AI Boost vs Gateplex

AI Boost and Gateplex 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.

AI Boost

MCP server for capturing and injecting developer expertise as searchable, reusable context for LLM agents.

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.

Attribute	AI Boost	Gateplex
Pricing	Paid	Paid
Price	—	$199/month
Free trial	No	No
Open source	No	No
Has API	Yes	Yes
Self-hosted option	No	No
Platforms	Web (MCP server), integrates with Cursor, Claude Code, and generic MCP clients	Cloud-based middleware; integrates with agent frameworks on any platform running OpenAI, Anthropic, LangChain, CrewAI, AutoGen, Vertex AI, or AWS Bedrock
Pros	Solves real gap: agents get curated, structured expertise instead of noisy memory replays Private by default with strong commitments: never indexed, sold, or used for training Seamless integration: one MCP config, works across all agent clients and projects Semantic + keyword indexing ensures correct boosters surface at the right moments	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.
Cons	Pricing model and tiers not clearly communicated on the vendor site Requires agents to support MCP protocol (limits compatibility to newer tools) Booster quality and relevance depend on human curation; poor capture = poor suggestions	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.

Bottom line

AI Boost and Gateplex are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between AI Boost and Gateplex?

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

Is AI Boost better than Gateplex?

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.