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Akamai Firewall for AI
Your chatbot answers customers all day. One crafted sentence shouldn’t make it leak — Akamai Firewall for AI reads every prompt before your model does and every answer before your users do, on Akamai’s edge, through a REST API or in a reverse proxy, whichever model you run.
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How it’s rated
Full scoreboard ↓Quick answer
The Akamai platform family
This page covers Akamai Firewall for AI — protection for the AI apps you publish. The rest:
Quick facts
30-second orientation- Product
- An LLM firewall that screens AI prompts going in and model responses coming out
- Maker
- Akamai Technologies, Cambridge, Massachusetts; NASDAQ: AKAM; CEO Tom Leighton
- Status
- Announced 29 April 2025 and shown at RSA Conference; no separate GA date is stated
- Price
- Quote-only; Akamai offers a demo form, with no published price, trial or licensing unit
- Licence
- Its own entry in Akamai’s cybersecurity catalogue, bought apart from App & API Protector
- Deploys
- Three paths: on Akamai’s edge, through a REST API, or as a reverse proxy
- Covers
- Prompt injection, jailbreaks, scraping, model theft, toxic output, leaks; OWASP LLM Top 10
- Models
- Model-agnostic by Akamai’s account; models may sit on-prem, in a cloud or in a hybrid
- India
- No Indian inspection location is documented; Akamai’s Bengaluru facility dates from 2018
- In India via
- TechBag — AI endpoint inventory, quote in INR with GST, pilot on one LLM app
Understand LLM firewalls before you buy one
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
What is an LLM firewall?
A filter that reads the words of an AI conversation, not just the request, and blocks attacks going in and harm coming out.
A system prompt and a WAF vs an LLM firewall — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | A system prompt and a WAF | Akamai Firewall for AI |
|---|---|---|
| Stopping a jailbreak | A system prompt that says “do not” | Each prompt read for evasion before the model |
| Leaks in answers | Found when a customer complains | Responses checked for sensitive data first |
| Scrapers and floods | Rate limits tuned by hand | Scraping and AI DoS named as targets |
| Where the check runs | Code copied into every app | Edge, REST API or reverse proxy |
| Audit language | Ad-hoc test cases | Mapped to the OWASP LLM Top 10 |
| What it is NOT | — | A WAF, staff AI control, or a priced SKU |
The cheapest test is one public app in monitor mode for two weeks: replay known jailbreak and data-leak prompts and count what it catches.
The five pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Edge
For an AI app already delivered through Akamai, the firewall can screen requests and responses on the edge servers in front of it, which Akamai says keeps added delay low.
REST API
The app sends the prompt, or the model’s draft answer, to a REST endpoint and acts on the result, so the check works even where traffic never crosses Akamai’s delivery network.
Reverse proxy
Akamai lists a reverse-proxy mode as the third path: AI traffic is pointed at the proxy, which inspects it in line before it reaches the model or returns to the user.
Rules
Inbound queries are tested for guardrail evasion and scraping, outbound answers for leaked data and harmful text, using rules Akamai says adapt as new AI attacks appear.
Three ways in, one set of rules — prompts and answers checked on the edge, by API call or through a proxy.
Nine capabilities. Inbound, outbound, operate.
Akamai Firewall for AI inspects both sides of an AI conversation, wherever the model runs.
Prompt attacks stopped
Phrasing built to get round a model’s safety or privacy instructions, including jailbreaks, is caught before the model reads it.
Extraction at volume
Large-scale scraping and unauthorised queries that aim to copy a model’s knowledge or behaviour are flagged and blocked.
Costly floods of prompts
Akamai names AI-specific denial of service among its targets, such as prompt floods aimed at one AI endpoint.
Secrets kept out of answers
Responses are checked for sensitive data, such as a customer’s account number, before the user ever sees them.
Toxic or biased output
Answers that are toxic, biased or misleading are filtered, so a chatbot does not speak for your brand in a way you regret.
Made-up answers filtered
Akamai’s launch material lists hallucinations beside toxic content as output it can filter before a reply is returned.
Edge, API or proxy
The same protection can sit on Akamai’s edge, behind a REST call from your code, or in a reverse proxy before the model.
Not tied to one LLM
Akamai calls it model-agnostic: it guards apps built on any LLM, hosted on-prem, in a public cloud or across both.
Mapped to the LLM Top 10
Detections are aligned with the OWASP Top 10 for LLM applications, a shared list your auditors and developers know.
Watch Akamai Firewall for AI in action
A demo of screening a generative AI app, Akamai’s April 2025 launch video, and an RSA Conference 2025 talk on threats to AI apps.
Demo: Secure generative AI apps | Akamai Firewall for AI
A product walk-through of how prompts and responses are screened for a generative AI app (July 2025).
Get Powerful Protection for New LLM App Threats
Akamai’s launch video for the LLM firewall, published in April 2025.
Securing AI Apps from Emerging Threats | RSAC 2025 Booth Presentation | Akamai
A booth talk from RSA Conference 2025 on the threats facing AI apps and where a firewall fits.
Want a live, India-context walkthrough for your environment?
Book a guided demo →A WAF reads requests; an LLM attack is a polite sentence. Firewall for AI reads what is being said.
Here’s what genuinely sets it apart — and exactly where it stops.
Checks both sides of the conversation
A WAF looks for malformed requests; an LLM attack is usually a well-formed sentence. Firewall for AI reads the intent of each prompt for jailbreaks and injection, then reads the model’s reply for leaked account data, toxic or biased text and invented answers, so a bad turn can be stopped going in or coming out.
Fits where your AI already runs
Teams that already serve their site through Akamai can switch on inspection at the edge. Others can call a REST API from the app or route traffic through a reverse proxy. Akamai says it is model-agnostic, so the model can stay on-prem, in a cloud or split between the two.
Abuse at volume, not only clever prompts
Public chatbots attract scrapers who want the model’s knowledge and floods that burn inference spend. Akamai lists large-scale scraping, model theft and AI-specific denial of service as targets, an area its bot and DDoS work has long covered for websites.
Where it stops
There is no price, no trial and no published licensing unit. Akamai prints no latency figures, no payload limits and no Indian inspection location, and the full documentation sits behind a login. It guards AI apps you publish; staff use of outside AI tools is Workforce Protector’s job.
The numbers behind the platform
What your Akamai Firewall for AI rollout looks like
List every AI endpoint
Find each chatbot, copilot and LLM API you expose, the model behind it, and who can reach it from the internet.
Choose edge, API or proxy
Map each endpoint to a path: edge if Akamai delivers it, a REST call for internal models, a proxy where code can’t change.
Pilot on one live app
Put the busiest public app behind the firewall in monitor mode and replay known jailbreak and data-leak prompts at it.
Tune and measure delay
Clear false positives on ordinary questions, record added latency per turn, and set what is blocked versus logged.
Enforce and widen
Switch the pilot to blocking, add the remaining endpoints, and review blocked prompts each week with the app owners.
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 loan-assistant bot was being asked to recite its system prompt within a day of launch. Those requests now die at the edge.”
“The REST mode let us screen answers from a model in our own data centre without moving that app onto Akamai delivery.”
“A competitor was hammering our product-search chatbot to copy the catalogue. Scraping blocks cut that traffic off quickly.”
“Output filtering caught replies quoting a policy number back to the wrong customer during testing. That alone justified it.”
“We needed a login to read the real docs, and nobody would give latency numbers until the pilot. Plan time for that.”
“Tuning took two rounds: the first policy blocked harmless questions about exam fees as if they were attacks.”
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 application firewall market — tap any vendor to see why it sits where it does.
TechBag AI Application Firewall Grid
Execution strength vs product vision — the classic market map, minus the paywall.
Announced April 2025; quote-only, demo first.
Deployment Reach × Detection Breadth
The grid nobody publishes — how many ways and places the check can run vs how much of the conversation it inspects.
Edge, REST or proxy; inbound, outbound, scraping and AI DoS.
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.
Akamai Firewall for AI vs the AI application firewall field
Against Palo Alto Prisma AIRS, Cloudflare AI Security for Apps, Amazon Bedrock Guardrails, Azure AI Content Safety and Securiti — on deployment, threats, price, limits and India.
| Dimension | Akamai Firewall for AI | Palo Alto Prisma AIRS | Cloudflare AI Security for Apps | Amazon Bedrock Guardrails | Azure AI Content Safety | Securiti LLM Firewalls |
|---|---|---|---|---|---|---|
| What it is | LLM firewall | AI runtime platform | WAF add-on for LLMs | Managed AWS safeguards | Moderation API | Firewalls in Gencore AI |
| Deployment | Edge, API or proxy | Network or API intercept | Cloudflare proxy only | Inline or ApplyGuardrail | API or Foundry guardrail | Inline in the pipeline |
| Threats covered | Injection to model theft | Injection, DLP, URLs | PII, topics, injection | Six policy types | Harms plus attacks | Injection to poisoning |
| Prompts, answers, RAG | Both ways; RAG unstated | Both ways plus grounding | Incoming prompts | In, out, grounding | Prompt plus 5 documents | Prompt, retrieval, reply |
| Model support | Model-agnostic | Your apps and agents | Model-agnostic | Any model via API | Any text sent to it | Tied to its data graph |
| Pricing model | Quote; unit unpublished | Monthly tokens | Enterprise add-on | Per 1,000 text units | Per 1,000 text records | Quote |
| Published entry price | Not published | No public rate | Enterprise quote | $0.15 per 1K units | $0.375 per 1K records | Not published |
| Included vs add-on | Own SKU; discovery apart | SCM, DLP, logs bundled | Discovery free only | Each policy billed | Shields in the free tier | Part of a platform |
| Published limits | None published | 2 MB sync, 5 MB async | JSON bodies only | 1,000-character units | 5 documents a call | None published |
| Integrations | Edge, REST, proxy | SDK, Strata stack | WAF rules, Wiz, IBM | Bedrock, SageMaker, EC2 | Foundry and REST | DataAI Command Graph |
| India region | Not documented | India region, Aug 2025 | Indian edge sites | Mumbai Region | South India meters | Not published |
| Support | Per contract | Not on product pages | Enterprise contract | Paid AWS plan | Paid plan from $29 | Not published |
| Lock-in and exit | Edge mode needs Akamai | Region-bound keys | DNS on Cloudflare | Checks live in AWS | Microsoft’s categories | Platform-bound |
| Best fit | Akamai-fronted AI apps | Palo Alto estates | Cloudflare Enterprise | Builders on AWS | Azure Foundry teams | RAG on governed data |
Which approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose Akamai Firewall for AI if…
- ✓Your customer-facing chatbot or LLM API already sits behind Akamai, and you want prompts and answers checked at the same edge
- ✓You need one control that can also run as a REST call or reverse proxy for models hosted on-prem or in another cloud
- ✓Scraping, model theft and prompt floods worry you as much as clever jailbreaks, and you want both handled in one place
Compare alternatives if…
- ✓You want a price you can read today — Bedrock Guardrails and Azure AI Content Safety both publish per-1,000 rates
- ✓You need a documented India processing region — Prisma AIRS added one in 2025 and Bedrock Guardrails runs in Mumbai
- ✓Your risk is mostly poisoned RAG data — Securiti’s retrieval firewall screens what the model pulls in
Do not expect…
- ✓A trial, a public price or a stated licensing unit for this firewall
- ✓Published latency, payload or rate limits before you run a pilot
- ✓Control over staff use of ChatGPT and other outside AI tools — that is Workforce Protector
TechBag has no AI application security guide yet, so Akamai Firewall for AI sits outside the category guides. Browse all products to compare it with the rest of the catalogue. →
What does hand-built AI guardrailing cost you?
Drag the sliders (developers shipping LLM features; developer-hour cost). Estimates model time spent hand-writing prompt filters, reviewing odd model replies and chasing abuse at an assumed 1.5 hours per developer a year, with 70% of it removed by one shared firewall 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: Akamai sells Firewall for AI on quote after a demo request, with no trial and no public licensing unit. Finding unknown GenAI endpoints is API LLM Discovery, part of the separately sold API Security. TechBag lists your AI endpoints, asks what the quote counts and where Indian traffic is inspected, then quotes in INR with GST.
Firewall for AI
Best for customer-facing LLM apps
- Quote-only; demo form, no trial
- Edge, REST API or reverse proxy
- Prompts in and answers out
+ Platform add-ons
Best for a broader rollout
- Scoped to your estate
- Add-on modules as needed
- Phased, right-sized deployment
+ API Security
Best when AI endpoints are unknown
- Sold separately, also quoted
- API LLM Discovery finds GenAI endpoints
- TechBag scopes which you need
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 AI apps and LLM APIs face customers or partners, and which only staff? Price the public ones first.
Is each app already served through Akamai, or will it need the REST API or reverse-proxy mode instead?
Where do the models run — on-prem, a public cloud or both — and does any vendor contract bar a proxy in front?
Which matter most to you: jailbreaks, leaked customer data, scraping of model knowledge, or prompt floods?
What added delay per turn can the app tolerate? Ask Akamai for pilot figures, since none are published.
Where will inspection of Indian users’ prompts take place? Get the answer in writing; it is not documented.
Do you also need API Security’s LLM discovery to find GenAI endpoints you do not yet know about?
What unit does the quote count — apps, requests or traffic? Ask for INR with GST and the renewal terms.
Questions buyers ask
Ready to evaluate Akamai Firewall for AI?
List the AI endpoints you expose first, or let a TechBag advisor scope a pilot that puts one live chatbot behind the firewall in monitor mode.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.