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F5 AI Guardrails
Your AI apps answer customers all day. Every prompt is a way in, every reply a way out — F5 AI Guardrails checks every prompt and every reply against your policies — in a public cloud, a private cloud, or on-premises and air-gapped beside the model itself.
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How it’s rated
Full scoreboard ↓Quick answer
The F5 platform family
This page covers F5 AI Guardrails — runtime policy enforcement for AI models, apps and agents. The rest:
Quick facts
30-second orientation- Product
- Runtime policy enforcement for the inputs and outputs of AI models, apps and agents
- Maker
- F5, Inc., Seattle; NASDAQ: FFIV; Chairman, President and CEO François Locoh-Donou
- Heritage
- Built on F5’s 2025 acquisition of CalypsoAI, closed 26 September 2025 for $145.2M in cash
- Price
- Quote-only; f5.com prints no price, no licensing unit and no marketplace rate for it
- Stops
- Prompt injection, jailbreaks, data leakage and harmful output, checked as traffic flows
- Models
- Model-agnostic by F5’s account, so it is not tied to one LLM provider
- Runs in
- AWS, Azure, Google Cloud, a private cloud, or on-premises, air-gapped sites included
- Platform
- One of four parts of the F5 AI Security Platform, launched in June 2026
- India
- Self-hosting puts the inspection in your own Indian site; F5 R&D has run in Hyderabad since 2019
- In India via
- TechBag — AI app inventory, quote in INR with GST, a pilot on one model endpoint
Understand AI guardrails before you buy them
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
What are runtime AI guardrails?
A policy check on every prompt and reply, so an AI app cannot be steered into leaking data or misbehaving.
A system prompt that says “refuse” vs runtime guardrails — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | A system prompt that says “refuse” | F5 AI Guardrails |
|---|---|---|
| Defending the system prompt | One more line telling the model to refuse | Injection and jailbreaks checked before the model |
| Data in replies | Spotted after a user screenshots it | Leaks blocked before the answer is returned |
| Where checks run | A vendor’s cloud API, outside your network | Public cloud, private cloud or air-gapped site |
| Changing the model | Rewrite the filters for the new LLM | Same policies, model-agnostic by design |
| Learning from tests | A red-team report filed and forgotten | Findings pushed into policy via AI Remediate |
| What it is NOT | — | Staff AI-use control, a WAF, or a priced SKU |
The cheapest test is one endpoint in monitor mode: log what Guardrails would block, time each check, then decide.
The five pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Runtime
Each request bound for a model and each answer coming back is tested against your policies as it passes, so an injection attempt or a leaking reply can be stopped mid-flight.
Placement
F5 lists AWS, Azure and Google Cloud, private clouds, and on-premises estates, air-gapped ones included, so the checks can sit beside the model instead of on a vendor network.
Coverage
Policies apply to a model, to the app built on it, or to an agent acting on a user’s behalf; F5 calls the product model-agnostic, so the LLM underneath can change without a rebuild.
Feedback
Weaknesses that F5 AI Red Team finds can be pushed into Guardrails as policy; F5 AI Remediate, unveiled at AppWorld 2026, is the step that turns a finding into a rule.
Policy checks on every prompt and reply — run in a public cloud, a private cloud, or an air-gapped rack beside the model.
Six capabilities. Inspect, enforce, operate.
F5 AI Guardrails enforces your AI policies at runtime, wherever the model runs.
Hostile instructions caught
Text written to override a system prompt or smuggle in new orders is flagged before the model ever acts on it.
Safety rules kept intact
Role-play tricks and other attempts to talk a model out of its own limits are treated as attacks, not as questions.
Sensitive data held back
Confidential records in a prompt or a draft reply can be blocked, so a chatbot does not hand one customer’s data to another.
Bad answers never shipped
Replies that break your content rules are stopped on the way out, before a customer or an agent downstream acts on them.
One policy set, many LLMs
The same guardrails cover models, apps and agents whatever LLM sits underneath, so a model swap does not reset your controls.
Test findings become rules
Attacks that F5 AI Red Team lands can be turned into Guardrails policy, the loop F5 presented as AI Remediate in 2026.
Watch F5 AI Guardrails in action
F5 on what runtime guardrails do, screening model inputs and outputs on Google Cloud, and its 2026 Gartner Emerging Market Quadrant placement.
F5 named a Market Shaper in the Gartner® Emerging Market Quadrant™ for AI Application Security | AI
F5 on its 2026 Gartner Emerging Market Quadrant placement; note this is not a Magic Quadrant.
What Are AI Guardrails? How to Secure AI Models, Apps & Agents | AI
An F5 explainer on what runtime guardrails do for models, apps and agents (January 2026).
Secure AI model inputs and outputs against attacks, tampering, or leaks with F5 on Google Cloud | AI
F5 on screening model inputs and outputs for workloads running on Google Cloud (October 2025).
Want a live, India-context walkthrough for your environment?
Book a guided demo →Every AI app is a new way in. AI Guardrails checks each prompt and reply where the model runs.
Here’s what genuinely sets it apart — and exactly where it stops.
Runs where the model runs, even offline
Many AI guardrails are a cloud API, so every prompt leaves your network to be judged. F5 lists AWS, Azure, Google Cloud, private clouds and on-premises sites, air-gapped ones included. A bank or a ministry can therefore keep both the model and the checks inside a data centre it controls.
Testing and blocking in one loop
Guardrails is sold beside F5 AI Red Team, which attacks your AI apps on purpose. What the red team gets through can be pushed into Guardrails as policy, and F5 AI Remediate, shown at AppWorld 2026, automates that hand-off, so the rules track the attacks that actually worked against you.
Specialist roots, platform owner
The product comes from F5’s 2025 acquisition of CalypsoAI, which closed on 26 September 2025 for $145.2M in cash, per F5’s 10-Q. KuppingerCole named F5 a Leader for GenAI Defense in December 2025, and Gartner’s 2026 Emerging Market Quadrant for AI Application Security calls F5 a Market Shaper.
Where it stops
There is no price, licensing unit or trial on f5.com, and F5 publishes no latency, payload or throughput figures. The only customer named on the page is an anonymous global bank. It guards AI you build and run; employees pasting data into outside AI tools is the job of F5 Workforce AI Security.
The numbers behind the platform
What your F5 AI Guardrails rollout looks like
Map the AI you run
List every model, AI app and agent in production, where each runs, and which ones touch customer or employee data.
Choose the placement
Decide whether the guardrail runs in a public cloud, a private cloud or an on-site rack beside the model, air-gapped or not.
Guard one endpoint
Put one live AI app behind Guardrails in monitor mode and record what it would have blocked and how long each check took.
Attack it, then tighten
Run F5 AI Red Team or your own testers against the pilot, push what got through into policy, and switch to blocking.
Extend to agents
Roll the tuned policy set to the remaining apps and agents, and agree who owns false positives and policy changes.
The review scoreboard
Modelled on Gartner Peer Insights structure. *Counts and breakdowns are illustrative pending verified review collection.
Quick poll — what’s driving your evaluation?
Talk to an advisor“Our underwriting assistant runs on a model in our own rack, and the guardrail sits in the same rack. Nothing goes to a vendor cloud.”
“The red team broke our HR bot with a role-play prompt on day two. A week later that exact pattern was a blocking rule.”
“We swapped the model behind our support agent mid-pilot and kept the same policy set. That alone saved a re-test cycle.”
“Output checks stopped a draft reply that quoted another patient’s visit notes. The pilot was justified on that one catch.”
“Getting a price took three calls and there was no latency figure until we measured our own. Budget time for the pilot.”
“First-pass policies flagged plain questions about refund rules as attacks. Two rounds of tuning brought false blocks down.”
Where everyone sits — the grids
Analyst firms bury this view behind paywalls, and G2 retired its Grid. So here’s TechBag’s synthesis of the AI runtime security market — tap any vendor to see why it sits where it does.
TechBag AI Runtime Security Grid
Execution strength vs product vision — the classic market map, minus the paywall.
Quote-only; CalypsoAI heritage, F5-owned since 2025.
Deployment Freedom × Threat Breadth
The grid nobody publishes — how many places the checks can run, air-gapped included, vs how many kinds of AI attack they name.
Three clouds, private cloud, on-prem and air-gapped; four threat classes.
Positions are TechBag’s illustrative synthesis of public review-platform data and vendor documentation — not a reproduction of any analyst graphic. Verify before relying on it.
F5 AI Guardrails vs the AI runtime security field
Against Palo Alto Prisma AIRS, Akamai Firewall for AI, Lakera Guard, Amazon Bedrock Guardrails and Kong AI Gateway — on deployment, air-gap, threats, price, limits and India.
| Dimension | F5 AI Guardrails | Palo Alto Prisma AIRS | Akamai Firewall for AI | Lakera Guard (Check Point) | Amazon Bedrock Guardrails | Kong AI Gateway |
|---|---|---|---|---|---|---|
| What it is | Runtime AI guardrails | AI security platform | Firewall for LLM apps | Guard API, Check Point | AWS-managed safeguards | AI gateway with guards |
| Deployment | Clouds, private, on-prem | Network or API mode | Edge, REST or proxy | SaaS or container | Inside AWS only | Konnect for 2.x |
| On-prem and air-gap | Air-gapped supported | Not documented | Not documented | Offline on Enterprise | No on-prem mode | Self-hosted V1 only |
| Threats covered | Four named classes | Injection to URLs | Injection to AI DoS | Prompts, PII, agents | Six policy types | PII and prompt guards |
| Model support | Model-agnostic | Your apps and agents | Any LLM-based app | Model-agnostic | Any model by API | Many providers, MCP |
| Pricing model | Quote; unit unstated | Tokens per month | Quote after demo | Not captured | Per 1,000 text units | Per model per month |
| Published entry price | Not published | No public rate | Not published | Not captured | $0.15 per 1K units | $100/model/month |
| Included vs add-on | Red Team sold apart | SCM, DLP, logging | Discovery is separate | SaaS has dashboards | Policies billed apart | AI plugins: add-on |
| Published limits | None published | 2 MB sync, 5 MB async | None published | Sub-50 ms claim | 1,000-character units | 5 models, 10M calls |
| Integrations | Red Team, Remediate | SDK, Strata stack | Edge, REST, proxy | SIEM, MCP, tools | Bedrock, SageMaker | OTel and cloud hooks |
| India data location | Your own site | India region, 2025 | Not documented | Singapore nearest | Mumbai Region | Konnect IN geo |
| Support | Set in the quote | Not on product pages | Per contract | Not published | Paid AWS plan | Email on Plus |
| Lock-in and exit | F5 policy format | Region-bound keys | Edge mode needs Akamai | Lakera API calls | Policies live in AWS | Konnect-tied 2.x |
| Best fit | Closed-network AI | Palo Alto shops | Akamai-fronted apps | Agent-heavy builders | Builders on AWS | Gateway-first teams |
Which approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose F5 AI Guardrails if…
- ✓Your models run in your own data centre or an air-gapped network, and the guardrail must run there too
- ✓You want red-team findings turned into blocking policy, using F5 AI Red Team and AI Remediate together
- ✓You expect to change LLMs and want one model-agnostic policy set across models, apps and agents
Compare alternatives if…
- ✓You need a rate card before a sales call — Amazon Bedrock Guardrails and Kong AI Gateway both publish one
- ✓You want a vendor-hosted India region — Prisma AIRS added one in August 2025 and Bedrock Guardrails runs in Mumbai
- ✓Your AI apps already sit behind Akamai or Palo Alto, where their own AI controls slot into tools you run
Do not expect…
- ✓A public price, a stated licensing unit or a self-serve trial for AI Guardrails
- ✓Published latency or payload limits before your own pilot measures them
- ✓Control over staff pasting data into outside AI tools — F5 sells Workforce AI Security for that
TechBag has no AI security guide yet, so F5 AI Guardrails sits outside the category guides. Browse all products to compare it with the rest of the catalogue. →
What does hand-checking AI traffic cost you?
Drag the sliders (AI apps, agents and model endpoints; security-engineer hour cost). Estimates model time spent hand-reviewing prompts and replies, writing ad-hoc filters and re-testing after model changes at an assumed 1.5 hours per endpoint a year, with 70% of it removed by central runtime policy. Both figures are assumptions. Illustrative.
Loaded cost = salary + overheads per productive hour. Illustrative only — your TechBag quote models your actual environment and modules.
Three ways to consume it
Not published: F5 sells AI Guardrails on quote only, with no licensing unit, trial or marketplace rate on f5.com, and AI Red Team is a separate product. TechBag maps your models, apps and agents first, then gets the quote itemised in INR with GST.
AI Guardrails
Best for teams guarding AI they run
- Quote-only; licensing unit not published
- Public cloud, private cloud or on-premises
- Air-gapped deployment supported
+ Platform add-ons
Best for a broader rollout
- Scoped to your estate
- Add-on modules as needed
- Phased, right-sized deployment
Guardrails + AI Red Team
Best for teams that test, then block
- Two separate F5 products, both quoted
- Red Team findings feed Guardrails policy
- AI Remediate automates the hand-off
Buy it for less — TechBag pricing beats list
Whatever the list prices above, TechBag negotiates a significantly better deal — with GST-compliant INR invoicing and local support. Ask us for your discounted quote.
Get an India-ready quote
Tell us your requirements and current tools — we’ll model it against what you spend today.
The 8 questions to ask every vendor
Take this into your next vendor call — including ours.
Which models, AI apps and agents are live, who owns each, and which of them can see regulated or customer data?
Must checks run on-premises or air-gapped, or is a public or private cloud acceptable for each workload?
Which matter most to you: prompt injection, jailbreaks, data leakage or harmful output? Test each one in the pilot.
F5 publishes no figures, so what delay per check will your app accept, and did the pilot measure it under load?
Will you buy F5 AI Red Team as well, and who signs off before a finding becomes a blocking rule?
Do your agents call tools or act for users? Ask F5 to show agent coverage working on one of your own flows.
What does the quote count — tokens, requests, apps or instances — and what happens when traffic doubles?
Is the quote itemised in INR with GST, with the support hours, renewal terms and any platform bundle stated?
Questions buyers ask
Ready to evaluate F5 AI Guardrails?
Map the models, apps and agents you run first, or let a TechBag advisor scope a monitor-mode pilot on one live AI endpoint.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.