Traversal alternatives, 2026

At a glance

	Traversal	NOFire AI	Cleric	Anyshift
Core idea	A graph of production, searched causally	A live, time-versioned model of production. Every answer is a specific deploy, config or code change	Read-only investigation with accumulated memory	One versioned graph reconciling a resource across tools
When work starts	Unprompted. Workers act on the signal	On the alert, and before a change ships	On the incident	On the question being asked
Published accuracy	82% RCA accuracy, self-reported	89% Top-1 on RCAEval, a public benchmark	92% actionable findings, self-reported	30% faster root cause, self-reported
Published scale	Node counts in the millions	Not published as a node count	Not published	Not published
Time dimension	Oriented to the live system	Change history attached to each answer	Memory across incidents	Versioned, past states queryable
Default authority	Read-only and agentless, actions proposed for approval	Read-only collectors, acting under an enforced bound	Read-only by default	Not published as a read-only guarantee
Where agent code runs	Nowhere. Agentless and read-only	Read-only collectors. Coding agents run in a microVM via brig, our open-source sandbox	Not applicable. The agent does not act	Not published
Deployment	Bring your own cloud	Read-only collectors, in-VPC, BYOC	Vendor-hosted, SOC 2 Type II	Not published

Why teams look for an alternative

Traversal is a serious product built on a defensible bet, and most of these searches are not complaints. Three things drive them.

The first is evidence. Traversal reports 82% root-cause accuracy from its own customer deployments, alongside graph scale in the millions of nodes. A buyer who has acted on a confidently wrong answer often wants a figure they can reproduce. That means a public dataset and a stated method, rather than a result from someone else's estate. When that becomes the deciding question, the shortlist narrows to whoever has published against one.

The second is unprompted work. Workers investigating without waiting to be paged is genuinely valuable when somebody reads the output. In an organisation already struggling with alert fatigue, a second stream of unrequested findings can make things worse before it makes them better, and teams sometimes discover this only after deployment.

The third is coverage rather than capability. Anything reasoning over a modelled graph is blind to a dependency the graph does not contain. That limit is shared with us and with Anyshift, and it is not a criticism of Traversal, but teams whose incidents keep landing outside the model start looking for a different construction.

Occasionally the driver is the time dimension: the recurring question turns out to be what changed between two moments rather than what is failing now, and that is a different product.

Read-only answers the safety question by removing the ability to act. It also caps what the product can do. We went the other way. Coding agents run inside a microVM through brig, our open-source sandbox. Production actions pass a gate that can hold them or refuse them before they run. The agent writes its own log, and that log shows what the agent reported. The gate writes a second record, outside the agent, and an auditor reads that one. The map is versioned, so you can ask what the agent knew when it proposed an action, and what changed after.

Where the current tool still wins

Unprompted investigation is the capability least likely to transfer. Most alternatives investigate on an alert or on request. If the gap between something going wrong and somebody noticing is a large share of your incident time, Traversal closes it directly, and no amount of diagnostic quality in a competitor helps if the investigation starts forty minutes late.

Bring your own cloud is the second. Telemetry never leaving your account is the difference between a possible and an impossible purchase in some regulated environments, and it is a shorter conversation with a security reviewer than any certification. NOFire AI answers the same requirement with in-VPC processing and BYOC, and Cleric answers it differently with SOC 2 Type II and a commitment not to train on customer data, but not everyone in this category offers an answer at all.

Graph scale is the third, and it is a real signal even though it is not an accuracy claim. Holding millions of nodes is a statement about which size of estate the product is built for, and on a large interconnected estate that ambition matters.

How to switch

Do not migrate first. Run the alternative alongside Traversal for a fortnight on the same alert stream, and score the first hypothesis rather than the eventual one on incidents whose true cause you already know. That measurement is the whole decision and neither vendor's material can settle it for you. The AI SRE Benchmark sets out how that scoring works on a public dataset if you want a method to copy.

Test coverage, not scale, on both. Take three incidents whose cause crossed a service boundary and check whether the dependency involved appears in each product's model at all. A large graph that does not contain your unusual dependency is not better than a smaller one that does, and what causal AI means for root cause analysis sets out why coverage rather than size is what decides whether this approach works on your estate.

Decide in advance whether unprompted investigation is something your team will read. If nobody has time to triage findings nobody requested, that capability is a cost rather than a benefit, and you should stop paying for it deliberately rather than discovering it later.

Expect the connectors to be the work. Every product here reads the telemetry, deploy history and incident records you already keep, so switching cost is integration time rather than a migration project.