Rogue
Rogue by Qualifire red teams AI agents and enforces real-time LLM guardrails with purpose-built SLM judges.
Qualifire is worth a serious look when text agents are already in production and you want guardrails, evals, and Rogue's red teaming behind one API instead of three vendor contracts. The SLM-judge economics are the pitch that matters — the vendor claims 99.6% faster and 97% cheaper per check than frontier calls. Buy it for the judges and Rogue; skip it if you need multimodal guardrails or your footprint is too small to justify $550/mo Pro.
Verified 4d ago · liveness 71/100 · cite: rightaichoice.com/tools/rogue
- Enterprises running text-based LLM agents in production who need guardrails and evals on one platform
- AI reliability and platform engineers owning hallucination, injection, and PII leakage risk
- Security teams that want red teaming and reliability evidence inside the same eval platform
- Teams routing traffic through LiteLLM or Portkey who want guardrails at the gateway layer
- Teams needing image, audio, or video guardrails — evaluation scope here is text
- Token-heavy teams with a tight budget who can't clear the $550/mo Pro step
- Buyers who want no-code setup; production assumes engineering ownership of policies and API wiring
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Skip Qualifire if your guardrail needs are multimodal or if $550/mo Pro is out of reach and Basic's 3-rule, 300k-token, 7-day-log cap would block you anyway.
Extra tokens cost $29.90 per 1M on Basic versus $19.90 per 1M on Pro, so overage is nearly 50% more expensive on the cheaper plan.
Free-forever at 3M tokens/month with unlimited users is genuinely usable for evaluation and Rogue reports, and $0/mo Basic is a real entry point — but the 300k-token cap and 3-rule limit mean most production teams land on Pro at $550/mo, which is priced against enterprise eval suites rather than the $20–$100/mo dev-tool tier. Enterprise is custom-quoted for BYOC, on-prem, and unlimited tokens.
In short
Rogue — Rogue by Qualifire red teams AI agents and enforces real-time LLM guardrails with purpose-built SLM judges. Best for Enterprises running text-based LLM agents in production who need guardrails and evals on one platform, AI reliability and platform engineers owning hallucination, injection, and PII leakage risk, Security teams that want red teaming and reliability evidence inside the same eval platform. Free to start; paid plans from $550/mo.
What's new in Rogue
Checked 4 days agoAcross the latest 3 updates: 1 launch and 2 news mentions.
Qualifire partners with Portkey to bring guardrails to its LLM Gateway
Qualifire guardrails and evaluations now integrate with Portkey's LLM Gateway, targeting enterprise-scale AI deployments that already route traffic through a gateway.
Qualifire listed on AWS Marketplace
Qualifire became available on AWS Marketplace under the AI Security, Observability, and AI Agents categories, giving AWS buyers a procurement path.
Qualifire publishes Rogue Agent Evaluation Framework
The vendor outlined a framework for testing agent behavior, aimed at teams that need reliability evidence for autonomous agent deployments.
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.
Average across the 4 sources that answered — each source counts once, not each post.
- +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: October 2026
How we score →Key Features
- Real-time contextual guardrails on live LLM traffic
- Purpose-built SLM judges, one per failure mode
- Prompt injection detection and prevention
- Hallucination and grounding checks
- PII detection and PII masking (Pro tier)
- Content moderation: hate speech, harassment, explicit content
- Syntax verification on model output
- Content analysis and instruction check policies
- Rogue: AI agent evaluator and red team platform
- Rogue Agent Evaluation Framework for agent behavior testing
- Rogue reports surfaced inside Qualifire
- Continuous evaluation against custom policies
- Observability dashboards for every agent action
- Prompt Studio for experiments and prompt management
- Deploy as hosted SaaS, BYOC, or on-premise
About Rogue
Qualifire sells an AI control plane for teams running text-based LLM agents, RAG pipelines, and chatbots in production — the ones who need safety enforcement to happen while a request is in flight, not in a nightly review. Three products sit under one roof: contextual guardrails that inspect live traffic with context, continuous evaluation against your own policies, and Rogue, its AI agent evaluator and red team platform that probes agents for security and reliability flaws before users hit them. In October 2025 Qualifire published the Rogue Agent Evaluation Framework, a testing methodology aimed at teams that need reliability evidence before shipping autonomous agents. Detection runs on a set of small language model judges, each tuned to one failure mode: prompt injection, hallucination and grounding, PII detection and masking, hate speech, harassment, explicit content, and syntax verification. Qualifire states these judges run 99.6% faster and 97% cheaper than calling a frontier model per check. Around them you get observability dashboards for agent actions, Prompt Studio for experiments and prompt management, data curation, and multi-environment workflows that keep staging and production policies apart. Deployment is hosted SaaS, your own cloud (BYOC), or on-premise, and recent moves have widened the distribution: a November 2025 partnership with Portkey brings Qualifire guardrails to Portkey's LLM Gateway, and the product also listed on AWS Marketplace under AI Security, Observability, and AI Agents. LiteLLM was already a supported gateway path. Scope is text. Teams needing image, audio, or video guardrails are better served elsewhere, and the pricing structure assumes you either stay inside the Free tier's 3 million monthly tokens or move to a paid plan. Against standalone eval vendors or gateway-native moderation, the argument is consolidation: guardrails, evals, and red teaming behind one API.
Behind the Verdict
The case for Qualifire is consolidation. Most teams in production with a text agent end up buying a moderation API, an eval tool, and something for red teaming, then wiring them together and paying three bills. Qualifire puts all three behind one control plane, with the guardrail layer running on small purpose-built judges rather than a frontier model call per check. If your monthly LLM spend on safety checks is the thing your finance lead keeps asking about, that architectural difference is the reason to look. We'd reach for it when the agent is live and the risk is specific: prompt injection, hallucination, PII leaking into responses. Those are named failure modes with named judges, and tuning a judge to one job is a saner design than asking a general model to catch everything. Rogue is the piece worth watching. The Rogue Agent Evaluation Framework published in October 2025 is a real attempt at a repeatable testing methodology for agent behavior — the kind of evidence a security review will ask for before an autonomous agent ships. Red teaming living inside the same platform as your eval runs saves the export-import dance between tools. The pricing layout is Free at $0 for life with 3 million tokens per month, Basic at $0 with 300k monthly tokens and 3 protection rules, then a jump to Pro at $550/mo with 1 billion tokens and unlimited rules. Anyone past a prototype lands on Pro, and that cliff is the honest caveat. Overages run $29.90 per additional million tokens on Basic and $19.90 on Pro, so token-heavy teams should model the real bill, not the sticker. Pass on this if you need image, audio, or video guardrails — scope is text. Pass if you want no-code setup; production assumes engineering ownership of policies and API wiring. And if you only need one or two
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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 route all production LLM traffic through the Qualifire API, turn on prompt injection, PII detection, and grounding checks, and give each of staging and production its own environment and rule set.
Outcome: Policy violations get caught in the request path instead of in a support ticket three days later, and staging experiments can't loosen production's rules.
You point Rogue at the agent before release, run red team probes for security and reliability flaws, and read the Rogue report inside Qualifire.
Outcome: You ship with a documented list of failure modes and the policies that address them, rather than discovering them from user complaints.
You keep routing through LiteLLM or Portkey and attach Qualifire guardrails at the gateway layer instead of rewriting application calls.
Outcome: Guardrails and evaluations land without a rewrite of the application's model-call path.
Use Cases
- Run SLM grounding checks inline so hallucinated answers get caught before they reach a customer.
- Red team an agent with Rogue before launch to surface security and reliability flaws while they're still cheap to fix.
- Enforce context-aware content moderation on a customer-facing chatbot without adding a frontier-model call per message.
- Watch every agent action in observability dashboards to trace where a policy violation actually originated.
- Keep a staging environment on looser policies while production runs strict enforcement, using multi-environment support.
- Filter toxic or injected input at the policy engine before it consumes downstream tokens.
- Mask PII in model output so regulated data doesn't land in logs or user-facing responses.
Models Under the Hood
as of 2026-10-17
Limitations
- Evaluation scope is text, so teams needing image, audio, or video guardrails need a different vendor.
- Free and Basic are community-support only — no email or Slack until Pro, and no 24/7 support until Enterprise.
- On the free-forever tier you get 3M tokens per month with Rogue reports in Qualifire; Basic is automated safety checks only, capped at 300k monthly tokens, 3 protection rules, 1 environment, and 7-day log retention, with extra tokens at $29.90 per 1M.
- Pro at $550/mo lifts that to API access, custom policies, PII masking, Prompt Studio experiments, 1B monthly tokens, unlimited rules, unlimited environments, and 90-day logs, with extra tokens at $19.90 per 1M.
- Enterprise is custom-priced and adds BYOC or on-prem, unlimited tokens, a bespoke white-glove SLM, SAML SSO, and 7-year log retention.
- Production use assumes engineering ownership of policies and API integration.
as of 2026-10-04
Verification history
We have re-verified Rogue 9 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 9 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 builders and small teams evaluating whether Qualifire's judges catch their agent's failure modes before committing budget.
What this tier adds
Free entry point: 3 million tokens per month, unlimited users, contextual guardrails, Rogue reports in Qualifire, Prompt Studio, and observability, with community support.
Basic
$0/mo
Ideal for
Prototypes and internal tools running low token volume that only need the standard automated safety checks.
What this tier adds
Free starting tier for automated safety checks: prompt injection, hate speech, harassment, explicit content, syntax verification, and PII detection, but capped at 300k monthly tokens, 3 protection rules, 1 environment, and 7-day logs.
Pro
$550/mo
Ideal for
Production teams with an agent in front of real users who need API access, custom policies, and PII masking.
What this tier adds
Adds API access, custom policies (content analysis, instruction check, grounding check, hallucination check), PII masking, and Prompt Studio experiments, and raises the ceiling to 1 billion monthly tokens, unlimited protection rules, unlimited environments, and 90-day logs, with
Enterprise
Custom
Ideal for
Regulated or data-residency-constrained organizations that must run guardrails in their own cloud or on-premise.
What this tier adds
Adds BYOC cloud or on-premise hosting, unlimited tokens, a bespoke white-glove SLM, custom web hooks, SAML SSO, 10 hours of AI consulting, 24/7 support, and 7-year log retention at custom pricing.
Where the pricing makes sense
The company stage and team size where Rogue's pricing actually pencils out — and where peers do it cheaper.
Free-forever at 3M tokens/month with unlimited users is genuinely usable for evaluation and Rogue reports, and $0/mo Basic is a real entry point — but the 300k-token cap and 3-rule limit mean most production teams land on Pro at $550/mo, which is priced against enterprise eval suites rather than the $20–$100/mo dev-tool tier. Enterprise is custom-quoted for BYOC, on-prem, and unlimited tokens.
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.
With an existing gateway (LiteLLM or Portkey) in place, guardrails can land at the gateway layer in an afternoon. Starting from raw application calls, budget a few days for API wiring plus policy authoring, since custom policies are self-serve only from Pro. Free and Basic can be started without a sales conversation.
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 a per-check frontier-model evaluator: move detection to Qualifire's per-failure-mode SLM judges and keep your policy definitions as custom policies on Pro.
- →From LiteLLM or Portkey with no guardrails attached: add Qualifire at the gateway layer rather than rewriting model calls.
- →From a standalone red team engagement: replace the one-off with Rogue and rerun the same framework against each release.
- ↗To a frontier-model evaluation suite: rebuild your Qualifire custom policies as prompt-based checks, accepting higher per-check latency and cost.
- ↗To a multimodal guardrail vendor: keep Qualifire for text policy enforcement and split image or audio checks to the other platform.
- ↗To an in-house rules engine: export the policy logic and reimplement, losing the contextual SLM judges' context handling.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Rogue”, and we withheld 6: 6 could not be judged, because “Rogue” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Rogue.
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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