ActiveFence

Alice (formerly ActiveFence) red-teams AI models and agents before launch, then runs custom guardrails in production.

68/100UnverifiedCustom pricingContact Sales

Alice is one of the few vendors selling both the red-team evidence and the runtime guardrail that follows from it, and the two 2026 deployment paths — a LiteLLM guardrail provider and a Databricks Unity AI Gateway external policy — remove the rebuild-your-serving-stack objection. The Nemotron 3.5 Super VL post-training result and the Cyber RL Benchmark v1.0 show real research output, not slideware. Buy it when EU AI Act, NIST or OWASP exposure is real, or when you are shipping autonomous agents. Skip it if you need a cheap moderation API and nothing more — plug-in toxicity filters cost less.

Last checked 1d ago · cite: rightaichoice.com/tools/activefence

Best for

Frontier labs needing external, comparable red-teaming and indirect prompt injection benchmarks
Consumer platforms with multilingual abuse and AI-generated content exposure
Finance, insurance, healthcare and education teams facing EU AI Act, NIST or OWASP duties
Teams deploying autonomous agents that need injection defense plus runtime guardrails

Not ideal for

Startups wanting a cheap plug-in toxicity filter with no red-team work
Projects with no written AI policy or staff to own threshold tuning
Teams on fully custom serving stacks that can't adopt LiteLLM or a gateway

Visit Website

AdvancedAlready on LiteLLM: guardrail screening can be live in a day. On Databricks Unity AI Gateway: one attach in Unity Catalog, then policy mapping. Custom stacks: days to weeks of engineering wiring. Turning a written policy into a deployed classifier is quoted at about 24 hours — clock starts only once the policy text exists and a safety owner is named.API · WebAPI available3.5k viewsLast checked 1d ago

Pricing

Custom pricing

Contact Sales5 hidden costs

Learning curve

Advanced

Already on LiteLLM: guardrail screening can be live in a day. On Databricks Unity AI Gateway: one attach in Unity Catalog, then policy mapping. Custom stacks: days to weeks of engineering wiring. Turning a written policy into a deployed classifier is quoted at about 24 hours — clock starts only once the policy text exists and a safety owner is named.

Runs on

APIWeb

API available · 5 integrations

Who it's for

Frontier model lab safety leadPlatform engineering lead already on LiteLLMEnterprise architect standardizing on Databricks

Live sentiment

Is ActiveFence actually worth it?

We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.

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Real pros & cons from real users
Attributed quotes with receipts

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Skip it if

Skip Alice if you only need to filter toxic text with a cheap plug-in API and have no written AI policy, no trust-and-safety owner, and no regulatory deadline forcing you to document red-team evidence.

The 30-second take

Biggest gripe

Guardrails only pay off after someone tunes thresholds and policies — budget a part-time safety owner, because off-the-shelf default settings won't match your policy.

Price reality

Alice prices as an enterprise safety program, so budget expectations belong to the same bracket as other enterprise AI-security vendors that bundle red teaming plus runtime enforcement, and above standalone moderation APIs that charge per call. It fits organizations large enough to have a named safety or compliance owner. For a small team whose only need is toxity filtering, a per-call moderation API will cost materially less.

In short

ActiveFence — Alice (formerly ActiveFence) red-teams AI models and agents before launch, then runs custom guardrails in production. Best for Frontier labs needing external, comparable red-teaming and indirect prompt injection benchmarks, Consumer platforms with multilingual abuse and AI-generated content exposure, Finance, insurance, healthcare and education teams facing EU AI Act, NIST or OWASP duties. Contact Sales pricing.

No head-to-head comparison for ActiveFence yet — the alternatives below are the closest matches we list.

What people actually say about ActiveFence — 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.

10 mentions across 2 sources (Hacker News, YouTube) · researched Aug 23, 2026.

70% positive30% critical

Average across the 2 sources that answered — each source counts once, not each post.

Recurring strengths

+Recognized as a leading private model for AI safety on Hacker News
+Benchmarks show strong performance on accuracy, recall, and F1
+Comprehensive 120+ language and multi-modal threat coverage
+Continuous red-teaming and drift detection for production monitoring
+Policy alignment allows tailoring to regulatory and risk needs

Recurring frustrations

−Enterprise-only pricing deters smaller teams and startups
−Lack of public pricing requires sales contact for quotes
−Steep learning curve for non-experts in AI safety
−Limited independent community reviews available for assessment
−Potential for high false positive rates if policies not tuned

Patterns worth knowing

ActiveFence is a top-tier private AI safety model, often benchmarked against open-source alternatives.

Seen on Hacker News

Enterprise focus means high capability but limited accessibility for smaller users.

Seen on Hacker News, YouTube

Videos and interviews highlight the company's growth and vision but lack technical depth.

Seen on YouTube

Learning curve

advancedProductive in ~Weeks (sales onboarding, integration, and policy tuning)

Hidden costs people mention

• Implementation and integration services likely billed separately
• Potential overage charges for high-volume API calls
• Annual contracts may require long-term commitment

Viability Score

68/100

Unverified

How well maintained and how widely used is ActiveFence? 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

Recent activity

Traction

Site health

identity move

not measured

User sentiment

What the vendor publishes

Last calculated: October 2026

How we score →

Key Features

Pre-launch red teaming for models, apps and agents
Custom guardrails trained on your own written policies
Continuous red teaming in production to catch drift
Guardrail provider inside LiteLLM — no SDK or app changes
Databricks Unity AI Gateway external policy, attached via Unity Catalog
Policy document to deployed inline classifier in 24 hours
Rabbit Hole adversarial intelligence engine with billions of examples
Multi-modal adversarial detection across text and image
120+ language coverage for detection and guardrails
Prompt injection and jailbreak detection at the application layer
Agentic AI guardrails for autonomous and third-party agents
Pre-execution security scanning for AI agent skills
Over-refusal testing to measure false positives on real requests
Adaptive policy alignment tuned to regulatory needs and risk tolerance
RL environments and alignment data to train failures out of models

About ActiveFence

Contact SalesAdvancedAPI availableAPI · Web

Alice — the safety and security company formerly known as ActiveFence — sells AI safety to three kinds of buyer: frontier model labs, consumer platforms, and regulated enterprises in finance, insurance, healthcare and education. The product line is packaged as WonderSuite, with three buying units: red teaming before launch, custom guardrails trained on your own written policies, and continuous red teaming in production to catch drift. Underneath sits Rabbit Hole, an adversarial intelligence engine the company describes as hundreds of researchers contributing billions of stored attack examples, which frontier labs and enterprises then test their agents against. Two deployment paths matter for integration math. WonderFence now runs as a guardrail provider inside LiteLLM, so teams already routing model calls there can screen prompts, responses and tool calls against their own policies without SDK or application changes. As of October 2026 it also ships as a Databricks Unity AI Gateway external policy — attach once in Unity Catalog to screen every governed model call. Alice documents a workflow that turns a written policy document into a deployed inline classifier in 24 hours. The research arm, Alice Labs, publishes benchmarks: ENT-IPI Bench scores frontier models across 147 indirect prompt injection scenarios, and Cyber RL Benchmark v1.0 tested 10 frontier models on 12 real cybersecurity tasks with fresh code, live vulnerabilities and decoys. A separate collaboration with AI21 and Inflection AI post-trained NVIDIA's open Nemotron 3.5 Super VL to resist indirect prompt injection — 41 points more resilient, the company says, with capability intact. This is an enterprise safety program, not a drop-in moderation endpoint. You need a written AI policy and someone to own threshold tuning, or you will under-use the platform.

Behind the Verdict

Alice's structural advantage is that the evidence and the enforcement come from the same shop. Rabbit Hole, described as hundreds of researchers and billions of stored attack examples, feeds both the red-team exercises you run before launch and the guardrails you deploy afterwards. That matters because most safety stacks are stitched together from a testing vendor and a moderation API that were never calibrated against each other. The engineering story got concrete in 2026. Alice Guardrails running inside LiteLLM means prompt, response and tool-call screening without SDK changes — a real reduction in integration cost for teams already on that router. The Databricks Unity AI Gateway external policy goes further: attach once in Unity Catalog and every governed model call is screened. If your organization already standardizes model access through Databricks, that is close to zero-architecture-change enforcement. On research: Alice Labs publishes ENT-IPI Bench (147 indirect prompt injection scenarios) and Cyber RL Benchmark v1.0, which tested 10 frontier models on 12 real cybersecurity tasks using fresh code, live vulnerabilities and decoys, and concluded no model reliably covers a security analyst's range. The NVIDIA Nemotron 3.5 Super VL post-training work with AI21 is the most interesting item — 41 points more resilient against indirect prompt injection while keeping capability, which is the argument that safety tuning need not gut a model. Also worth crediting: Alice's red teaming tests over-refusal, not just break attempts — measuring how often agents wrongly block real customers. False positives are the failure mode buyers actually feel. Weaknesses are organizational, not technical. Adaptive policy alignment is a knob you have to operate. Off-the-shelf guardrails don't know your policies — that's the sales pitch, and it's also the workload. Teams without a trust-and-safety owner will buy the red-team report, file it, and never touch the drift-detection surfaces. Coverage is strongest on text and image; if your roadmap centers on audio or video generation, treat coverage as unproven until you test it. And a 24-hour policy-to-classifier claim is only credible if your policies are actually written down and current.

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Real-world workflow fit

Concrete scenarios for the personas ActiveFence actually fits — and what changes day-one when you adopt it.

Frontier model lab safety lead

You need external evidence that a new checkpoint resists indirect prompt injection before release, and a comparable score you can put in a model card.

Outcome: Run Alice's red teaming against ENT-IPI Bench (147 indirect prompt injection scenarios) and get a score you can compare against other frontier models, plus RL environments and alignment data to train the worst failures out.

Platform engineering lead already on LiteLLM

Your chatbot is live and you need policy enforcement on prompts, responses and tool calls without rebuilding the serving stack.

Outcome: Add Alice as the guardrail provider in LiteLLM; screening happens in the routing layer with no SDK or application changes.

Enterprise architect standardizing on Databricks

All model traffic is governed through Unity Catalog and you can't justify per-app code changes for safety screening.

Outcome: Attach WonderFence once as a Unity AI Gateway external policy; every governed model call gets screened from a single attach point, and you can review blocked traffic centrally.

Use Cases

Stress-test a new GenAI model for safety vulnerabilities before launch with automated red-teaming.
Drop Alice Guardrails into LiteLLM to screen prompts, responses and tool calls against your own policies without code changes.
Attach WonderFence once in Databricks Unity Catalog so every governed model call is screened automatically.
Continuously monitor production AI agents for drift and emerging risks after launch.
Align AI behavior with EU AI Act, NIST or OWASP obligations in finance, insurance, healthcare or education.
Measure over-refusal so your agent stops wrongly blocking legitimate customer requests.
Evaluate frontier models against an indirect prompt injection benchmark before shipping an agent.
Turn an internal policy document into a deployed inline classifier in about 24 hours.

Models Under the Hood

LLM-based classifiers

as of 2026-10-16

Limitations

Alice is built for enterprise safety programs, not lightweight moderation.
Expect integration effort to wire guardrails into your production stack, and the platform only pays off if someone owns policy tuning — adaptive policy alignment is a feature you have to operate.
Teams without a dedicated safety or trust-and-safety function will under-use the red-teaming and drift-detection surfaces.
The 24-hour policy-to-classifier workflow assumes your policies are written down; if they live in people's heads, add discovery time before anything deploys.
Published benchmarks (ENT-IPI, Cyber RL) cover text and cybersecurity tasks — treat audio and video generation coverage as untested until you run your own red team.

as of 2026-10-21

Verification history

We have re-verified ActiveFence 21 times since May 30, 2026. 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.

Oct 18, 2026 — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Oct 11, 2026 — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Sep 14, 2026 — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Aug 29, 2026 — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Aug 23, 2026 — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Aug 15, 2026 — 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 21 verification passes.

Free to cite with attribution — this page re-verifies continuously.

Hidden costs & gotchas

What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.

Guardrails only pay off after someone tunes thresholds and policies — budget a part-time safety owner, because off-the-shelf default settings won't match your policy.
Custom guardrails are trained on your written policies, so if yours are vague or out of date you'll pay in professional-services time to get them into deployable shape.
The 24-hour policy-to-classifier promise assumes the policy text exists; sloppy policy documents turn into a longer, more expensive engagement.
Red-team findings usually create remediation work — repeated rounds of testing after each fix add scope and cost beyond the initial run.
If your serving stack isn't LiteLLM or a gateway like Databricks Unity AI Gateway, budget engineering time for custom wiring before enforcement goes live.

Where the pricing makes sense

The company stage and team size where ActiveFence's pricing actually pencils out — and where peers do it cheaper.

Setup time & first value

How long it actually takes to get something useful out of ActiveFence — broken out by persona, not the marketing-page minute.

Switching to or from ActiveFence

How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.

Migrating in

→From a standalone moderation API: route model calls through LiteLLM and add Alice as the guardrail provider, keeping existing serving code unchanged.
→From manual red-team engagements: replace one-off PDF reports with repeatable Alice red teaming plus continuous production testing.
→From in-house regex/classifier filters: train custom guardrails on your own written policy instead of maintaining hand-written rules.
→From Databricks-governed stacks with no safety layer: attach WonderFence as a Unity AI Gateway external policy via Unity Catalog.

Migrating out

↗To a per-call moderation API: if you only need toxicity filtering, a plug-in endpoint is cheaper but drops red-team evidence and drift detection.
↗To a pure red-team consultancy: you keep the launch-time testing but lose the runtime guardrails that enforce what the testing found.
↗To a gateway-native guardrail: if your traffic never touches LiteLLM or Databricks Unity AI Gateway, you rebuild the enforcement path elsewhere.

Integrations

LiteLLMDatabricks Unity AI GatewayAWSAzureGoogle Cloud

Resources & Guides

Tutorials & Learning

YouTube returned 6 videos for “ActiveFence”, and we withheld 6: 6 could not be judged, because “ActiveFence” 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 ActiveFence.