Software Alternatives & Startups

PromptShield VS cppq

Compare PromptShield VS cppq and see what are their differences

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
cppq

Simple, reliable & efficient distributed task queue for C++

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0 reviews
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.

Base details

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

PromptShield
cppq
Website lorthris.github.io github.com
Pricing
Open source Freemium $19 / One-off
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Platforms
Web Windows Linux Mac +1
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Company Startup from Australia · 1 - 9 employees · 2026 —
Listed in —

About PromptShield and cppq

In their own words, as submitted to SaaSHub.

PromptShield
cppq

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

No description of cppq yet.

Features and specs

What each product offers, as listed by its team.

PromptShield 3 features
cppq 5 features
  • 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.
  • Lightweight C++ implementation
    cppq is written in modern C++ and designed to be a minimal, efficient task queue library, making it a good fit for performance-sensitive applications where the overhead of scripting language runtimes is undesirable.
  • Redis-backed reliability
    Built on top of Redis as the message broker, cppq benefits from Redis's persistence, atomic operations, and wide adoption, giving the queue system durability and the ability to survive process restarts.
  • Inspired by proven design (asynq)
    The library's architecture mirrors the popular Go asynq task queue, so it inherits a well-thought-out API design for task enqueueing, retries, and worker processing patterns that have already been validated in production systems.
  • Native integration for C++ projects
    For teams already working in a C++ codebase, cppq allows adding background job processing without needing to introduce a separate service written in another language (like Go or Python), simplifying the technology stack.
  • Open source and inspectable
    Being open source on GitHub, developers can review the source code, modify it for custom needs, and contribute improvements, which offers transparency not available with closed-source queueing solutions.

Possible disadvantages

  • Limited maturity and battle-testing
    Compared to established task queue libraries like Sidekiq, Celery, or asynq itself, cppq is a newer and smaller project with less real-world production usage, which may translate into undiscovered bugs or edge cases.
  • Smaller community and ecosystem
    Because it has far fewer users and contributors than mainstream task queue systems, finding community support, third-party tutorials, or pre-built integrations is more difficult.
  • Documentation gaps
    As a smaller project, cppq may have less comprehensive documentation compared to major task queue frameworks, requiring developers to read source code directly to understand advanced features or edge-case behaviors.
  • C++ build and dependency complexity
    Using cppq requires managing C++ build toolchains and dependencies (such as a Redis client library), which can be more cumbersome than installing a package via pip or npm in other ecosystems.
  • Fewer built-in features and tooling
    cppq likely lacks the extensive ecosystem tooling of more mature queues, such as web-based monitoring dashboards, extensive metrics/observability integrations, or advanced scheduling features found in more established solutions.

Analysis

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

PromptShield
cppq

No analysis of PromptShield yet.

Overall verdict

  • cppq is a lightweight, Redis-backed distributed task queue for C++ inspired by Go's popular 'asynq' library. It's a good choice for developers who need a simple, dependency-light job queue system in C++ without the overhead of larger enterprise messaging systems, though it's a smaller, less battle-tested project compared to mature alternatives.

Why this product is good

  • Simple and lightweight design makes it easy to understand and integrate into existing C++ projects
  • Built on Redis, a well-established and reliable backend for queue storage, giving it a solid foundation
  • Inspired by asynq (a proven Go library), so the design patterns and task queue semantics are based on a well-thought-out model
  • Open source on GitHub, allowing developers to inspect, modify, and contribute to the code
  • Good fit for projects already using Redis, minimizing the need for additional infrastructure
  • Straightforward API for enqueueing and processing background jobs in C++ applications

Recommended for

  • C++ developers needing a background job/task queue without adopting a heavyweight message broker
  • Projects already using Redis as part of their infrastructure
  • Small to medium-sized applications requiring asynchronous task processing
  • Teams comfortable working with less mainstream or smaller open-source libraries and willing to contribute or debug issues themselves
  • Developers familiar with asynq (Go) looking for a similar pattern in C++
  • Prototyping or hobby projects where a minimal task queue solution is sufficient

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
PromptShield
cppq
100% 100%
AI
0% 0%
100% 100%
0% 0%
100% 100%
0% 0%

Questions & Answers

As answered by people managing PromptShield and cppq.

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

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