Compare
Alpha vs. a typical AI gateway
Most gateways route a call and log it. An operating layer owns the agent that makes the call — its budget, its reliability, and the intelligence it generates.
The difference
A gateway routes. An operating layer owns.
A typical AI gateway
- Routes a call to a provider and logs what happened
- Cost is measured per workspace, after the fact
- Your traces are telemetry — data you look at
- Reliability depends on the gateway staying up
- Runs where the vendor runs it
Alpha — agent operating layer
- Runs the agent: routing, budgets, guardrails and memory in one layer
- Budget capped and attributed per agent, before you overspend
- Your traces become memory and a model — an asset you own
- Fails open — agents fall back to your providers directly
- Cloud, hybrid, or fully self-hosted / sovereign
Side by side
Six dimensions that matter.
Compared at the category level — a proxy/observability gateway versus an operating layer. Bring your shortlist and hold each vendor to the same rows.
DimensionTypical AI gatewayAlpha
Primary jobRoute and observe LLM callsRun, govern and compound agents
Cost controlReporting, usually per workspaceA hard budget per agent
Model spendSometimes a margin on tokensBYOK — your accounts, zero markup
Your trace dataLogs to inspectMemory reused + a fine-tune flywheel — and it stays yours
Failure modeOften in the request pathFails open to your providers
DeploymentTypically hostedCloud, hybrid, or sovereign / self-hosted
This compares categories, not a single named product — capabilities vary by vendor and change fast, so check each one's current docs. More on the categories → AI gateway comparison.
The comparison that ends the comparison.
See what your agents cost today — free, no keys, no integration. Then decide what's worth paying for.