HealOps vs Resolve AI: AI SRE Comparison (2026)

HealOps and Resolve AI are close competitors: both are agentic SREs that investigate production incidents with parallel hypothesis testing and propose a code-level fix without auto-deploying it. The differences are not whether they remediate, but what artifact they hand you and who can buy them. HealOps attaches a regression test that reproduces the incident to every pull request, keeps your logs in your own cloud with no server-side investigation memory, and isn’t restricted to enterprise sales-led deals.

Resolve AI is the best-funded pure-play in the category, founded by OpenTelemetry co-creators, with a strong investigation engine. This post is fair about what it does well and precise about where HealOps differs. Note that data residency is not the headline distinction here — both read in place with least privilege. The real axes are the regression-test-attached PR, server-side memory, and accessibility. Where a capability is unverified, we say so.

At a glance

Capability	HealOps	Resolve AI
Autonomous root-cause investigation	✓	✓
Parallel hypothesis testing	✓	✓
Learns from past incidents	✓	✓
Ships fix as a reviewed pull request	✓	✓ (generates remediation PRs)
Regression test attached to the fix	✓	Not advertised
Never auto-deploys (human merges)	✓	✓ (roadmap toward closed-loop)
Reads in place, least privilege	✓	✓
Server-side investigation memory	✕ (none)	Retains history server-side
Logs stay in your cloud	✓	Processed in Resolve’s cloud
Accessibility	Private beta, not enterprise-only	Enterprise-only, sales-led
Scope	General infra	General (prod operations)

What Resolve AI does well

Resolve AI is a serious, well-built product and currently the best-funded pure-play AI SRE. Publicly available information points to roughly $190M+ raised, including a $125M Series A at a $1B valuation in December 2025, with a later extension reportedly near a $1.5B valuation (exact figures as of writing). Its founders co-created OpenTelemetry and came out of Splunk, which shows in the depth of the investigation engine.

Functionally, Resolve AI does the hard parts well. It runs parallel hypotheses, learns from past incidents, and can map a regression back to the exact pull request that introduced it. On remediation it generates remediation pull requests and operations commands, and it deliberately keeps a human in control rather than auto-deploying — describing a roadmap “toward closed-loop” automation. It executes some operations actions in the runtime plane. On data handling it is strong: it reads in place with no raw-data ingestion, uses least privilege, and carries SOC 2 Type II, GDPR and HIPAA. Its customer logos include well-known names like Coinbase, DoorDash and MongoDB.

If you are a large enterprise looking for a deeply-resourced autonomous investigation engine from a team with real observability pedigree, Resolve AI is a credible choice.

Where HealOps differs

The two products share the core agentic loop — see what is an AI SRE for the shape of it — so HealOps’s differences are specific and deliberate.

The regression test attached to the PR. This is the sharpest difference. Resolve AI generates remediation pull requests, but there is no public evidence it attaches a regression test that reproduces the incident. HealOps does, on every PR. The test is what turns “a plausible fix” into a verified fix: it fails before the change, passes after, and becomes a permanent guard against the same failure returning. A diff with no test closes today’s page and invites tomorrow’s. This artifact — incident in, tested diff out — is the part almost no competitor ships, and it is the core of why your AI SRE should open a pull request, not deploy to prod.

No server-side investigation memory. Both tools read in place, so raw data residency is comparable — we won’t pretend otherwise. But Resolve AI retains investigation history server-side, building a memory of your environment in its cloud. HealOps does not. It uses a read-only role, keeps your logs in your own cloud, reads only the narrow incident slice it needs, and does not accumulate a server-side model of your systems. For teams that want the agent’s footprint to end when the incident does, that is a structural difference.

Accessibility. As of writing, Resolve AI is enterprise-only and sales-led. HealOps is in private beta and built to be adoptable beyond top-down enterprise deals, so a platform team can run the fix-shipping loop without an enterprise procurement cycle.

Portable runbooks. HealOps promotes each verified fix into a human-owned runbook entry and a continuous regression check, so knowledge stays portable and inspectable rather than locked inside vendor-side memory.

Which should you choose?

Pick Resolve AI if you are a large enterprise that wants the best-funded pure-play investigation engine, value its OpenTelemetry-rooted pedigree and enterprise certifications, and are comfortable with a sales-led rollout and investigation history retained in the vendor’s cloud.

Pick HealOps if you want the remediation artifact to be a reviewed pull request with a regression test attached, prefer your logs to stay in your own cloud with no server-side investigation memory, and want a tool you can adopt without an enterprise-only sales motion.

Both refuse to auto-deploy, which is the right call for production code. For the wider field — including Cleric, Traversal and incident.io — see the best AI SRE tools in 2026, and compare against the closest Slack-native hybrid in HealOps vs incident.io.