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AI Security Gateway VS cppq

Compare AI Security Gateway VS cppq and see what are their differences

AI Security Gateway

AI Security Gateway (AISG) is an AI firewall, LLM proxy & MCP Gateway that redacts 30+ PII types (text+vision), blocks prompt injections, smart-routes 600+ models, & enforces budgets —2-line integration, loop detection, zero data retention.

Rating
0 reviews
Pricing
Open source Freemium Free trial
Alternatives Is it good?
cppq

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

Rating
0 reviews
Alternatives Is it good?
AgentClara
AgentClara is a UK AI receptionist that answers calls, books clients, and handles follow-ups 24/7 for service businesses and agencies. Free AI agents for website chat, SMS, social media and email included. Set up online with a free number in minutes 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, AI Security Gateway seems to be more popular. It has been mentioned 1 time since March 2021.

social mentions
1 vs 0
AI popularity
100% vs 0%

Base details

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

AI Security Gateway
cppq
Website aisecuritygateway.ai github.com
Pricing
Open source Freemium Free trial Official pricing
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Company Startup from the United States · 2026 —
Listed in
AI Security & Privacy LLM Security LLM Gateway Data Leakage Prevention Data Protection Data Privacy Developer Tools AI Tools API Tools +7
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About AI Security Gateway and cppq

In their own words, as submitted to SaaSHub.

AI Security Gateway
cppq

AI Security Gateway is the control layer between your application and any LLM provider. Change your base_url. Two lines of code. Your prompts are secured before they leave your infrastructure. Core Problems It Solves Sensitive DLP in text and vision prompts Prompt injection and jailbreak attacks...

Read more about AI Security Gateway

No description of cppq yet.

Features and specs

What each product offers, as listed by its team.

AI Security Gateway 19 features
cppq 5 features
  • PII Detection & Redaction
    28+ entity types (emails, SSN, API keys, etc.)
  • Multi-Modal DLP
    Text + OCR-based image scanning
  • Prompt Injection Detection
    Pattern + heuristic-based filtering
  • Stateless Processing
    No prompt/response storage
  • Smart Model Routing
    Cheapest/fastest model selection
  • Multi-Provider Support
    OpenAI, Claude, Gemini, Groq, MistralAI, Together.AI, DeepInfra
  • Budget Enforcement
    Block or downgrade based on cost thresholds
  • OpenAI-Compatible API
    Drop-in replacement
  • BYOK (Bring Your Own Key)
    Use your own provider credentials
  • Managed Wallet Mode
    Prepaid credits for simplified billing
  • Low Latency
    <50ms for text DLP
  • Audit Logs (Metadata Only)
    Violation types, counts, latency
  • Project-Based Isolation
    Per-key policies and configs
  • Recursive Loop Protection
    SHA-256 fingerprint detection kills agent retry loops
  • Webhook Notifications
    HMAC-signed real-time alerts to Slack/PagerDuty/SIEM
  • EU AI Act Compliance Logging
    Hash-chained tamper-evident audit records
  • Python SDK
    pip install aisg — typed exceptions
  • Chain Verification API
    Verify tamper-evidence of audit trail
  • JSONL Audit Export
    Export compliance records for regulators
  • 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.

AI Security Gateway
cppq

Overall verdict

  • Based on its positioning as an AI security gateway solution, it appears to be a specialized tool aimed at helping organizations secure their AI/LLM deployments, though independent verification of specific claims and performance is recommended before adoption.

Why this product is good

  • Focuses on the growing need for AI-specific security controls as organizations adopt LLMs and AI systems
  • Likely provides centralized monitoring and policy enforcement for AI traffic and interactions
  • May help address emerging risks like prompt injection, data leakage, and unauthorized AI usage
  • Positions itself in a specialized niche addressing modern AI infrastructure security gaps

Recommended for

  • Organizations deploying multiple AI models or LLMs that need centralized security oversight
  • Enterprises concerned about data leakage through AI/LLM interactions
  • Security teams looking to implement guardrails for AI usage across their organization
  • Companies needing to demonstrate AI governance and compliance for regulatory purposes
  • Businesses integrating third-party AI APIs who want an additional security layer

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

Videos

Walkthroughs and reviews on video.

AI Security Gateway 1 video + Add
cppq 0 videos + Add

AI Security Gateway - The AI Firewall. Stop Data Leaks & Slash LLM Costs in 3 Minutes

No cppq videos yet. You could help us improve this page by suggesting one.

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
AI Security Gateway
cppq
100% 100%
AI
0% 0%
100% 100%
LLM Gateway
0% 0%
100% 100%
Security & Privacy
0% 0%
100% 100%
Developer Tools
0% 0%

Questions & Answers

As answered by people managing AI Security Gateway and cppq.

Why should a person choose your product over its competitors?

AI Security Gateway's answer

AI Security Gateway offers a unique combination that no competitor matches:

  • vs. Portkey/Helicone: They observe and log. We actively redact PII, kill agent loops, and push webhook alerts — before your data leaves your infrastructure.
  • vs. OpenRouter: They route traffic. We govern it — with PII redaction, budget enforcement, loop protection, and EU AI Act compliant audit trails.
  • vs. Vercel AI Gateway: They passively log. We scan, redact, block, and alert in real time.
  • vs. LiteLLM: We add a built-in firewall, webhook notifications, compliance logging, and managed hosting.

Plus: EU AI Act Article 12 enforcement begins August 2, 2026. AISG ships compliant audit trails today — hash-chained, tamper-evident, with JSONL export and chain verification. Most competitors have no compliance story at all.

How would you describe the primary audience of your product?

AI Security Gateway's answer

Software Architects, CISOs, and Engineering Managers at organizations moving LLM implementations from "experimentation" to "production." We serve teams that need to satisfy strict security and compliance reviews while simultaneously reducing the high cost of proprietary AI models. AI Security gateway is designed for developers, startups, and enterprises building applications with LLMs. It is especially valuable for teams moving from experimentation to production, where concerns around data security, compliance, and cost control become critical.

Which are the primary technologies used for building your product?

AI Security Gateway's answer

AI Security Gateway is built using a cloud-native architecture with Python-based API services, containerized deployments (ECS/EKS), and AWS infrastructure components such as NoSQL Databases and CDN. It leverages industry-standard NLP and security frameworks for detection and integrates with multiple LLM provider APIs through a unified, controlled and optimized gateway layer.

Who are some of the biggest customers of your product?

AI Security Gateway's answer

AI Security Gateway is currently being adopted by early-stage startups, independent developers, and AI-focused teams experimenting with production use cases. As the platform evolves, it is designed to support enterprise-grade deployments with advanced governance and compliance requirements.

What's the story behind your product?

AI Security Gateway's answer

AI Security Gateway (AISG) was built after observing a recurring pattern across teams adopting LLMs: developers were unknowingly sending sensitive data such as API keys, logs, and customer information directly to external AI providers, while also struggling with unpredictable costs. Existing solutions either focused on routing or security, but not both. AISG was created as a unified layer to solve these real-world problems — enabling safe, controlled, and cost-efficient AI adoption. AISG was born out of a realization by its founder, a veteran software architect, that 'Shadow AI' was becoming the biggest security risk in the modern stack. After seeing users accidentally paste company secrets into AI prompts, we engineered a stateless, high-performance firewall to allow innovation to continue without compromising data sovereignty.

What makes your product unique?

AI Security Gateway's answer

Unlike standard AI gateways that act as passive proxies, AI Security Gateway is an active security and compliance engine. We combine:

  1. Multi-modal Vision DLP — OCR scans images for PII before they reach any provider
  2. Recursive Loop Protection — the only API proxy that detects and kills agent retry loops at the gateway layer, across all frameworks (LangChain, CrewAI, AutoGPT)
  3. Real-time Webhook Alerts — HMAC-SHA256 signed push notifications to your SOC workflow. Most AI proxies require polling; AISG pushes events the moment they happen.
  4. EU AI Act Compliance — hash-chained, tamper-evident audit records that satisfy Article 12 requirements. SHA-256 fingerprints only — no raw content stored. Privacy by design.
  5. Financial Intelligence — treats LLM tokens like a trading desk, routing each request to the cheapest provider in real time. All in a single stateless proxy with 2-line integration

User comments

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Social recommendations and mentions

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

AI Security Gateway 1 mention
cppq 0 mentions
  • I Built an Open-Source AI Firewall Because Every LLM App Leaks Data
    I built AI Security Gateway to fix this. It's an open-source proxy that sits between your app and any LLM provider. Every prompt passes through a security layer before it reaches the model. - Source: dev.to / 6 months ago

Tracking cppq since Nov 2022.

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