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Semgrep VS ViewForge

Compare Semgrep VS ViewForge and see what are their differences

Semgrep

Semgrep is a fast, open-source, static analysis tool for finding bugs and enforcing code standards at editor, commit, and CI time.

Rating
0 reviews
Pricing
Open source
Alternatives Is it good?
ViewForge

Year/Make/Model fitment search for Shopify. 8 verticals, Smart Parse, and your data in Shopify metaobjects — not a vendor database. Free tier, Pro at $49.

Rating
0 reviews
Pricing
Freemium Free trial $19 / Monthly ($19/month Starter 1,000 products, custom templates, CSV import)
Alternatives Is it good?
PromptBrake
Automated AI security testing for agents, chatbots, and APIs. Find prompt injection, data leaks, and unsafe behavior before launch. sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, Semgrep seems to be more popular. It has been mentioned 26 times since March 2021.

social mentions
26 vs 0
Code Analysis popularity
100% vs 0%

Base details

Website, pricing, platforms and company facts side by side.

Semgrep
ViewForge
Website semgrep.dev normalview.pro
Pricing
Open source
Freemium Free trial $19 / Monthly ($19/month Starter 1,000 products, custom templates, CSV import) Official pricing
Platforms —
Shopify
Company — Startup from the United States · 1 - 9 employees · 2026
Listed in
Code Analysis Code Coverage Code Quality Developer Tools +1
eCommerce Tools Custom Search Fitment

About Semgrep and ViewForge

In their own words, as submitted to SaaSHub.

Semgrep
ViewForge

No description of Semgrep yet.

ViewForge is a Year Make Model (YMM) parts finder for Shopify. Shoppers pick their vehicle, machine or device from cascading dropdowns and see only the parts that fit. Fitment search works across eight verticals — auto, motorcycle, tractor, marine, power equipment, bicycle, printer and...

Read more about ViewForge

Features and specs

What each product offers, as listed by its team.

Semgrep 5 features
ViewForge 9 features
  • Easy to Use
    Semgrep offers a straightforward setup and simple syntax, making it easy for developers to start using it for static code analysis without extensive configuration.
  • Language Support
    It supports a wide range of programming languages, including popular ones like Python, JavaScript, Java, and more, making it versatile for different codebases.
  • Customizable Rules
    Users can create custom rules tailored to their specific codebase needs, allowing for more control and precision over code analysis.
  • Real-time Analysis
    Semgrep can be integrated into CI/CD pipelines, providing real-time feedback on code submissions and helping to catch issues early in the development process.
  • Open Source
    Being open source, it allows for community contributions and transparency, enabling users to understand and trust the tool more deeply.

Possible disadvantages

  • Performance Overhead
    Running extensive checks or using it on a large codebase might introduce a performance overhead, potentially slowing down development and analysis processes.
  • Learning Curve for Custom Rules
    While powerful, creating and fine-tuning custom rules can be challenging and require a good understanding of the tool and the code patterns to be detected.
  • Limited Advanced Features
    Compared to some commercial static analysis tools, Semgrep might lack certain advanced features such as deep data flow analysis or sophisticated vulnerability detection out-of-the-box.
  • False Positives
    Like many static analysis tools, Semgrep can produce false positives, requiring developers to manually review and filter out incorrect findings.
  • Community Support Dependency
    As an open-source platform, the availability of new features, bug fixes, and support heavily relies on the community, which may not always align with enterprise needs.
  • Fitment Information
    Cascading Year/Make/Model fitment search, up to four levels
  • Vertical integration nobody else has
    8 built-in vertical templates, plus custom templates on paid tiers
  • Shopify Metaobjects
    Fitment stored as native Shopify metaobjects — your data survives uninstall
  • Smart Data Processing
    Smart Parse: extract fitment from existing product titles and descriptions, with confidence scoring
  • CSV Import/Export
    CSV import with fuzzy matching and a coverage dashboard
  • ACES / PIES
    ACES / PIES import and NHTSA VIN decoding
  • My Garage
    Saved-vehicle garage, compatibility table, and product-page fit notice
  • Context Aware Fitment Search
    Collection-level fitment assignment
  • No Obligations
    Free tier with no expiry, up to 50 products

Videos

Walkthroughs and reviews on video.

Semgrep 3 videos + Add
ViewForge 1 video + Add

Semgrep: a lightweight static analysis tool for security consultant and hackers

More videos

  • - Using Semgrep and Jenkins for Static Code Analysis
  • - Workshop: Scaling your AppSec Program with Semgrep

ViewForge: YMM Search & Filter

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Semgrep
ViewForge
100% 100%
Code Analysis
0% 0%
0% 0%
eCommerce Tools
100% 100%
100% 100%
Developer Tools
0% 0%
0% 0%
Custom Search
100% 100%

Questions & Answers

As answered by people managing Semgrep and ViewForge.

What makes your product unique?

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.

Why should a person choose your product over its competitors?

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.

How would you describe the primary audience of your product?

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.

What's the story behind your product?

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.

Which are the primary technologies used for building your product?

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

User comments

Share your experience with using Semgrep and ViewForge. For example, how are they different and which one is better?

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Semgrep 26 mentions
ViewForge 0 mentions
  • Clean code didn't get less important in the AI age — it got more important
    For static analysis there's PHPStan for PHP and Mypy for Python. For formatting, Prettier and gofmt are the cheapest guardrail there Is, with zero excuse not to run one. For security, Semgrep Covers the same principle at higher stakes. - Source: dev.to / 20 days ago
  • Scaling Code Reviews in the Age of Generative AI
    Static Analysis & Semgrep: Do not rely on LLM alignment to write clean code. Enforce it. Write Semgrep rules to ban specific anti-patterns. If your standard dictates no default mutable values in Python methods, codify it. When the agent... - Source: dev.to / about 1 month ago
  • Silent AI Code Bugs: Passing Reviews, Failing in Production
    I have noticed this in myself and in teams I have worked with: as output volume rises, review time does not rise with it. If anything, it compresses. The productivity gains are real. So is the risk they paper over. Tools like Semgrep and... - Source: dev.to / about 2 months ago

View more

Tracking ViewForge since Aug 2026.

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