On-Premise vs SaaS APM: A Practical Guide

The short version

Question	Points to SaaS	Points to on-premise
May telemetry leave your network?	Yes, with a contract and a transfer assessment	No, or only after a long approval process
Do production systems have outbound internet?	Yes, or a proxy path is easy to get	No, or every egress rule is a change request
Who can run a stateful platform?	Nobody has spare capacity	A platform team already runs databases and clusters
How spread out are your workloads?	Many clouds and regions, few on-premise	Most of it sits in your own data centres
How steady is telemetry volume?	Very spiky, hard to forecast	Reasonably predictable
How fast do you need new features?	As soon as they exist	On your own change calendar

If most of your answers land in one column, you probably have your answer. The rest of this page is for mixed cases, which is most regulated organisations.

What actually differs

Both models collect similar data with similar agents or OpenTelemetry SDKs. The differences are about where the backend lives and who is responsible for it.

Data flow

With SaaS APM, spans, logs, metrics and browser sessions cross your perimeter and sit in a vendor's storage. That data is more sensitive than people first assume. Span attributes can hold URLs with account numbers, SQL text, internal hostnames and user identifiers. Log lines can hold almost anything. Scrubbing rules help, but they are a filter you have to keep correct as applications change, and one missed attribute means personal data has already left.

With on-premise APM, the same data stays inside your network, under your access controls and your backup policy. The exposure does not go away: an APM backend is still a concentrated store of sensitive operational data, and it needs the same care as any other. What changes is who is accountable and which boundary the data crosses.

Operating work

SaaS moves the storage layer, upgrades, scaling and availability of the backend to the vendor. Your remaining work is agent rollout, instrumentation, cost control and dashboards.

On-premise keeps all of that and adds running the backend. You will need to plan storage, watch disk growth, back up what matters, apply upgrades and be on call for a system that is supposed to tell you when other systems are down. If your monitoring runs on the cluster it monitors, you also need a way to notice when that cluster fails. A small external probe or a second cluster is a common answer.

Availability relationship

A SaaS backend fails independently of your data centre, which is valuable: when your site goes down, your monitoring still shows it. It also depends on your internet path, and when that path fails, agents buffer briefly and then drop data.

An on-premise backend does not depend on the internet at all, which suits sites that keep running through an upstream outage. It may share power, network or cluster with what it watches, so failure domains deserve deliberate thought.

Upgrades and change control

SaaS features arrive whenever the vendor ships them. For many teams that is a benefit. For teams with strict change windows, a UI or behaviour change that nobody approved can be an audit question. Self-hosting gives you the calendar, and also the burden: an upgrade you keep postponing is a security patch you have not applied.

Cost shape

We will not quote numbers here, because list prices change and every contract differs. The structure is what matters. SaaS cost usually scales with hosts, ingested volume, indexed volume or similar units, so a chatty deployment can change the bill. On-premise cost is hardware, storage, licence and staff time. It scales more in steps, because you add capacity in chunks, and more of it is hidden in internal budgets. Compare them over several years with your own volumes and include staff time on both sides. Instrumentation and dashboard work does not disappear in either model.

A capacity sketch you can redo with your own numbers

Run this before anyone argues about either model. The figures below are an invented example, not a benchmark of any product.

Suppose 300 services together handle 4,000 requests per second, with an average of 12 spans per request and roughly 500 bytes per span on the wire.

Spans per second: 4,000 × 12 = 48,000
Raw span throughput: 48,000 × 500 bytes = 24 MB/s
Per day: 24 MB/s × 86,400 s ≈ 2.07 TB

Now apply tail-based sampling that keeps 10% of traces: about 207 GB per day. Over 14 days of retention that is about 2.9 TB before compression and replication overhead.

The same arithmetic drives both options. On SaaS, it estimates ingestion and indexing volume. On-premise, it sizes disks. It also shows why tail-based sampling belongs in the conversation early: it changes the answer by an order of magnitude in either model. Also check the outbound link. In this example, sending the unsampled stream would take about 192 Mbit/s of sustained egress before compression.

Questions that decide it in regulated environments

Regulation seldom says "no SaaS" in so many words. Requirements around personal data, outsourcing, cross-border transfer and operational resilience differ by country and sector, and the rules that apply to you depend on your specific facts. KVKK, GDPR, DORA, PCI DSS and banking supervision rules all touch this topic in different ways. Treat the following as questions for your compliance and legal teams, not legal advice.

Is any telemetry personal data? Assume yes unless you can show otherwise, because of user identifiers, IP addresses and free-text logs.
Would the APM vendor count as an outsourced provider or a sub-processor? That can trigger notification, contract and exit-plan work.
Where is data stored and from where can vendor staff access it? Storage region and support access are separate questions.
Can you prove deletion? Ask how a retention change or an erasure request is carried out and evidenced.
Do your AI features send data elsewhere? If an assistant calls an external model, your data flow has a new branch.
How long does approval take? In some organisations, a new external data flow takes longer to approve than the whole rollout. That delay is a real cost.

Our data residency guide goes further on the Turkish context.

When SaaS is the better choice

You should probably pick SaaS, or stay on it, when several of these are true:

Your workloads already run in public cloud, so telemetry going to another cloud service adds little new risk.
You have no team willing to own a stateful platform, and hiring for it is not planned.
You need global coverage in many regions without deploying anything in them.
Your legal team has already approved the data flow.
You want a broad library of integrations with SaaS products, cloud services and managed databases.
Your telemetry volume is small enough that the bill is modest compared with the staff time of running a platform.

A small team that spends a week a month keeping its self-hosted monitoring alive has made a poor trade. Moving to on-premise because it sounds more secure, with no specific requirement behind it, often ends that way.

When on-premise is the better choice

Network rules forbid outbound paths from production, or a segment is fully air-gapped. See air-gapped observability.
Your compliance position relies on telemetry staying in your own data centres.
Most of your estate runs in your own OpenShift or Kubernetes clusters and on Linux servers, and a platform team already operates them.
You want AI-assisted analysis but will not send logs and traces to an external model.
You need to operate through an internet outage with full visibility.

Hybrid is an option, with a catch

Some organisations keep SaaS for public-facing digital channels and on-premise for core systems. OpenTelemetry makes this more workable than it used to be, because the same instrumentation can export to either backend, and a collector can route by environment.

The catch is the trace that crosses the boundary. A request from a cloud-hosted web tier to a core banking service produces half a trace in each system unless you deliberately reconcile them. Decide in advance how responders will follow those requests, and keep W3C Trace Context propagating across the boundary so that at least the trace ID matches in both tools. Check, too, that the header itself carries nothing your policy forbids.

How to run a fair evaluation

Avoid comparing a polished SaaS trial with a half-installed on-premise proof of concept, or the other way round. Use the same services for both and score the same tasks:

Time from nothing to the first useful trace in a real, non-demo service.
Whether a recent real incident could have been diagnosed from what you captured.
What it took to get approved: firewall rules, security review, SCC or privilege exceptions.
A planned failure: stop part of the backend or cut the link, then see what is lost and how the system recovers.
One upgrade, performed by the people who will do it for real.

How Wrentrix is deployed

Wrentrix is an on-premise platform. It runs on OpenShift, Kubernetes or Linux servers and installs through a Helm chart, an OpenShift operator or Docker Compose. No telemetry leaves your network, and it works fully air-gapped with no licence calls and no external model. You still own the operating work described above, and it uses PostgreSQL for storage, so your team needs to be comfortable running that. If you want to look before committing to anything, the live demo runs in the browser with sample data, and the on-premise APM overview describes the deployment in more detail.

Frequently asked questions

Is on-premise APM more secure than SaaS?

Not automatically. It removes an external data flow and a third party from your risk picture, but you take over patching, access control, backup protection and monitoring of the platform itself. A poorly run internal system can be weaker than a well-run vendor service, so the answer depends on your team and threat model.

Is on-premise APM cheaper than SaaS?

It depends on volume, retention and how you count staff time, and there is no general answer. High and steady telemetry volumes tend to favour owned capacity, while small or irregular estates tend to favour SaaS. Model both over several years with your own numbers, and include the people who will run the system.

Can I use SaaS APM in a network with no internet access?

Generally not directly, because the agents have to reach the vendor's endpoint somehow. You can sometimes route through a controlled proxy or gateway, but then the network is no longer truly isolated. A truly air-gapped segment needs a backend inside it.

Can I move from SaaS to on-premise APM later without re-instrumenting?

If your applications use OpenTelemetry SDKs or the OpenTelemetry operator, usually yes, because you mostly change the export destination. Vendor-specific agents, custom dashboards, alert rules and SLO definitions still have to be rebuilt. Keeping instrumentation vendor-neutral from the start is the cheapest form of exit plan.

No. Data location is one factor among many, and those laws also cover lawful basis, retention, access and security measures, among other things. Keeping data inside your network can simplify some questions about transfers, but your legal team has to assess your full situation. This page is not legal advice.