Gateplex vs Skillier.ai

Gateplex and Skillier.ai are both inference engines & infra 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.

Skillier.ai

Skillier sits between you and your AI client, detecting what domain you're working in and loading the relevant skill — finance modeling, legal reasoning, DevOps runbooks — into the context without you leaving the interface. The Lite version is MIT-licensed and runs offline, which matters for air-gapped environments where cloud-dependent tooling is a non-starter. The routing model hands control back through an AskUserQuestion prompt, so you confirm the skill selection rather than having it decided for you. That model works cleanly for single-domain sessions. Blended workflows — writing copy while checking financial assumptions, for instance — require you to manually re-route between skills, and the seams show.

Attribute	Gateplex	Skillier.ai
Pricing	Paid	Paid
Price	Free to $199+/month	—
Free trial	No	No
Open source	No	No
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	Claude Desktop, Claude Web, Claude Code CLI, OpenClaw
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.	Offline skill access via the self-hostable Lite version, so air-gapped teams and low-connectivity environments can load domain expertise without a live API call — something cloud-only tools in this category cannot offer. Skill routing that triggers without leaving the chat interface, which means the context window you've built up in a session doesn't get abandoned every time you need to shift to a different domain. MIT-licensed Lite version with no paid tier required, so teams that need to audit, fork, or self-host the code have a legal path to do that without a procurement conversation. Explicit AskUserQuestion confirmation before a skill loads, so you stay in control of what gets injected into context — preventing the silent prompt stuffing that degrades output quality when auto-routing guesses wrong.
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.	Multi-domain sessions hit the routing model's friction ceiling fast: each skill switch requires a confirmation prompt, so a workflow that blends financial modeling with technical writing generates repeated interruptions — teams doing this regularly report falling back to manual context pasting because it's faster. No API surface is described, which means teams who want to embed skill routing inside a pipeline, a CI step, or any system outside Claude Desktop and Claude Web have no integration path — at that point they are looking at building their own context-injection layer or switching to a tool that exposes programmatic control. Scoped exclusively to Claude Desktop and Claude Web at time of review, so organizations standardized on other AI clients — GPT-4 via ChatGPT, Gemini, or internal models — get no benefit and need a different solution entirely.

Bottom line

Only Gateplex exposes a public API. Choose based on which difference matters most for your workflow.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.