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G8KEPR VS FriendlyData

Compare G8KEPR VS FriendlyData and see what are their differences

G8KEPR

Unified security for APIs, AI agents, MCP tools, and LLM applications. API Security + MCP Security + AI Gateway + Verification Engine. Starting at $399/mo.

Rating
0 reviews
Pricing
Freemium Free trial $399 / Monthly (API Security - MCP Security - AI Gateway - Verification Engine)
Promote on VibeVoted
Alternatives Is it good?
FriendlyData

Communicate with databases like a human

Rating
0 reviews
Promote on VibeVoted
Alternatives Is it good?
Modelence
Create production-ready applications with zero code sponsored

Which is more popular?

Cyber Security popularity
100% vs 0%
alternatives listed
24 vs 16

Base details

Website, pricing, platforms and company facts side by side.

G8KEPR
FriendlyData
Website g8kepr.com friendlydata.io
Pricing
Freemium Free trial $399 / Monthly (API Security - MCP Security - AI Gateway - Verification Engine) Official pricing
—
Platforms
SaaS Cloud Self Hosted REST API Docker Kubernetes Linux +4
—
Company Startup from the United States · 1 - 9 employees · 2026 —
Listed in
Cyber Security AI MCP API LLM Gateway API Gateway Security & Privacy Monitoring Tools Security API Tools +7
AI Databases No Code Spreadsheets +1

About G8KEPR and FriendlyData

In their own words, as submitted to SaaSHub.

G8KEPR
FriendlyData

A runtime security layer for AI apps — API, MCP, gateway, and model-output checks on one request plane, running inside your own VPC. Not a human at a keyboard — an AI agent running a multi-stage campaign: a recon probe, a fuzzing burst, a prompt injection, a poisoned tool. Each move rides below...

Read more about G8KEPR

No description of FriendlyData yet.

Features and specs

What each product offers, as listed by its team.

G8KEPR 7 features
FriendlyData 3 features
  • API Security
    1,500+ threat signatures, OWASP Top 10 coverage, real-time anomaly detection
  • MCP Security
    Deterministic tool-poisoning & rug-pull detection (SHA-256), CVE replay
  • AI Gateway
    Inline LLM firewall across 14+ providers, prompt + response scanning
  • Deterministic detection
    Regex + classical ML, ~7ms, $0 per token, no LLM-as-judge
  • Verification Engine
    Output DLP — PII/secret redaction plus grounding/hallucination checks
  • Deployment
    Runs in your VPC — zero data egress, self-hostable
  • Frameworks
    Mapped to OWASP, MITRE ATT&CK, ATLAS, and EU AI Act
  • Ease of Use
    FriendlyData allows users to interact with databases using natural language queries, which makes it accessible for non-technical users who are not familiar with complex database query languages.
  • Time-Saving
    By enabling natural language processing, FriendlyData reduces the time it takes for users to get the information they need, facilitating quicker decision-making processes.
  • Integration
    FriendlyData can be integrated with various platforms and services, making it versatile for different business needs.

Possible disadvantages

  • Complex Queries Limitation
    While FriendlyData is useful for simple queries, it may struggle to accurately interpret and execute more complex queries, limiting its effectiveness for advanced data analysis.
  • Language Ambiguity
    Natural language processing might not always accurately interpret user queries, especially if the input is ambiguous or lacks specificity.
  • Dependency on Vendor
    Relying on third-party services like FriendlyData introduces dependency on the vendor for maintenance, updates, and support, which could pose risks if the service changes or is discontinued.

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
G8KEPR
FriendlyData
100% 100%
Cyber Security
0% 0%
40% 40%
AI
60% 60%
100% 100%
API Gateway
0% 0%
0% 0%
Databases
100% 100%

Questions & Answers

As answered by people managing G8KEPR and FriendlyData.

What's the story behind your product?

G8KEPR's answer

G8KEPR started from a simple observation: every AI security tool assumed you were fine sending your prompts to someone else's cloud to be inspected — and none of them covered the new attack surface that MCP and AI agents introduced.

So it was built the opposite way: deterministic detection that runs inside your own network, covering all four layers of the modern AI stack, with honest, reproducible benchmarks instead of marketing numbers.

What makes your product unique?

G8KEPR's answer

Most AI security tools use an LLM to judge whether input is an attack — which is slow, costs per token, and is itself jailbreakable.

G8KEPR does the opposite:

  • Deterministic detection (regex + classical ML) — ~7ms, $0 per token, no LLM-as-judge
  • Runs in your own VPC — zero data egress, works air-gapped
  • Dedicated MCP security — deterministic tool-poisoning & rug-pull detection, with replay tests for real MCP CVEs (the part almost no one else covers)

Why should a person choose your product over its competitors?

G8KEPR's answer

Four security layers in one platform — API Security, MCP Security, AI Gateway, and a Verification Engine — instead of stitching together separate vendors.

  • Legacy gateways (Kong, Apigee) were built before LLMs, agents, and MCP existed, and charge $20k+/yr for middleware
  • G8KEPR starts at $399/month with AI-native capabilities they don't offer
  • Detection is deterministic and reproducible — published benchmarks, including the weak spots
  • Nothing leaves your network

How would you describe the primary audience of your product?

G8KEPR's answer

Engineering and security teams shipping AI applications to production — anyone running LLMs, AI agents, or MCP tools.

Especially:

  • Teams that can't send prompts to a third-party cloud for analysis (privacy, compliance, air-gapped)
  • Teams securing the agent tool-call layer (MCP), not just the API
  • Teams preparing for EU AI Act enforcement

Which are the primary technologies used for building your product?

G8KEPR's answer

Frontend: Next.js, React, TypeScript, Tailwind CSS Backend: Python, FastAPI, PostgreSQL, Redis Detection: self-hosted classical ML (TF-IDF + logistic regression) and fine-tuned models — no hosted LLM in the detection path Billing: Stripe Deployment: Linux / nginx, containerized for VPC & self-hosted

Who are some of the biggest customers of your product?

G8KEPR's answer

Newly launched — early access is open at g8kepr.com.

User comments

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