Looking for an Xverum alternative?
Compare what you get, beyond the quote.
A lower quote is a real advantage, and we won't pretend otherwise. But the price of B2B data is only settled once you've used it: what it costs to clean, what it costs to refresh, and what it costs when your users act on a profile that's six months out of date. We've been in the data business for 15 years, and that's the part we built our dataset around.
Some teams come to us to replace Xverum, while others run both side by side before they decide. Either way, here's what you get:
An update file every hour, on every plan
Every 60 minutes we send the records that changed, whatever your plan. Pull them in hourly, weekly, or monthly. The speed is the same for everyone.
Any profile, checked on demand
Need one record current right now, without waiting for the next update? Our live scan API fetches the latest version of a public profile on request.
A team you can actually reach
Enterprise customers get a shared support channel with our data team, including our founders. Questions get answered in a thread by the people who built the dataset.
What you're actually paying for.
Some comparison tables list MixRank without job postings data, or with numbers we outgrew years ago. Wherever you saw the comparison, here are our current numbers:
- people profiles with history back to 2013
- 1.2B+
- people-profile updates every month
- ~150M
- companies going back to 2013
- 120M+
- job postings, with history back to 2021
- 1.8B+
Where a lower quote tends to cost more later.
Choosing the lower quote is a reasonable call. Most teams make it for good reasons, and it usually works fine for a while. The trouble tends to show up later, in one of these three places:
The cheaper quote won, and the cleanup became your job.
The dataset arrived on time, and then your engineers spent the next few sprints matching records, removing duplicates, and fixing fields before anyone could use it. That time never showed up on the invoice, but it came out of your roadmap.
Your users want to see how hiring changed, not just what's open today.
A hiring-signal feature or a workforce product is only as good as the history behind it. Today's open roles tell you what a company wants now. Years of postings tell you where it's heading.
One profile has to be right today.
A user searches your product for a candidate, and the profile has to be right today. You can't wait for the next refresh cycle to find out whether that person still works there.
Where the savings actually show up.
The cheapest dataset isn't the one with the lowest quote. It's the one your team spends the least time fixing. This is where that difference shows up:
Fewer quarters lost to cleanup.
One team came to us after spending a quarter wrestling with problems in the data they'd been testing. They wanted to see whether a different dataset would hold up better on the same work.
More people and companies to work with.
Xverum lists 750M people profiles and 54M companies. We hold 1.2B+ people profiles and 120M+ companies, so more of your searches come back with someone in them.
One person, one ID.
We link every profile URL we've seen for a person to one ID. The same person stops showing up three times, and your engineers start with records they can use.
When Xverum is the smarter buy
For some teams Xverum is the better one. If any of these describes you, we'd tell you the same thing on a call.
Pick Xverum if:
You need location data. Xverum offers 320M place records worldwide. We don't sell that.
You want job-change predictions out of the box. Their Next Move Signal scores how likely someone is to change jobs, ready to use in sourcing and lead lists.
You want plain-English search today. Ask Xverum lets your team or your AI agent describe who it's looking for and get ranked results, and you can start free.
The upfront price decides it. Xverum often comes in lower. If budget is the main constraint, that's a fair reason to choose them.
If none of these is your deciding factor, keep reading.
The number that isn't on the quote
Most teams comparing us with Xverum are pricing the contract. The better number to price is what it costs to keep your data usable for a year: how much of it your users can actually act on, and how much work your team does before anyone touches it.
1.2B+
750M
120M+
54M
An update file every hour with the records that changed, on every plan. Every people record shows when we last checked it.
Data refreshed as fast as every 24 hours.
1.8B+ tracked, so you can see how a company's hiring changed over time.
9M active job postings, refreshed daily. No published history.
Our live scan API fetches the latest version of a public profile on request.
Plain-English search across their datasets, through an API or an MCP server.
People, companies, job postings, web technographics, mobile apps and SDKs, and developer data.
People, companies, job postings, and 320M place records, plus web search APIs.
API, bulk feeds with hourly update files, a hosted database, or delivery into Snowflake, BigQuery, or Redshift.
Datasets, API, or MCP.
When you compare quotes, ask each vendor for the price per record your team can actually use, after duplicates and dead profiles are removed. That's the number that shows up in your budget a year from now.
Put both quotes to work before you pick one.
Run these on your own records before you sign anything, with us or anyone else. Our data team will pull our side of the sample for you. Each takes an afternoon.
The usable-record test
Ask both vendors for the same 1,000 people and check whose current title and company are right. Divide each quote by the records that pass. Now you're comparing the real price.
The job-change test
Pick 200 people you know changed jobs this year. Send them to both vendors and check which dataset shows their new title and company.
The history test
Pick 20 companies you follow closely. Ask each vendor for every job posting it has on them over the last few years, and see whose record shows how their hiring changed.
Bring us your Xverum sample. We'll go through it line by line.
Send us the specific fields where the two datasets disagree, and we'll tell you why, including the cases where we're the one who's wrong.