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mlsql VS PromptShield

Compare mlsql VS PromptShield and see what are their differences

mlsql

Infer SQL queries from plain-text questions and table headers.

Rating
0 reviews
Promote on VibeVoted
Alternatives Is it good?
PromptShield

Stop credential leaks and block prompt injection attacks in local developer workflows. 100% offline, zero network telemetry.

Rating
0 reviews
Pricing
Open source Freemium $19 / One-off
Promote on VibeVoted
Alternatives Is it good?
Netumo
Ensure healthy website performance, uptime, and free from vulnerabilities. Automatic checks for SSL Certificates, domains and monitor issues with your websites all from one console and get instant notifications on any issues. 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, mlsql seems to be more popular. It has been mentioned 2 times since March 2021.

social mentions
2 vs 0
LMS popularity
100% vs 0%
alternatives listed
1 vs 1

Base details

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

mlsql
PromptShield
Website github.com lorthris.github.io
Pricing —
Open source Freemium $19 / One-off
Platforms —
Web Windows Linux Mac +1
Company — Startup from Australia · 1 - 9 employees · 2026
Listed in
Education LMS Online Learning Web App +1
AI Developer Tools Cyber Security

About mlsql and PromptShield

In their own words, as submitted to SaaSHub.

mlsql
PromptShield

No description of mlsql yet.

What is PromptShield? PromptShield is a lightweight, zero-telemetry cybersecurity suite designed for developers building with LLMs or sending code and logs into Claude, ChatGPT, and Gemini. It runs 100% locally on your machine with zero network calls, addressing two major AI security risks: 1....

Read more about PromptShield

Features and specs

What each product offers, as listed by its team.

mlsql 0 features
PromptShield 3 features

No features have been listed yet.

  • Secret Masking
    Redacts API keys, private keys, database URIs, passwords, and PII before prompts leave the device.
  • Prompt Injection Detection
    Blocks direct overrides, DAN and jailbreak personas, system prompt extraction, and hidden Unicode payloads in under 0.2 ms.
  • PII Detection
    Checks Australian TFNs and Medicare numbers, mobile numbers, credit cards, and other identity data before transmission.

Analysis

An editorial look at what each product does well and who it suits.

mlsql
PromptShield

Overall verdict

  • MLSQL is a solid open-source unified platform that blends SQL with machine learning, making data engineering and ML workflows accessible through a single, declarative language. It's a good choice for teams looking to streamline big data and ML pipelines without switching between multiple tools.

Why this product is good

  • It unifies data processing and machine learning under a single SQL-like syntax, lowering the learning curve for data teams.
  • Built on top of Apache Spark, it leverages a proven distributed computing engine for handling large-scale data.
  • Open-source and actively developed, allowing customization and community-driven improvements.
  • Supports the full ML lifecycle including data preprocessing, training, and deployment within one workflow.
  • Reduces the need to context-switch between separate ETL, analytics, and ML tools.

Recommended for

  • Data engineers and data scientists who prefer SQL-centric workflows
  • Teams working with big data on Apache Spark
  • Organizations wanting to unify ETL and machine learning pipelines
  • Companies seeking an open-source alternative to fragmented ML tooling
  • Analysts looking to build ML models without deep programming expertise

No analysis of PromptShield yet.

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
mlsql
PromptShield
100% 100%
LMS
0% 0%
0% 0%
AI
100% 100%
100% 100%
Education
0% 0%
0% 0%
Developer Tools
100% 100%

Questions & Answers

As answered by people managing mlsql and PromptShield.

Who are some of the biggest customers of your product?

PromptShield's answer:

  • No publicly disclosed customers yet; PromptShield is an early open-source project, and its public entry points are the GitHub repository and browser demo.

What makes your product unique?

PromptShield's answer:

PromptShield combines two protections in one local-first tool:

  • Outbound protection masks credentials, database URIs, and PII with deterministic pseudonyms, so context stays useful and values can be restored locally.
  • Inbound protection blocks prompt injection, DAN and jailbreak personas, system-prompt extraction, and hidden Unicode payloads.

It uses Python's standard library, zero telemetry, and zero external network calls. The code is open source and there is a live browser demo.

Why should a person choose your product over its competitors?

PromptShield's answer:

Choose PromptShield when protection should stay on the developer's machine. It combines outbound secret and PII sanitisation with inbound prompt-injection defence, uses Python's standard library, makes no external network calls, and has zero telemetry. Deterministic pseudonyms preserve useful context, while the Guard engine is designed for sub-millisecond checks. The project is open source and includes a live browser demo.

How would you describe the primary audience of your product?

PromptShield's answer:

PromptShield is for developers and small teams who send source code, logs, tickets, or customer text to Claude, ChatGPT, Gemini, and other LLMs. It also suits maintainers of LLM agents and RAG pipelines, security and privacy-conscious organisations, and people who use terminal AI tools. It is designed for users who want local, auditable controls without sending sensitive data to a third-party scanning service.

What's the story behind your product?

PromptShield's answer:

PromptShield was created for a practical problem in AI workflows: people can paste secrets and personal data into model prompts, while agent-facing prompts can carry injections and jailbreaks. The project addresses both directions with a local-first Python tool that sanitises outbound data and guards inbound prompts. It grew into an open-source project with a CLI, Git hook, clipboard workflow, CI integration, and browser sandbox. Zero telemetry and a standard-library-only implementation keep deployment simple.

Which are the primary technologies used for building your product?

PromptShield's answer:

The CLI and core engines use Python 3.8+ and the Python standard library. Detection combines secret signatures, pattern matching, entropy checks, Luhn credit-card validation, Australian TFN Mod 11 validation, and heuristics for prompt injection, jailbreaks, system-prompt extraction, and hidden Unicode. The project also includes Git hooks and GitHub Actions integration. The interactive demo runs client-side in the browser with JavaScript and WebAssembly.

User comments

Share your experience with using mlsql and PromptShield. 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.

mlsql 2 mentions
PromptShield 0 mentions
  • Download the files from Docker Container and locally edit them
    I am working on a project that requires me to take user input (as English) and return SQL queries. We will eventually be working with multiple datasets, which is why the valuenet over at https://github.com/paulfitz/mlsql ended up being... Source: over 4 years ago
  • Clone and edit Docker Projects hosted on Github
    I found this great resource here: https://github.com/paulfitz/mlsql. Source: almost 5 years ago

Tracking PromptShield since Sep 2026.

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