So you need a new data analytics platform. There are dozens of options, the demos all look good, and everyone claims to do everything. Where do you even start?

We put together a list of five things worth checking before you commit.

What This Guide Covers

Read on for five data analytics platform evaluation dimensions, the questions that expose real capabilities versus marketing demos, and a realistic timeline for running a proper eval.

The 5 Dimensions Worth Evaluating

1. AI Features: Separating Hype from Value

Every platform now has "AI-powered" something, which tells buyers almost nothing. There's a canyon between a chatbot that spits out generic Python and an assistant that actually knows the data, like Zerve.

Good data analytics platforms are the ones that actually understand the schema. They remember how the team does things. They generate code that runs the first time without anyone having to fix imports or rename columns.

What actually separates useful from useless:

• Does it know the tables exist, specifically, by name?

• Can it build a complete pipeline or just isolated functions that need stitching together?

• Will it learn that the team always filters out test accounts, or will someone explain that every single time?

Think about it like hiring a junior analyst. A bad AI assistant is like a junior who just started yesterday. A good one is like someone who's been on the team for six months and knows where everything lives.

2. Collaboration: Beyond "Share a Link"

Most platforms bolt on a sharing function as an afterthought. Users get a URL and maybe some comments. That's file hosting with extra steps, not actual collaboration.

Real collaboration looks like two people editing the same notebook simultaneously without overwriting each other. It looks like version history that shows exactly who changed what line and when, so nobody's guessing who broke the query. And it means review workflows where teammates can leave comments on specific cells instead of pasting code into Slack and waiting for someone to reply "lgtm."

Read more: Block Run History for Real Collaboration

Quick comparison of what "collaboration" actually means:

If your team is still emailing screenshots of charts around for feedback, the collaboration features in your current setup are not working.

3. Production Deployment: Where Most Platforms Fall Apart

Here's a stat that should make buyers uncomfortable: something like 90% of analyses never make it to production. Not because they're bad, but because productionizing them is a completely different job that requires completely different skills. 

The gap between "working notebook" and "scheduled job that runs reliably" is where projects go to die. Some platforms pretend this gap doesn't exist while others actually solve it.

What production-ready actually requires:

• Turn a notebook into an API without rewriting it in Flask

• Schedule it to run at 6am and alert someone if it fails

• Lock down the dependencies so it doesn't break when someone upgrades pandas

• Logs that tell operators what went wrong, not just a generic error message

• A way to roll back when the new version turns out to be worse

When watching a demo, be sure you understand the process for deployment. If the answer involves "then your engineering team takes over," that's not deployment but rather a handoff dressed up in nicer language.

Read: Zerve Achieves ISO 27001 Certification for Data Security

4. Reproducibility: Eliminating "Works on My Machine"

Nothing erodes trust faster than an analysis that gives different results for different people. "Works on my machine" is the data science equivalent of "well it compiled," and it's just as useless.

The old way involved maintaining requirements.txt files and praying, then building Docker containers and praying harder, then watching everything drift out of sync anyway. The new way has the platform tracking dependencies automatically. Someone imports numpy, it notes the version, and when a teammate opens the notebook they get the exact same environment without any manual setup.

Test this during the eval: Build something with a few dependencies and hand it to a colleague. If they have to debug imports before they can even look at the work, that platform fails the reproducibility test and teams should move on.

5. Performance: Slow Platforms Change Behavior

When a query takes ten minutes, people stop running queries. They start eyeballing smaller samples instead of checking the full dataset, or they skip the validation step entirely because who has time. The end result is worse decisions, all because checking assumptions takes too long to be worth it.

Slow platforms change how teams work. When running a query takes too long, people start sampling instead of checking full datasets, skipping validation steps, and building workarounds that defeat the purpose of having a platform in the first place.

What to stress-test:

• Compute: Can users get a GPU when needed, and how much memory is available?

• Parallelization: Can a big job spread across workers, or is it stuck on one core forever?

• Query pushdown: Does it send computation to the warehouse, or pull everything local?

• Concurrency: What happens when 50 people are working at once, or 100?

Run actual data through the eval instead of the demo dataset, with real volume and your real team hitting it simultaneously. Benchmarks rarely reflect how a platform performs under actual working conditions.

Five Mistakes That Tank Platform Evaluations

Evaluating features instead of workflows: Feature checklists are useless because they don't tell anyone whether the actual experience of using those features is any good. Run actual work through the platform and pay attention to whether things flow naturally or require constant friction.

Ignoring deployment until it's too late: Everyone demos exploration well because that's the easy part. Few demo deployment well because that's where the hard engineering problems live. Make vendors show the complete path from notebook to production job, and if they can't do that clearly, that's the answer.

Picking what's already familiar: "We use Jupyter, so we'll just get hosted Jupyter" isn't a strategy. Maybe hosted Jupyter is right for the team, but nobody will know unless they evaluate what they actually need rather than what feels familiar.

Having one person do the eval: Solo evaluations miss everything about how teams work together. Get multiple people involved with different roles and different perspectives, because the SQL analyst will notice things the ML engineer won't.

Optimizing for price: A cheap platform that slows the team is more expensive than a pricey one that makes them faster. Do the math on productivity impact, not just licensing costs, because the hidden costs of slow tools add up quickly.

Making the Decision

Which dimensions matter most depends on the team. Heavy on exploration? Collaboration and AI features. Running production pipelines? Deployment and reproducibility. Scaling headcount? Performance and pricing.

Run real work through the finalists for at least a week. Not demos. Actual projects with actual data. The stuff that seems minor in a sales call tends to become infuriating after three months of daily use.

Talk to existing users before signing. Not the references the vendor gives out. Find them on LinkedIn and ask what they wish they'd known.

Zerve was built to score well on the dimensions that matter most: workflow coverage from exploration to deployment, real-time collaboration, and environment management that eliminates reproducibility issues. You can try Zerve for free if you want to see how it stacks up.