A.I.G (AI Infra Guard) vs Depthdata: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of A.I.G (AI Infra Guard) and Depthdata — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
A.I.G (AI Infra Guard)
Tencent Zhuque Lab
Tencent's open-source AI red teaming platform for scanning agents, agent skills, MCP servers and AI infrastructure, plus LLM jailbreak evaluation.
Key features
- Agent Skills Scan: Audits agent skill packages against a nine-category risk taxonomy aligned with the public SkillTrustBench T01-T09 classification, including detection of .pyc bytecode bypasses and charset smuggling.
- MCP Server Scan: Inspects MCP servers for threats such as tool poisoning, credential exfiltration and command injection, with tool whitelisting to prevent remote code execution during dynamic scanning.
- AI Infrastructure Vulnerability Scanning: Checks deployed AI components against a library that has grown to roughly 130 components and over 2,000 CVE rules, covering frameworks such as llama.cpp.
- Jailbreak Evaluation: Runs single-turn jailbreak operators plus multi-turn attack techniques including Many-Shot, PAIR, GOAT and ActorAttack to measure a model's resistance.
- Agent Scan with OWASP Coverage: Assesses running agents using OWASP-derived skills and web exfiltration detection, with a dedicated agent red team skill for comprehensive assessment.
- Standalone Scanner CLIs: skill-scan, mcp-scan and agent-scan each install as an independent command-line tool so scans can be wired directly into enterprise CI/CD pipelines.
- Docker Deployment with Web UI: Deploy the full platform with Docker on 4GB+ RAM and reach the web interface at localhost:8088, or use a one-click install script or a source build.
- AI Security Skill Market: A catalog of official security scanning skills, with the frontend fully open-sourced and integration available from OpenClaw chat via the aig-scanner skill.
Best for
- Pre-Deployment Agent Audit: Scan an internally built agent and its skill bundle for prompt injection, exfiltration and privilege risks before it is released to staff.
- MCP Supply Chain Review: Vet third-party MCP servers for tool poisoning and credential exfiltration before connecting them to production assistants.
- CI/CD Security Gate: Run skill-scan as a standalone CLI on every pull request so risky agent skills fail the build rather than shipping.
- Model Safety Benchmarking: Measure how a deployed LLM holds up against single and multi-turn jailbreak techniques before and after guardrail changes.
- AI Infrastructure Patch Triage: Inventory AI serving components and match them against the CVE rule library to prioritise which hosts need patching.
- Security Research and Reporting: Use the open scan engines and SkillTrustBench alignment as a reproducible basis for internal or published AI security research.
D
Depthdata
Depthdata
Read-only AI spend and adoption intelligence — unify ChatGPT, Claude, Copilot, and Gemini admin data into one governed view of usage, cost, and outcomes.
Key features
- Unified AI Data Model: Normalizes usage, seats, and spend from ChatGPT, Claude, Copilot, Gemini, Cursor, Perplexity, and 16+ other tools into one schema.
- Workspace Health Score: A single 0–100 score rolls up adoption, depth, and spend across every AI tool your company uses.
- Cost per Outcome: Reports cost per active user, dormant-seat waste, and model mix using finance-grade math rather than vendor-reported figures.
- Coaching & Nudges: Detects what power users do differently, packages the patterns into playbooks, and routes nudges to the teams leaving value on the table.
- Depth-Based Leaderboards: Scores chained workflows, tool breadth, and retry patterns instead of raw prompt counts — the only way to game it is to get better at AI.
- Confidence-Labelled Analytics: Every KPI carries a confidence label and open methodology so board-deck numbers hold up under follow-up questions.
- Read-Only Admin OAuth: Connects via official admin APIs — no agents on laptops, no browser extensions, and prompts are never read.
- BI & Board Export: Push metrics to your BI stack or export board-ready reports with methodology attached.
Best for
- AI FinOps: CFOs and finance ops track cost per active user, idle-seat waste, and model mix across the entire AI stack in one ledger.
- Executive Reporting: Present the CEO or board a defensible workspace health score with trends and confidence labels instead of vendor screenshots.
- Enablement & Coaching: L&D and team leads target nudges at dormant seats using patterns learned from the workspace's own power users.
- License Right-Sizing: IT identifies which seats belong on premium tiers and which are already optimal, using measured depth-of-use.
- Cross-Tool Governance: Central IT gets one governed view of adoption and spend across ChatGPT, Claude, Copilot, Gemini, and future AI tools as they're added.