Azimutt VS ViewForge

Azimutt's answer

Azimutt is mainly targeted at developers working with databases, allowing them to easily explore and understand them by either importing the schema or connecting to a live instance.

As it's quite easy to use, we have seen other profile such as product owners, engineering managers and even CFOs using it to better understand the product they build or extract meaningful data on their own ^^

ViewForge's answer:

Shopify merchants whose customers need to know whether a part fits before they will buy it — and who do not have an engineer on staff to build that themselves.

Concretely: auto and truck parts retailers, powersports and motorcycle dealers, tractor and agricultural parts sellers, marine and outboard suppliers, small-engine and power equipment stores, bicycle and e-bike component shops, printer supply merchants, and electronics accessory sellers.

Catalog sizes run from a few dozen products on the free tier up into the tens of thousands; it is running in production on a catalog of roughly 40,000 SKUs. The common thread is not the industry — it is that "does this fit my thing" is the question deciding the sale.

Azimutt's answer

Early 2021 I joined Doctolib, a health startup very successful in France, and discovered their big Ruby on Rails monolith backed by a large PostgreSQL database with more than 700 business tables (more then 1300 in total). As an architect I worked with several teams and needed to understand their models but neither Ruby, Rails or the structure.sql were very helpful for such a big app. So I looked for a tool but they all failed with such a large database, so after a few month and tens of tools tested, I decided to build my own: Azimutt. Now it has evolved a lot and we are still very active to enable new usages every months. I believe it's a solid product and quite unique ❤️

ViewForge's answer:

ViewForge came out of agency work. Normal View was building for a parts retailer running roughly 12,000 SKUs who needed fitment search, and every app we evaluated stored the merchant's compatibility data in the vendor's own database.

That is a strange trade when you look at it directly. Fitment data is genuinely expensive to produce — it is weeks of work — and the merchant would not own the result. It would belong to whichever app happened to be installed that year.

Shopify metaobjects made a different answer possible: write fitment as native structured data inside the merchant's own store. The theme reads it, the Storefront API queries it, Admin GraphQL exports it, and it is still there after an uninstall. That decision is what the rest of the app is built around.

Everything else came from real catalogs rather than a roadmap. Eight verticals exist because a tractor catalog is not Year/Make/Model. Smart Parse exists because that retailer had already written fitment into 12,000 product titles, and nobody was ever going to retype them.

Azimutt's answer

From development languages, Azimutt is built with Elm/TypeScript for the frontend, Elixir/Phoenix for the backend and PostgreSQL/S3 as storage.

ViewForge's answer:

• TypeScript
• Shopify metaobjects, as the fitment data store
• Shopify theme app extensions for the storefront components: fitment search, saved-vehicle garage, compatibility table, product-page fit notice
• Shopify Storefront API, for querying fitment from the theme
• Shopify Admin GraphQL API, for writing and exporting fitment records
• NHTSA vehicle database, for VIN decoding
• ACES and PIES XML parsing, for automotive catalog import

Azimutt's answer

It's the only ERD able to handle databases with many tables (>1000) nicely thanks to unique features:

• layouts to see only the relevant tables (not all are useful for everyone)
• smart search everywhere

It's also very unique in the sense it's made to explore and understand real world databases, from development to production with larges features:

• database design with an intuitive DSL
• database documentation, on any table, column or layout (markdown text and tags)
• database analysis to make sure best practices are in place
• innovative data exploration

Thousands of developers already love it, give it a try, we have several samples you can try right away!

ViewForge's answer:

Three things. (1) Data ownership: ViewForge writes fitment as Shopify metaobjects native to your store — most competitors store fitment in their own database. (2) 8 verticals out of the box: auto, motorcycle, tractor, marine, power equipment, bicycle, printer, electronics — most competitors are automotive-only. (3) Smart Parse: extract fitment automatically from your existing product titles and descriptions instead of re-typing everything.

Azimutt's answer

Azimutt is the all-in-one app to explore real world databases. If you look for very specialized features some competitors may be more suited, but if you want a versatile app to explore and understand your database, we believe no competitor come close to us.

• if you have more than 50 tables, there is no match, you should be amazed by Azimutt features built for large databases
• if you want to mix data exploration and schema exploration, it's also very unique
• if you care about open source, visit our GitHub

ViewForge's answer:

Data ownership. Fitment lives in your Shopify metaobjects, so uninstalling does not take your compatibility data with it. Convermax, EasySearch and PartFinder all keep it in their own databases, and getting it back depends on their export tooling on the day you cancel.

Cost at the low end. The search widget, the compatibility table on the product page and the saved-vehicle garage are all on the free tier, up to 50 products, with no expiry. EasySearch puts the table and the garage behind its $75/month Premium plan. Convermax starts at $250/month.

Automotive and non-automotive coverage. Eight built-in templates, and fully custom templates from $19/month, for catalogs that do not decompose into Year/Make/Model at all.

Azimutt's answer

Azimutt is used at Doctolib (3000 people company) and some other french scale ups I can't disclose yet.