Anyshift vs Traversal

At a glance

	Anyshift	Traversal
Core idea	One versioned graph reconciling a resource across the tools that describe it	Production modelled as a graph, searched causally
Primary question answered	What was true at a past moment, and what changed since	What is causing this symptom now
Sources reconciled	AWS, GitHub, Kubernetes, Datadog and Jira into one node per resource	Telemetry and production signals across the estate
Time dimension	Versioned, so past states are queryable directly	Oriented to the live system during an incident
When work starts	On the question being asked	Unprompted. Workers act on the signal rather than on being paged
Published scale	Not published as a node count	Node counts in the millions
Deployment	Not published as a distinct deployment model	Bring your own cloud
Published accuracy	Not published against a public benchmark	Not published against a public benchmark
Structural blind spot	A resource no connected tool describes is absent from the graph	A dependency absent from the graph is invisible to the search

How the two differ in practice

Both of these build a graph, which makes them look adjacent, and they are pointed at different halves of the same problem.

Anyshift's contribution is reconciliation and time. The same piece of infrastructure exists as an ARN in AWS, a manifest in a Git repository, a pod in Kubernetes, a service in Datadog and a ticket in Jira, and none of those systems agrees with the others about what it is called or what it depends on. Anyshift collapses those into a single node and versions it, so the question of what the system looked like at a particular moment has a direct answer rather than being reconstructed by hand from five tools with different retention windows.

Traversal's contribution is causal search at scale during an incident. The graph is there to be traversed from a symptom towards a cause, the published node counts in the millions are a statement about how much of production the model holds, and the workers acting unprompted mean that search can begin before anybody has noticed.

The practical consequence is that they suit different recurring questions. If the question that keeps costing your team time is what changed between Tuesday and Thursday, Anyshift answers it directly and Traversal does not. If the question is what is causing the current symptom, Traversal is built for it and Anyshift is a tool you would use during the investigation rather than one that conducts it.

They also differ on when they do work. Anyshift responds to questions. Traversal generates output nobody requested, which is valuable when it is right and another queue when it is not.

Where each one is stronger

Anyshift is stronger on estates where the tooling disagrees with itself, which is most estates past a certain size. The reconciliation work is unglamorous and genuinely hard, and teams that have tried to build it internally tend to appreciate what it costs. The time dimension is the differentiator: an incident that began with a change three days ago is very difficult to investigate from tools that each hold a live view, and a versioned graph turns that from an archaeology project into a query.

Traversal is stronger during the incident itself. Causal search over a large graph is aimed at the moment when a symptom is visible and the cause is several hops away, and unprompted workers close the gap between something going wrong and somebody noticing, which is often a larger share of total incident time than the investigation.

Their limits are the same limit in different clothes, and it is the limit every graph-based product in this category shares, ours included. A graph cannot reason about what it does not contain. For Anyshift that means a resource no connected tool describes is simply absent. For Traversal it means a dependency missing from the model is invisible to the search. Coverage, not scale, is what determines whether either works on your estate.

How to choose

Work out which question you are actually paying to answer, because these two are not substitutes and buying the wrong one produces a product that works exactly as advertised and does not help.

Write down the last five incidents and mark each one as either a what-changed problem or a what-is-happening problem. The split is usually lopsided and usually surprises the team doing the exercise. That distribution is the decision.

Then test coverage rather than scale on whichever you shortlist. Take a dependency you know is unusual in your environment, something declared nowhere and discovered only through an outage, and check whether it appears in the model at all. What causal AI means for root cause analysis covers why this is the question that separates products that work on your estate from products that demo well.

If the underlying need is simply an accurate picture of what is running and what depends on it, that is a narrower requirement than either product sells, and what a derived service map contains sets out what that consists of on its own.