Onam vs Orca
Orca made agentless scanning credible to buyers who had been told an agent was unavoidable. If it is on your shortlist alongside us, run these seven questions against both.
How to read this page. We do not make claims about Orca’s product here. Products change monthly and a page full of second-hand assertions about someone else ages into a lie. What follows is seven questions worth asking any cloud security platform, answered for Onam only — then, plainly, where Orca is strong and where we are not. Ask Orca the same seven.
The seven questions
Our answers. Put the same list in front of Orca.
- 1
How many clouds get first-class treatment?
Seven, on the same footing: AWS, Azure, GCP, OCI, Alibaba Cloud, IBM Cloud and Kubernetes. 11,433 posture rule definitions across 549 cloud services — the all-cloud totals, not a per-cloud figure. Ask any vendor for the per-cloud breakdown rather than the headline number; that is where first-class and box-ticked diverge.
- 2
Is the analysis cross-cloud, or per-cloud silos side by side?
One security graph. Every engine writes the same finding contract into one store, so a path can start in one cloud and end in another. Correlation is a property of the data model here, not a report generated over separate databases.
- 3
Agentless — and how long to first finding?
Agentless and read-only. Nothing is installed in your workloads and nothing is written back to your accounts. The trade-off is stated in the trust whitepaper: read-only scanning cannot see inside a running process.
- 4
How does it prioritise — severity labels or business impact?
By verified attack path, then priced with FAIR using named external inputs. A ranked list of criticals tells you what is broken; a priced path tells you which chain reaches data and what it would cost. Ask to see the arithmetic, not just the ranking.
- 5
Does it catch toxic combinations across engines?
That is the whole design. The chain that reaches your data is usually four ordinary findings in a row, none of which any single rule would flag. Composition across posture, identity, data, workload and SaaS happens on one graph rather than by joining exports.
- 6
Is compliance evidence continuous or point-in-time?
A control is evaluated once and reported against 78 compliance frameworks, continuously, with each gap connected to the path it sits on. Evidence for an audit — your auditor still decides what satisfies a control.
- 7
Does coverage span code to runtime?
Posture, attack paths, identity (CIEM), data, containers and Kubernetes, SaaS posture across 8 platforms, and cloud detection and response — 29 engines on one graph rather than six products stitched together.
Where Orca is genuinely strong
A comparison page that finds nothing good to say about the other side is marketing, not evaluation. These are real advantages and you should weigh them.
- An early and influential agentless architecture — it moved the whole category away from agent-everywhere
- A mature product with a long track record in production estates
- A strong reputation for interface and workflow quality
- An established ecosystem and integration surface
The honest gap
The honest gap: Orca has been deployed at scale for years and has the operational scar tissue that comes with it. We are newer, and our integration surface is smaller. Judge us on the graph and the paths, not on breadth of integrations.
Do not take our word for any of it
Run a read-only scan against one account and tell us whether the attack paths we surface are real. If they are noise, we want to hear that — it is more useful to us than a signature. That is the same offer we make to everyone, and it is the only claim on this page you can check yourself today.
Other comparisons
Last reviewed 15 August 2026. Onam’s figures come from our published fact set; the strengths above are general market observations, not claims about Orca’s current capabilities. If anything here is wrong or out of date — including anything about Orca — tell us at hello@onamsecurity.com and we will correct it.