Start With Requirements, Not Vendors

The laboratory informatics market is large and growing, estimated at roughly USD 3.9 billion in 2024 and projected to reach USD 5.2 billion by 2030, with dozens of vendors and substantial feature overlap. That scale is precisely why a structured requirements process matters: in a crowded category, almost every platform can show you something impressive, and without a documented baseline, it is very easy to be sold features you will never use while missing the one capability that actually matters for your operation.

Before contacting a single vendor, document the following:

• Your workflows as they actually are. Map current sample flows, test processes, results handling, and reporting, including the manual steps and known pain points. This is your requirements baseline.
• The systems and instruments it must integrate with. List every analyser, existing system, and data source the platform will need to connect to. Integration scope drives both cost and feasibility.
• Your compliance obligations. Establish which regulations apply (GxP, 21 CFR Part 11, ISO 17025, CLIA) and which workflows fall under them. This eliminates non-compliant platforms before you waste time on demos.
• Your must-haves versus nice-to-haves. Separate the non-negotiable requirements from the features that would be useful but are not essential for day one. This distinction is what keeps scope and budget under control.
• Who the users are. Scientists, analysts, QA, and IT all interact with the platform differently. Requirements that ignore the daily user produce systems that are bought but not adopted.

It is also worth deciding, before you start, whether you are buying a LIMS, an electronic lab notebook, a combined platform, or a broader informatics stack. The ELN-versus-LIMS distinction matters here because buying the wrong category is more costly than choosing the wrong vendor within the right one.

In a crowded category, every platform can demo something impressive. Requirements written down in advance are what keep the decision grounded in what your lab actually needs.

What Evaluation Criteria Actually Matter?

Once requirements are defined, evaluate platforms against weighted criteria rather than feature lists. Weighting matters: integration depth might be decisive for one lab and secondary for another. The table below sets out the criteria that reliably separate platforms, what to assess under each, and rough weighting guidance to adapt to your priorities.

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Build these into a simple scored matrix, weight the criteria before you see any demos, and score each platform against the same scale. This converts a subjective decision, easily swayed by presentation quality, into a defensible one. When platforms claim AI capability, apply the scrutiny set out in AI-Powered LIMS: ask what the feature actually does, what data it needs, and how its performance is monitored, rather than rewarding the word itself.

Why Instrument Integration Makes or Breaks the Decision

Of all the evaluation criteria, instrument integration deserves special attention because it is where informatics projects most often quietly fail. A platform that cannot connect cleanly to your analysers forces manual data entry, which reintroduces the transcription errors the system was supposed to eliminate and caps the value of every analytics feature on top. Integration is also the area where vendor demos are least representative, because they run on the vendor's curated equipment, not yours.

Questions that reveal real integration capability:

• Does the platform connect to your specific instruments, by make and model, and is that connection bidirectional or read-only?
• Who builds and maintains the integration, the vendor, a third party, or your team, and what does each option cost over time?
• How are instrument software updates handled? An integration that breaks every time an analyser is updated is a recurring liability.
• Does the integration capture full metadata and raw data, or only final results? This determines what analytics and AI features will later be possible.
• Can the vendor demonstrate the integration on your instruments or a close equivalent, ideally in a proof of concept rather than a slide?

Integration depth also shapes downstream data quality. A platform that captures clean, complete, well-structured data at the point of instrument connection is doing the groundwork that good data quality and any future AI feature depend on. Treat integration not as a technical detail but as a core decision criterion.

How Do You Assess the Vendor, Not Just the Software?

You are not only buying a product; you are entering a multi-year relationship with a vendor whose stability, support, and roadmap will shape your operations long after go-live. Strong software from a weak or poorly aligned vendor is a risky purchase, and the factors that matter here rarely appear in a feature comparison.

What to evaluate about the vendor:

• Financial stability and longevity. Will the vendor still be supporting and developing the platform in ten years? Acquisitions and discontinued products are real risks in this market.
• Install base and references. Ask for reference customers in labs of similar size, workflow type, and regulatory environment, then actually talk to them about implementation reality, not just satisfaction.
• Product roadmap. Where is the platform heading, particularly on integration, cloud, and AI? A roadmap that aligns with your direction reduces future friction.
• Support model and cost. Understand exactly what support covers, what response times are guaranteed, and what support and maintenance cost after the first year, when initial goodwill fades.
• Implementation track record. Who performs the implementation, the vendor or a partner, and what is their record on timelines and budgets for labs like yours?

A few vendor-side red flags worth weighing heavily:

• Reluctance to provide reference customers in a comparable lab type.
• Capability that lives mostly on the roadmap rather than in the current release.
• Vague answers on integration ownership, support cost after year one, or what the quoted price excludes.
• Pressure to sign quickly, or pricing that expires suspiciously fast.

Implementation and Change Management

The contract is the midpoint of the journey, not the end. Most of the value, and most of the risk, lives in implementation and adoption. A capable platform that the team never fully adopts is a more common failure than a platform that cannot do the job.

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Plan deliberately for the parts that derail projects:

• Data migration. Legacy data is almost always messier than expected, and the cost of cleaning and mapping it is routinely underestimated. Budget for it explicitly and use it as an opportunity to improve data quality, not just move it.
• Validation. In regulated labs, IQ, OQ, and PQ, plus computer system validation, add significant time and effort. Factor this into both the timeline and the comparison between vendors, since validation support varies.
• Configuration versus customisation. Configuration within the platform is sustainable; heavy custom code is expensive to build and may not survive vendor upgrades. Favour platforms that meet your needs through configuration.
• Training and adoption. Treat the human rollout as its own workstream. Phase it, train for confidence through hands-on practice, and support adoption actively after go-live when old habits reassert themselves.
• Phased rollout. Where possible, prove the platform on one workflow or site before extending it, building the organisational confidence that makes wider rollout smoother.

Before committing, run a proof of concept on your own data, workflows, and instruments rather than the vendor's demonstration set. It is the single most reliable test of whether a platform will work in your environment, and it surfaces the integration and data quality issues that otherwise appear only after the contract is signed.

How Do You Navigate Pricing and Negotiation?

Lab informatics pricing is rarely a single number, and the headline figure is usually the least informative part. Understanding the pricing models and what drives cost within each is what lets you compare platforms fairly and negotiate from a position of knowledge.

Practical negotiation guidance:

• Compare the total cost of ownership over the platform's expected life, not on the licence price. Ask each vendor to itemise implementation, validation, training, support, and any per-integration or per-module fees.
• Get explicit clarity on what the quoted figure excludes. Hidden implementation and integration costs are the most common budget surprise.
• Negotiate support terms and future pricing, not just the initial fee. Year-one discounts mean little if renewal and support costs escalate.
• Where AI or premium modules are involved, confirm whether they are included or priced separately, and whether model updates carry additional cost or validation burden.
• Use your shortlist as leverage. A genuine, comparable alternative is the strongest negotiating position you have.

This article was produced under Lab Manager’s AI Editorial Guidelines