Rogue
AI guardrails, evaluations, and rogue agent red teaming platform
Qualifire is a solid pick for enterprises running LLM agents in production, especially those needing low-latency, cost-efficient guardrails. Rogue's red-teaming is a genuine differentiator for security-minded teams, but the lack of multimodal evaluation is a real gap. If your workloads are text-heavy, this is worth a look.
Verified 1d ago · liveness 71/100 · cite: rightaichoice.com/tools/rogue
- Enterprises deploying LLM agents in production with strict reliability requirements
- Developer teams building agentic AI systems that need to harden against security flaws
- AI reliability engineers looking for cost-efficient, fast evaluation at scale
- Compliance teams enforcing AI safety policies across multi-environment workflows
- Hobbyists looking for a free standalone chatbot without token limits
- Teams building no-code AI applications without engineering support
- Projects that require image or audio guardrails, as Qualifire supports text-only evaluation
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Skip Qualifire if you need multimodal (image/audio) guardrails, are a hobbyist needing unlimited free tokens, or cannot invest in technical integration and the $550/mo Pro tier.
Going past the 300K monthly tokens on Basic adds $29.90 per extra 1M tokens, which can add up quickly.
Qualifire's freemium model suits small teams experimenting (free tier with 3M tokens), while Pro at $550/mo fits serious production use. Compared to competitors like Lakera or Protect AI, Qualifire's SLM-judge approach may offer lower latency and cost, but the $550/mo entry is higher than some alternatives.
In short
Rogue — AI guardrails, evaluations, and rogue agent red teaming platform. Best for Enterprises deploying LLM agents in production with strict reliability requirements, Developer teams building agentic AI systems that need to harden against security flaws, AI reliability engineers looking for cost-efficient, fast evaluation at scale. Free to start; paid plans from $550/mo.
What's new in Rogue
Checked yesterdayAcross the latest 4 updates: 2 launches and 2 news mentions.
LLM hallucinations in production
Qualifire explains how AI gateways and guardrails detect and contain hallucinations at scale in production systems.
Qualifire is Now Available on LiteLLM
Qualifire integrates with LiteLLM, extending guardrails to another major LLM gateway.
Qualifire partners with Portkey
Partnership brings production-ready guardrails to Portkey LLM Gateway, enhancing enterprise AI safety.
Qualifire Joins AWS Marketplace
Qualifire now listed on AWS Marketplace across AI Security, Observability, and AI Agents categories.
What people actually say about Rogue — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
61 mentions across 4 sources (Hacker News, Stack Overflow, Lemmy, Tech Press) · researched Aug 19, 2026.
- +Real-time contextual guardrails with sub-20ms latency
- +SLM judges specialized for hallucination detection and prompt injection
- +Built-in red teaming with Rogue agent evaluator
- +Generous free tier of 3M tokens per month
- +On-premise and BYOC deployment for data-sensitive teams
- −No independent validation of claimed speed and cost savings
- −Text-only evaluation; no image or audio guardrails
- −Pricing tiers vague and hidden costs undocumented
- −Name collision with rogue AI incidents creates confusion
- −Requires intermediate skill level; not plug-and-play
- • No clear overage pricing beyond free tier
- • Enterprise on-prem setup likely requires consulting fees
- • Custom policy implementation may need engineering time
Viability Score
How well maintained and how widely used is Rogue? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Real-time contextual guardrails
- SLM judges for hallucination detection
- Prompt injection detection and prevention
- PII detection and masking
- Content moderation (hate speech, harassment, explicit)
- Syntax verification
- Rogue: AI agent evaluator and red team platform
- Observability dashboards
- Prompt Studio for experiments
- Custom policy creation
- Data curation tools
- Multi-environment support
- On-premise and BYOC deployment
- SaaS deployment
- Integration with LiteLLM and Portkey gateways
About Rogue
Qualifire is an AI control plane for the agentic era, combining real-time guardrails, continuous evaluation, and pre-production agentic testing. It targets teams deploying LLM-powered agents, RAG systems, and chatbots in production. The platform pairs purpose-built small language model (SLM) judges—each specialized for hallucination detection, prompt injection prevention, content moderation, and PII detection—with low latency and cost efficiency. Its SLM judges are claimed to be 99.6% faster and 97% cheaper than alternatives, making them practical for high-volume workloads. Rogue, Qualifire's AI agent evaluator and red team platform, proactively probes agents for security and reliability flaws. With recent incidents—like a student exposing a rogue AI hacking attempt and reports linking an Israeli startup to AI hacks at major labs—proactive agent testing is no longer optional. The platform also includes observability dashboards, prompt management (Prompt Studio), and data curation tools. Deployable via API, on-premise, or SaaS, Qualifire supports multi-environment workflows and integrates with gateways like LiteLLM and Portkey. The free tier includes 3 million tokens per month, while Pro and Enterprise tiers scale for production use. However, Qualifire currently supports text-only evaluation, so teams needing image or audio guardrails must look elsewhere. Its positioning against alternatives focuses on using dedicated SLMs that balance cost, latency, and accuracy, and on making red teaming a built-in feature rather than an add-on. For enterprises deploying LLMs at scale, this combination of speed, cost efficiency, and proactive security testing offers a strong value proposition.
Behind the Verdict
Qualifire positions itself as an AI control plane, offering real-time guardrails, evaluations, and the Rogue red teaming platform. The core value proposition is its set of specialized SLM judges that detect hallucinations, prompt injections, toxic content, and PII with low latency and cost. The claimed performance metrics (99.6% faster, 97% cheaper) are attention-grabbing, but you should verify these against your own workloads. Rogue is the standout feature: it proactively red-teams your agents, probing for security and reliability flaws before deployment. This is especially timely given recent high-profile incidents of rogue AI behavior. Qualifire's pricing is freemium, with a free tier that includes 3 million tokens per month, which is generous for experimentation. However, Pro jumps to $550/month, which may be steep for smaller teams. Enterprise offers custom pricing with BYOC/on-prem deployment. Integrations with LiteLLM and Portkey are valuable for teams already using those gateways, and the AWS Marketplace listing makes procurement easier. However, the platform is text-only; if you need image or audio guardrails, you'll need to look elsewhere. Overall, Qualifire is best for enterprises with serious reliability and security needs, but it's not a fit for hobbyists or teams needing multimodal support.
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Real-world workflow fit
Concrete scenarios for the personas Rogue actually fits — and what changes day-one when you adopt it.
You need to prevent hallucinations in a customer-facing RAG chatbot.
Outcome: Implement Qualifire's real-time guardrails with grounding checks, deploy Rogue for pre-deployment red teaming, and monitor via observability dashboards.
You need to harden an internal AI agent against prompt injection attacks.
Outcome: Use Rogue to run security tests, apply prompt injection detection guardrails, and integrate with LiteLLM for centralized policy enforcement.
Your team must ensure AI outputs comply with content policies and PII regulations.
Outcome: Configure custom policies for content moderation and PII masking, retain logs for 7 years on Enterprise, and use audit-ready observability.
Use Cases
- Prevent hallucinations in production LLMs by deploying real-time SLM judges for grounding checks.
- Red team your AI agent with Rogue to uncover security and reliability flaws before deployment.
- Enforce content moderation policies on chatbots and virtual assistants using context-aware guardrails.
- Monitor and debug agent actions with observability to ensure compliance and accuracy.
- Optimize token usage by routing all requests through Qualifire's policy engine to filter toxic inputs.
Models Under the Hood
as of 2026-09-01
Limitations
- Qualifire's Basic tier includes 300k monthly tokens and up to 3 protection rules with a 7-day log retention, while the Pro tier includes 1 billion monthly tokens, API access, and unlimited protection rules with 90-day log retention.
- Enterprise plans offer custom pricing, BYOC, on-premise, and custom web hooks.
- The platform supports deployment in your cloud, on-premise, or as SaaS.
- Free tier includes 3 million tokens per month and unlimited users.
as of 2026-09-02
Verification history
We have re-verified Rogue 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Rogue tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Solo developers or small teams experimenting with guardrails and evaluations, with under 3M tokens per month.
What this tier adds
Free entry point with 3M tokens, unlimited users, contextual guardrails, Rogue reports, Prompt Studio, observability, and community support.
Basic
$0/mo
Ideal for
Teams needing automated safety checks and content moderation for small-scale projects with limited volume.
What this tier adds
Adds automated safety checks, prompt injection detection, hate speech detection, syntax verification, PII detection, but with only 300K tokens and 3 protection rules.
Pro
$550/mo
Ideal for
Production teams with high token volumes and need for API access, custom policies, and deeper evaluations.
What this tier adds
Adds API access, custom policies, PII masking, Prompt Studio experiments, 1B tokens, unlimited rules, and 90-day log retention.
Enterprise
Custom
Ideal for
Large enterprises requiring BYOC/on-prem, unlimited tokens, custom web hooks, and dedicated support.
What this tier adds
Adds BYOC/on-prem, bespoke model, SAML SSO, dedicated CSM, 24/7 support, 7-year log retention, unlimited applications.
Where the pricing makes sense
The company stage and team size where Rogue's pricing actually pencils out — and where peers do it cheaper.
Qualifire's freemium model suits small teams experimenting (free tier with 3M tokens), while Pro at $550/mo fits serious production use. Compared to competitors like Lakera or Protect AI, Qualifire's SLM-judge approach may offer lower latency and cost, but the $550/mo entry is higher than some alternatives.
Setup time & first value
How long it actually takes to get something useful out of Rogue — broken out by persona, not the marketing-page minute.
For a developer familiar with APIs, you can integrate Qualifire within a day via API or gateway (LiteLLM/Portkey). Free tier allows immediate sandbox testing; full production setup with Rogue might take 1-2 weeks.
Switching to or from Rogue
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From custom in-house guardrails: Replace ad-hoc checks with Qualifire's SLM judges via API.
- →From other guardrail tools: Import policies and map them to Qualifire's custom policies.
- ↗To open-source guardrails: Export logs and policies for manual migration.
- ↗To another commercial guardrail vendor: Use documented APIs to extract data and recreate policies.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Featured Head-to-Head Comparisons
Rogue vs Audioeye
Rogue and AudioEye serve entirely different domains: Rogue is an AI control plane for LLM reliability, featuring real-time guardrails, evaluation, and red teaming—ideal for AI teams deploying LLMs in production. AudioEye is a web accessibility compliance platform automating ADA/WCAG remediation with human audits and legal support. Choose based on your core need: trustworthy AI or accessible web content.
Rogue vs Push Security
These tools solve fundamentally different problems. Push Security is for defending against browser-based attacks and controlling AI tool usage across any browser. Rogue is for ensuring LLM outputs are safe, accurate, and policy-compliant. Choose Push if your primary concern is security attacks like AiTM, session hijacking, or data leakage to AI tools. Choose Rogue if you're deploying LLMs in production and need guardrails against hallucination, prompt injection, and PII leakage.
Rogue vs Temporal Ai
Choose Temporal if your primary need is building fault-tolerant AI agents or workflows that survive crashes and require automatic retries, state persistence, and human-in-the-loop signals. Choose Rogue if your main concern is LLM safety, guardrails, and continuous evaluation to prevent hallucinations, prompt injections, and policy violations in production. They are complementary: you could use Rogue for guardrails on Temporal-executed agents.
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