How to define technical policies according to client type in an MSP

Technology often fires the imagination with utopian scenarios, such as complete automation in the style of Star Trek or, for the topic at hand, standardisation. So, when MSPs decide, at last, to put their operations in order, they long for a single technical model that can be applied to every client. But the dream collides with reality from the very first minute, and reality always wins. That is when we realise that treating different clients in the same way means homogenising badly rather than standardising.
And it generates just as much noise as the chaos we were trying to eliminate.
When we standardise, we tend to try to erase differences, but the key is to group together those that are similar in order to build repeatable frameworks.
That is why the first step towards effective standardisation in an MSP is proper segmentation, so we are going to explore in greater depth how to homogenise operations without the price outweighing the benefit.

Why treating all clients in the same way creates problems

When we apply the same technical policy to a small local accountancy firm with five employees and to the regional hospital, that single model fails at both ends at the same time, the worst kind of fire.
At the upper end, we go wrong by over-monitoring simple clients with the same arsenal of checks, thresholds and ten-second verifications that a critical infrastructure deserves. We are taking the Ferrari to go grocery shopping, and the result is a tsunami of irrelevant notifications. That is when we discover that alert overload is like today’s advertising overload: the fastest way to condition a team to ignore notifications, including the ones that matter.
At the lower end, we fall short with critical clients. Policies designed to avoid drowning a small client in irrelevance are far too lax for an organisation that cannot afford even two minutes of downtime. Here, the issue is a lack of coverage, and it is much worse than noise because the damage becomes visible late and, by the time we respond, the tumour has metastasised and we are facing a serious problem.
And between these two extremes, a silent bill also takes up permanent residence, which we pay in the form of:

• Unnecessary time consumption, chasing false positives with a butterfly net across environments that never needed that level of attention.
• Difficulty justifying the service and its cost, because when all clients pay the same, the critical client feels they are paying too much, while the simple client feels they are receiving nothing special. And although we work in IT with silicon and cables, the client’s feelings still rule.
• A decline in operational efficiency that increases with every new client we gain instead of being diluted, as would be expected with effective standardisation.

Ultimately, the single-model approach is as easy to draw on a whiteboard as it is expensive to operate in the day-to-day trenches.

Which variables can be used to segment clients in an MSP

So, how should we approach standardisation? I have already revealed that the first step is segmentation and, at last, there is some good news here: there is no need to make up categories based on guesswork.
There are objective criteria that enable professional and useful MSP client segmentation, such as:

• Business criticality: what actually happens if the client’s system goes down for an hour? The consultancy firm can focus on other tasks or take the opportunity to have a coffee, but the hospital cannot, and neither can an e-commerce business when Black Friday arrives.
• Environment size: are we dealing with a garden of ten endpoints or a jungle of three thousand? Volume changes the rules of the game and is a key factor in segmentation.
• Technical maturity: does our client have its own team that understands what we are monitoring, or are we its only IT department and technology seems like magic to them?
• Security or compliance obligations: a client subject to draconian regulations, such as a company operating in a sector legally considered strategic, cannot use the same retention or auditing policies as the shoe shop on the corner.
• Infrastructure complexity: because we will manage homogeneous environments as flat as a plain, hopefully, but also hybrid architectures involving cloud, virtualisation and half a dozen technologies fighting tooth and nail for resources.
• Type of service contracted: the ultimate law of the land is whatever has been signed, so what the client has purchased logically determines how far our responsibility extends.
• Expected availability level, which almost always ends up being written into an SLA (Service Level Agreement) and should be respected, because the penalties for non-compliance are usually written further down as well.

In short, it is the Prime Directive from Star Trek, under which Starfleet does not intervene in the same way on every world. Its actions depend on the level of development of each civilisation, because applying the same formula to a pre-industrial culture and to another capable of bending space has disastrous consequences every other episode.
Our client segmentation should work in the same way. It would be convenient if the deciding factor could be our own comfort in deploying the same thing for everyone, but the maturity and real context of each client take precedence.

Which technical policies usually need to be adapted by client type

In real life, we cannot segment every detail and, for the sake of operational hygiene and the team’s sanity, some decisions are best kept identical for everyone.
However, there are a number of technical policies that we will necessarily need to adapt according to the type of client in an MSP.
These are the areas where a single model would cause more pain than it solves, including:

• MSP monitoring policies: in other words, what is monitored and how deeply in each environment. This is impossible to unify, so it is better not to waste time trying and to segment properly from the outset.
• Alert thresholds, which should be triggered earlier for a critical client and only when there is a genuine reason for a simpler one.
• Check frequency, because the parameters of a three-person shop probably do not need to be measured every second.
• Reporting, which will be detailed for a technical client and, for a manager, a readable summary that does not look like a hieroglyph.
• Permitted automations, since we cannot, or rather should not, allow the system to act on its own in every environment.
• Maintenance windows, since they will be determined by the activity and working hours of each business.
• Incident escalation and response, which must be aligned with the criticality and the SLA we have signed.

The lesson is that, while the operational logic may be the same for different clients, the parameters that give it its final form must be adapted.
In fact, confusing logic with parameters is the source of far too many complications in standardisation.

How to standardise without creating a different exception for each client

We have reached the heart of the matter, because between an identical single model and the idea that “every client is a special snowflake”, there is a middle ground where effective MSP standardisation lives.
This point of balance takes the form of standardising by groups.
The first step is to define tiers or operational groups: a small number of categories that make up the well-known service tiers of an MSP, grouping together clients with similar needs.
We should have between three and five at most.
Once the groups have been defined, we build templates by client type on top of them: complete, validated technical policies that are ready to deploy.
And where the template falls short, we use parameters.
This gives us the best of both worlds. The tier logic is shared, while the specific value of the threshold, maintenance window or report recipient is taken from each client’s customised configuration.
This is how we achieve the balance between uniformity and flexibility, with the same backbone but different fine-tuning.
Incidentally, this is the difference between truly standardising services and accumulating handcrafted scripts that only the person who wrote them understands.
From that point onwards, the key word is discipline, which we implement by…

• Limiting exceptions. A one-off exception may be a necessary patch, fair enough, but twenty exceptions are really a poorly disguised new tier that should either be formalised or removed.
• Documenting common rules, so that knowledge lives in the system rather than in the unreliable neurons of the on-call technician whom the competition is trying to lure away.
• Periodically reviewing whether each client still fits its assigned group, because clients grow, change and sometimes even move into another group without warning.
• Not allowing the pressure to close a sale to destroy the standard operation. The dreaded phrase from the sales team, “We promised this client something special”, is the hole through which many perfectly designed MSP operations bleed out.

When done properly, however, this is what makes it possible to manage hundreds of clients with the same technical team without dying in the attempt.

The most common mistakes when designing policies by client type

IT is a Dungeons & Dragons dungeon: the path is full of traps and, when it comes to designing policies in an MSP, most of them arise from trying to be too clever.
Drawing on several decades of experience since Pandora was first created, the mistakes we have seen repeated most often are:

• Creating too many categories. If we have one tier for every client, we have recreated the same old chaos under a different name, which serves no purpose. Let us remember the discipline mentioned in the previous paragraph.
• Customising too early, before knowing whether that special requirement is genuine or simply a whim that will disappear within three months.
• Failing to review the operational profitability of each tier. Some groups require more work than they generate in revenue, and this needs to be identified before the wound drains the profit and loss account.
• Carrying forward inherited policies without proper judgement, such as configurations that nobody remembers the reason for, but everyone is afraid to touch.
• Mixing commercial requirements with operational ones. In other words, putting what we sold and what makes technical sense into the same box. Anyone with even minimal experience in IT knows that any resemblance between the sales utopia and technical reality is purely coincidental, so we need to manage that railway line very carefully, so that marketing does not derail our work with promises that are worth nothing, as the song goes.
• Failing to adapt alerts and automations to the context, allowing one tier to inherit the noise of another that bears no resemblance to it.

These missteps are closely related to those I already described when discussing mistakes when automating processes in an MSP, and they can almost always be summed up as a condition called excessive complexity disguised as rigour.

What changes when an MSP segments its policies with sound judgement

When an MSP’s multi-client operation stops treating everyone the same and begins grouping similar contexts together, the change becomes noticeable very quickly, and for the better, through specific effects such as:

• Greater operational consistency: because the same type of client receives the same type of service, regardless of who is managing the service during that shift.
• Less noise, because each tier generates alerts for what actually concerns it without creating unnecessary commotion around what is irrelevant.
• Better alignment between service and criticality: achieving a balance between egos and budgets, because the critical client receives what they are paying for, while the simpler client does not pay for what they do not need.
• Safer automation, by allowing the system to act autonomously only where the context permits it, rather than everywhere in the same way, configuring one thing while misconfiguring a hundred others.
• Better reuse of templates, which we can deploy for similar new clients with only a few adjustments.
• Real scalability, the kind where managing a thousand devices does not cost a thousand times more than managing one.

How Pandora FMS helps apply technical policies by client type

I do not know whether there is any corner of IT left where a suitable strategy, such as the one we have broken down, can be implemented in practice without professional tools. We stopped being craftspeople a long time ago, and this is where Pandora FMS and Pandora SIEM enable the tier model to become a practical reality instead of remaining in a PowerPoint presentation.
To begin with, they allow us to work with reusable templates and policies, the backbone of the strategy.
This means that we define the logic of each tier once and apply it to every client that fits within that group, automatically inheriting the configuration.
This group-based segmentation is native, so every contract secured and every tier remain organised and isolated, with client-specific parameterisation that makes it possible to adjust thresholds or maintenance windows without altering the structure or breaking the base template.
Above all of this sits centralised infrastructure monitoring, an eagle capable of spotting its prey from miles away through our Metaconsole. It is the long-awaited single pane of glass from which we can view the entire multi-client operation without jumping from one console to another, fulfilling our dreams of domination.
Alongside this, we have:

• Dashboards and reports adaptable to each client profile.
• Event and alert management fine-tuned by tier.
• A security layer with Pandora SIEM for those subject to compliance obligations, which a simpler client neither considers nor needs.

Ultimately, it provides exactly what is needed and has already been proven with real clients and operations, to detect incidents before they affect the client and reduce support hours without affecting the SLA.
Let us recap the essentials.
The reality is that an MSP does not scale by treating all its clients in the same way, even if saying so is not particularly diplomatic.
The key to genuine growth lies in grouping similar contexts together and applying repeatable policies that respect their real differences without multiplying exceptions.
Optimal standardisation means taming variety without eliminating it and without allowing it to drag us along like a runaway horse. It means moving from handcrafted chaos, where every client is a world of their own, a beautiful but impractical philosophy, to a small number of clear frameworks in which every client has a place.
That is how we will ascend to the Olympus of MSPs that survive without accumulating disasters until there are no hands left to resolve them.