Its product overview describes agents that triage every alert, mitigate critical incidents, remediate recurring failures and run scheduled production tasks. The company was founded in October 2024 by Spiros Xanthos and Mayank Agarwal, who led Splunk Observability and co-created OpenTelemetry, and it states a $1.5B valuation and more than $190M in funding.

What the vendor's own pages say it does:

• Investigation. It "plans investigations that pursue multiple hypotheses in parallel" and builds causal timelines linking code changes, infrastructure events and telemetry (docs). Alerts run in a triage, investigation or adaptive mode, and adaptive mode only escalates to a full investigation when needed (billing docs).
• Actions. Mitigation actions such as silencing an alert or reverting a commit are proposals that a person must approve; "Resolve never has direct access to execute write APIs on your monitoring platforms" (docs). Code changes come as PRs that also need approval.
• Models. "Our own domain models and the best frontier models" (overview), with per-customer fine-tuning: "Models are tailored specifically to your data and needs" (security).
• Integrations. 60+ pre-built integrations. The integrations page lists AWS, Google Cloud, Microsoft Azure, Kubernetes, Datadog, Grafana, Splunk, Dynatrace, Elastic, New Relic, Honeycomb, Sentry, Logz.io, PagerDuty, Opsgenie, ServiceNow, Slack, Microsoft Teams, Confluence, Notion and more, plus MCP, APIs and webhooks.
• Deployment. Two models: "Shared", a multi-tenant environment with a satellite, and "Dedicated", a single-tenant cloud deployment with bring-your-own keys (security). The Resolve Satellite runs in your Kubernetes cluster or on ECS Fargate and proxies queries back to Resolve.
• Compliance. SOC 2 Type II, GDPR and HIPAA (security).

Customer results it publishes include Coinbase ("surfaced accurate root causes 72% faster than our teams") and DoorDash ("from hours to minutes for investigations in many incidents") on its customers page. Its about page also claims 95% RCA accuracy. We found no published test set or scoring method behind that figure, so it cannot be checked from outside.

Resolve AI (resolve.ai) is not Resolve Systems at resolve.io, which sells workflow automation for IT teams.

How much does Resolve AI cost?

Resolve AI does not list a price. The pricing page describes the model and asks for your details:

• Credit-based. "Credits are used as ResolveAI agents perform defined types of work across your production environment."
• Five billable actions: chats, tasks, investigations, incidents and triages; tasks are metered as chats unless your contract lists them separately (billing docs).
• Per-action cost is in the contract. "How each action is metered, and how many credits it costs, is defined in your contract with Resolve."
• Caps. Admins can set a monthly cap that pauses automated work. By default it "is not a hard spend stop", because work a person starts keeps running; a "Block manual work" option makes it apply to all usage.
• Buying. The site's "Try now" button goes to the demo booking page. It is also listed on AWS Marketplace.

So for a budget you need a quote. The alternatives below that publish prices let you estimate before a call.

Who needs a Resolve AI alternative, and why?

• Teams that need a price before a sales cycle. A small team may not be able to open a procurement process just to learn whether a tool fits. Cleric and Datadog publish their unit prices and NeuBird its credits per run; the open source agents cost what your model calls and hosting cost.
• Teams that need the agent inside their own network. Resolve offers a shared multi-tenant environment or a dedicated single-tenant cloud deployment, with the satellite as the gateway inside your network. If the reasoning layer itself must run in your VPC or an air-gapped network, look at Aurora, HolmesGPT, Traversal's private deployment or NeuBird's VPC option.
• Teams that want to pick the model. Resolve combines its own models with frontier models of its choice. Aurora, HolmesGPT and K8sGPT let you set the provider, including local models through Ollama; Traversal advertises bring-your-own-model.
• Teams that want to verify accuracy themselves. You cannot read Resolve's agent or rerun its 95% figure. With an open source agent you can read the prompts and every tool call, and rerun our 19 scenarios.
• Teams already paying for one observability vendor. If everything lives in Datadog, Bits Investigation is bought in Datadog AI credits on the same bill. See our Datadog Bits AI alternatives page for its pricing.

Resolve AI alternatives compared

Checked against each vendor's site or repository on 2026-10-21. "Lab-tested" means we ran it on our 19 faults; the others we have not run.

How did the open source alternatives do on real faults?

We built a small app (an nginx frontend, a Python api, a worker, redis and a load generator) on a 3-node kind v0.33.0 cluster running Kubernetes v1.37.0, injected one known fault per scenario (two in scenario 12) and gave Aurora and HolmesGPT the alert; K8sGPT takes no alert and scanned the namespace. Each tool ran twice per scenario, 38 runs per tool. All three got the same admin kubeconfig, each behind its own guard. Aurora (commit 87d22baf, under its default read-only command policy) and HolmesGPT 0.43.0 (holmes ask, default toolsets) both used bedrock/us.anthropic.claude-sonnet-5. K8sGPT 0.4.40 ran k8sgpt analyze --explain --namespace shop on us.anthropic.claude-sonnet-4-5, because Bedrock rejects the temperature parameter it sends to Sonnet 5. Aurora's runs are from 2026-10-02, HolmesGPT's and K8sGPT's from 2026-10-15 and 2026-10-16, on the same cluster and faults.

A judge model (us.anthropic.claude-opus-5-5) graded every answer against the injected fault. A person at Arvo read all 38 Aurora grades and changed none. The 76 HolmesGPT and K8sGPT grades are judge-model only: a second model re-read all 76 and agreed with every one, a third pass graded them from scratch and agreed on all 38 HolmesGPT verdicts while disputing 6 K8sGPT labels, and no person has checked them yet. A blinded regrade, with tool names removed from the judge's prompt, matched the published verdict in 38 of 38 Aurora runs, 37 of 38 HolmesGPT runs and 36 of 38 K8sGPT runs. The two prompts differ in wording, and HolmesGPT's own allow-list blocked kubectl rollout history in 12 of its runs; Aurora's default policy blocked the same command in its runs. The /benchmark page computes a two-sided Fisher exact test on the totals, p = 0.52, so the accuracy gap between Aurora and HolmesGPT is not significant, and it compares human-read grades with judge-only ones. HolmesGPT is clearly faster.

K8sGPT answers a different question. It scans a namespace and explains what its analyzers flag; it does not take an alert or follow evidence. It is the fastest health scan of the three, and the wrong tool for "why did this alert fire?". More in HolmesGPT vs K8sGPT.

One fault, three answers

Scenario 13 is the kind of fault a vendor demo rarely shows: every pod is Running and Ready, and a NetworkPolicy silently drops traffic from the api to redis. The alert was HighErrorRate [critical] namespace=shop service=frontend: 100% of requests to /api/ returned 5xx over the last 5 minutes (threshold 5%).

Aurora, run 1 (202 s, 25 tool calls, $0.69):

The root cause was a NetworkPolicy named redis in the shop namespace, created at 2026-10-02T20:23:54Z, which restricts ingress to redis:6379 to pods labeled app=worker only [20, 22]. The api pods are labeled app=api, not app=worker [23], so their connections to redis were silently dropped by the policy [...]

HolmesGPT, run 1 (110 s):

The api pods (api-f64486c4c-f7fpv, api-f64486c4c-nz5j6, label app=api) are not covered by this allow-list, so their connections to redis:6379 are now dropped by the policy.

K8sGPT, run 1:

No problems detected

Both agents got it in both runs. HolmesGPT took 110 s and 118 s, Aurora 202 s and 444 s (its second run waited for a worker slot behind a background discovery job). The fix steps are in the runbook NetworkPolicy blocking traffic.

Where they failed

Scenario 12 has two faults; the alert covers only a worker OOMKilled. Both agents found the OOM in both runs and missed that the frontend Service selected no pods. Aurora also wrote that the frontend was healthy:

Other workloads in the shop namespace (api, frontend) remained healthy and unaffected [5, 14].

Pod status was its only evidence. The checks that catch it are in a second fault the alert does not cover. Every transcript and grade, run by run, is on /benchmark.

How does Grafana compare to Resolve AI for autonomous incident remediation?

Grafana's equivalent is Grafana Assistant Investigations, for incidents that need analysis across metrics, logs, traces and profiles. You describe the problem in Assistant Workspace, or alerting and IRM webhooks start one automatically; it tests hypotheses and writes a report with findings and recommended next steps. It needs a Grafana Cloud stack: per Grafana's docs, investigations are "available only with the full Grafana Cloud feature set", so self-managed Grafana alone does not get them. Resolve, by contrast, reads Grafana as one of many sources and proposes approved mitigations such as alert silences. If your telemetry is in self-hosted Grafana, Prometheus and Loki, an open source agent that queries them directly is the closer fit; Aurora takes Grafana alerts and HolmesGPT has Grafana, Loki, Tempo and Prometheus toolsets.

Is Resolve AI compatible with all cloud providers?

Resolve's integrations page lists AWS, Google Cloud and Microsoft Azure, plus Kubernetes through the satellite. Its Azure integration is read-only and "covers Azure Commercial Cloud only", reading Azure Monitor, Application Insights and Entra logs. For code it supports github.com, GitHub Enterprise, GitLab, Bitbucket, Azure DevOps and self-hosted Git, with Git commands run either in Resolve's cloud or on your satellite (docs). Aurora runs CLI investigations on AWS, Azure, GCP, OVH, Scaleway and Kubernetes.

How to test any Resolve AI alternative before you sign

Vendors show you their best incidents. Bring yours, or bring ours: every scenario in Arvo-AI/aurora-lab has the alert, the true root cause and the findings an answer must contain.

git clone https://github.com/Arvo-AI/aurora-lab && cd aurora-lab
./lab.sh up
./lab.sh break 13-networkpolicy-blocks-redis

Connect the trial agent to the cluster, give it the alert line from scenarios/13-networkpolicy-blocks-redis/scenario.yaml, and compare its answer with the must_identify list in the same file. Scenarios 11, 12 and 19 are the ones both agents found hardest. ./lab.sh reset restores the app. To diagnose one failure on your own cluster, /diagnose walks through it.

Which Resolve AI alternative fits which team?

What this comparison does not cover

The lab is Kubernetes-only, synthetic, and the faults are ours. None of the hosted products ran it, so their rows are what the vendors publish, not what we measured. HolmesGPT 0.44 and 0.45 were already out when we ran 0.43.0, and K8sGPT 0.4.41 shipped on 2026-10-21, so their current versions are untested. Aurora's $0.35 median is model cost only: you also run the servers, and Aurora's background discovery cost about $1 per cluster connection in the lab (REPORT.md).

Verified 2026-10-21: Resolve AI pages and docs, Datadog pricing, Cleric, NeuBird and Traversal pages, Grafana docs and GitHub repos as linked; lab numbers from results.json generated 2026-10-21.

Aurora is on GitHub under Apache 2.0, or book a demo to see it on your alerts.