Mammoth vs Looker
A governed semantic layer, or an answer before the model exists.
Looker's premise is that metrics should be defined once, in version-controlled LookML, so the whole company computes revenue the same way. For a large organisation with the engineering capacity to maintain it, that is the right answer. The cost is time. Until the model is built, there is no dashboard. Mammoth starts from the data as it is, gets a correct dashboard out now, and applies governance to the pipeline instead of requiring a modelling layer up front.
Side by side
The same nine criteria we use on every comparison
Including the two where the answer doesn't favour us.
| Mammoth | Looker | |
|---|---|---|
| Time to first trustworthy dashboard | Around 15 minutes from raw data | Weeks. The semantic model comes first |
| Who can build one | Anyone on the team, in plain language | An analytics engineer writes LookML; others explore it |
| Data preparation | Same product, same canvas, re-runnable | Expected upstream, usually in dbt |
| Cost to share with 200 viewers | $0. Viewers are free on every plan | Per-user pricing, quoted per deployment |
| Keeping numbers current | Scheduled and event-driven refresh, built in | Queries the warehouse live. Freshness is upstream |
| Metric consistency at scale | Shared pipelines and checkpoints | Stronger. One governed definition, in code |
| Requires a warehouse | No. Files and databases work directly | Yes, in practice |
| Entry price | Free tier, then $199/mo | Annual contract, quoted |
| Compliance | SOC 2 Type II, ISO/IEC 27001:2022, HIPAA, GDPR | Varies by vendor, edition and region — check their trust page |
Looker pricing and capabilities as published August 2026. If something here is out of date, tell us and we'll fix it.
Fair's fair
What Looker does better
No comparison is worth reading if it pretends the other tool has no advantages. These are real.
- A single governed definition of every metric, in version control
- Excellent for large orgs where consistency outweighs speed
- Strong developer workflow: code review, git, environments
- Deep BigQuery and Google Cloud integration
So which should you pick?
Genuinely depends on the job. Here's how we'd decide.
Choose Looker if…
- You have analytics engineers to build and own a semantic layer
- Metric consistency across hundreds of users is the top priority
- Your warehouse is already clean and modelled
Choose Mammoth if…
- You need an answer before a modelling project can finish
- There's no analytics engineering resource to maintain LookML
- Data still needs cleaning and joining before it can be modelled
Test it against your own data.
Twenty minutes on the free plan will tell you more about the difference than this page can. Keep Looker running while you do.
- 21-day Pro trial
- No credit card
- Viewers always free