Gateplex vs Panguard.AI

Gateplex and Panguard.AI are both guardrails & safety tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Gateplex

Gateplex is governance middleware: it does not run your agents, it watches them. The vendor describes it as a policy enforcement layer that intercepts agent actions — API calls, approvals, data sends — checks them against defined rules, and blocks or flags violations before execution completes. That distinction matters for regulated environments where post-hoc logging is not enough. The free tier covers three agents and a capped intercept volume per month, which fits a proof-of-concept but runs short the moment a second team deploys. Beyond that ceiling, teams move to a paid tier or hit a wall.

Panguard.AI

Panguard installs in one command, runs entirely offline with zero telemetry, and auto-detects agents across a wide surface — Claude Code, Cursor, VS Code Copilot, Gemini CLI, and more. The vendor states 768 ATR (Agent Threat Rules) execute locally as deterministic checks before any skill loads, then continue guarding each action at runtime against prompt injection and poisoned MCP tools. Rules contributed anywhere benefit every adopter — the vendor describes this as 'threat crystallization.' The ceiling appears when a threat is genuinely novel: deterministic rules only catch what someone has already seen and codified, so the AI analysis fallback carries the weight for zero-day patterns. Teams with regulated environments get signed, audit-ready output without routing data to a third party.

Attribute	Gateplex	Panguard.AI
Pricing	Paid	Free
Price	$199/month	—
Free trial	No	No
Open source	No	Yes
Has API	Yes	No
Self-hosted option	No	Yes
Platforms	Cloud-based middleware; integrates with agent frameworks on any platform running OpenAI, Anthropic, LangChain, CrewAI, AutoGen, Vertex AI, or AWS Bedrock	Linux, macOS (via shell install)
Pros	Real-time action interception before execution completes, which means a procurement agent cannot approve an out-of-policy spend and then get flagged about it afterward — the action is stopped in the moment. PII detection at the intercept layer, so customer data does not reach a third-party API before a policy check has cleared it — without this, a misconfigured agent integration becomes a data leak that logging discovers too late. Duplicate transaction detection for financial agents, which prevents a refund or payment from issuing twice due to a retry loop or race condition — the kind of error that is trivial to miss and expensive to reverse. Audit trail output formatted for legal and compliance review rather than raw telemetry, so the evidence package a regulator or procurement committee requests does not require a data engineering sprint to produce. API access to the enforcement layer, which means policy rules can be managed programmatically and integrated into existing deployment pipelines rather than configured only through a UI.	One-command offline install with zero telemetry, which means teams in air-gapped or regulated environments get runtime protection without routing agent traffic through a third-party service. 768 deterministic ATR rules execute locally in milliseconds, so security checks add no meaningful latency to skill loading and produce consistent, reproducible results rather than probabilistic LLM verdicts. Community threat corpus with upstream merges from Cisco and Microsoft, which means a rule written against an attack anywhere in the ecosystem closes the same gap for your agents without your team having to discover the threat independently. Signed, audit-ready output generated locally, so compliance reviews have a tamper-evident evidence trail without exporting agent behavior data to a vendor. Auto-detects a broad set of agent environments — Claude Code, Cursor, VS Code Copilot, Gemini CLI, and more — so teams running heterogeneous tooling do not need per-environment configuration to get baseline coverage.
Cons	No self-hosted deployment option is documented — every agent action routed through Gateplex passes through vendor infrastructure. Teams with data residency requirements, air-gapped environments, or legal restrictions on externalizing sensitive financial or health data have no workaround: this is a hard architectural incompatibility, not a configuration problem, and those teams evaluate on-premises alternatives instead. The free tier caps at three agents and a fixed intercept volume per month. A team piloting with two agents clears that ceiling the moment a third team onboards or production traffic spikes — at which point the choice is a paid tier commitment or a freeze on agent expansion, and the evaluation timeline compresses. Gateplex enforces policy on agent actions but does not itself define what your agents should do — teams that want policy logic tightly coupled to agent orchestration (branching based on what a prior step returned, approval gates wired into the agent graph) end up maintaining Gateplex as a separate enforcement layer alongside their orchestration framework, which is two systems to debug when something breaks.	Deterministic rules only catch threats someone has already seen and codified: a novel prompt injection technique or a newly poisoned MCP tool with no prior CVE or ATR entry passes the rule layer clean. The AI analysis fallback carries that burden, but teams whose threat model is dominated by zero-day or highly targeted attacks are betting on a layer with no published recall figures for unseen patterns. No API and no hosted option, which means security checks cannot be integrated into a CI pipeline or a centralized policy enforcement layer without scripting around the CLI directly — teams that need programmatic gate control in their build system end up writing and maintaining that wrapper themselves. Private, organization-specific tooling generates attack surfaces the community corpus will never describe. Teams building internal MCP servers with custom business logic will need to author their own ATR rules, and the docs describe a review-and-merge pipeline optimized for community contribution — not private rule management at scale. At the point where a team is maintaining a significant private rule library on top of the public corpus, the operational model starts to resemble a full detection engineering practice, and teams with that capacity often move toward purpose-built security platforms that offer rule management, alerting, and incident workflows.

Bottom line

Gateplex is paid while Panguard.AI is free; Panguard.AI is open source; only Gateplex exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Gateplex and Panguard.AI?

Gateplex is Paid, while Panguard.AI is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Gateplex better than Panguard.AI?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.