For LLM gateway teams

Discover projects that may need routing, policy, cost, and model access controls

AgentInfra Radar helps LLM gateway companies monitor public LLM app, agent, and infrastructure projects that may become relevant for gateway adoption. It supports account research and partner mapping without promising purchase intent.

Pain points

Gateway needs often show up inside public product and developer activity before a team announces a formal platform decision.

Teams may adopt multiple models before centralizing routing, fallback, spend controls, or policy enforcement
Agent and MCP projects can expand model traffic across tools and workflows
It is hard to separate genuine infrastructure fit from general AI noise

How AgentInfra Radar helps

We group public projects by infrastructure relevance and explain the possible gateway need in plain language.

Surface projects building LLM apps, agents, evals, MCP integrations, and builder platforms
Attach a fit note and suggested angle for review before outreach
Use source links and source-check status to decide what requires more research

Example lead type: multi-model apps

Applications or frameworks that appear to work across more than one model provider or LLM workflow.

Potential fit for routing, fallback, rate limits, and model policy
Useful for gateway teams mapping early platform demand

Example lead type: agent tool stacks

Agent projects that connect model calls to external tools, APIs, and workflow steps.

Potential fit for access controls, request logging, and usage governance
The suggested angle can reference the public tool-use context without overclaiming intent

Example lead type: AI app builders

Low-code or builder platforms where teams may need gateway controls as usage moves closer to production.

Potential fit for spend controls, monitoring, and centralized model access
Fit scores help separate better-fit projects from broad AI directories

Category-specific sample use

A gateway-focused sample can exclude irrelevant security-only or investor-only records.

Prioritize projects where model access and orchestration are visible
Keep early-stage watchlist records separate from outreach-ready records
Use the data as account research, not automated selling

Using fit notes, scores, and suggested angles

The fields help your team triage which projects might be relevant for gateway conversations.

The fit note describes a plausible gateway use case such as routing, fallback, spend control, rate limiting, or model governance
The fit score ranks the strength of public evidence, category fit, and review readiness
The suggested angle provides context for a human note or partner-research memo after verification

Risk boundary

The radar is based on public-source research and manual verification.

No guaranteed outcomes, customer intent, or response rates
No scraped private emails, hidden data, or automated outreach
Gateway fit should be confirmed by a human before any sales motion

Next step

Download an LLM gateway sample

No form is required. Download a small LLM gateway sample focused on routing, agent tool use, AI app builders, and MCP workflows.

Download LLM gateway sample