Your AI Gateway Won't Save You
The AI gateway pitch sounds compelling: one proxy layer, all your models, unified cost tracking. But when your agent needs to update a purchase order in SAP, approve an invoice in Oracle, or query a legacy database over SFTP — what does your gateway do? Nothing. Because gateways govern requests. The hard problem is governing execution.
What Gateways Actually Solve
AI gateways like Portkey, Helicone, and LiteLLM sit between your application and your LLM provider. They solve real problems: model routing across providers, automatic fallback when one model is down, cost tracking per request, rate limiting, and caching identical prompts to save money.
These are legitimate infrastructure concerns. If you're running a consumer product that makes thousands of LLM calls per minute, you need a gateway. The economics demand it.
But here's the assumption baked into every gateway: the LLM call is the thing that matters. The request goes out, the response comes back, and you track what happened in between.
For enterprise operations, that assumption is wrong.
The Governance Gap
What happens after the model responds? In an enterprise environment, the answer is: the agent acts. It updates a record in SAP. It triggers an SFTP file transfer to a vendor. It executes a parameterized query against a production database. It creates an invoice through a SOAP endpoint that hasn't been updated since 2014.
These are the actions that carry real risk. A bad LLM response costs you a few cents in wasted tokens. A bad agent execution costs you a wrong purchase order, a compliance violation, or a production database write that can't be undone.
No gateway covers this. They can't, because they operate at the wrong layer of the stack.
Two Different Layers
Gateways sit between your app and the LLM. They see prompts and completions. Oceum sits between your agents and your enterprise systems. It sees connections, credentials, operations, approvals, and execution results. Different layer, different problem, different architecture entirely.
What Governed Execution Looks Like
When an agent in Oceum needs to act on a legacy system, the action flows through a governed pipeline:
- The execution request hits the policy engine, which evaluates risk classification, rate limits, and scope authorization
- High-risk operations route to a human approval queue — an operator sees the connection context, the operation, and the input data before approving
- Credentials are resolved from a zero-knowledge vault at execution time — the agent never sees the raw secret, and decrypted material is cleared from memory after the call
- The appropriate protocol adapter executes the operation — REST, SOAP with WS-Security, SFTP with SSH keys, or parameterized database queries
- Output is sanitized to strip any credential material that might have leaked into response data, then recorded with full timing metadata in an immutable audit trail
Every step is auditable. Every execution has a trail. Every credential is handled with zero-knowledge isolation. This is what enterprise operations teams need — not cheaper model routing.
The Stack, Not the Choice
This isn't a versus argument. Gateways and governance infrastructure aren't competing for the same budget line. They're different layers that coexist.
You might run Portkey for model routing and cost optimization on your LLM calls. And you'd run Oceum for governed execution when those LLM-powered agents need to act on SAP, Oracle, legacy databases, and file systems. One handles the intelligence layer. The other handles the action layer.
| AI Gateway | Oceum | |
|---|---|---|
| Sits between | App ↔ LLM | Agent ↔ Enterprise Systems |
| Governs | Model requests | Agent execution |
| Credentials | API keys for LLM providers | Zero-knowledge vault for legacy systems |
| Protocols | HTTP to LLM APIs | REST, SOAP, SFTP, JDBC, Webhooks |
| Audit trail | Request logs + cost | Execution records + approval chain |
| Human oversight | Spend alerts | Approval gates per operation |
The Real Question
The question isn't "which AI gateway should we use?" That's a procurement decision you can make in an afternoon.
The question is: what happens after the model responds? When your agent has a plan and needs to execute it against the systems your business actually runs on — who governs that?
If the answer is "nobody," you have a gap that no gateway will close.
If you're thinking about this problem, we should talk.