Topic starter
20/08/2026 6:04 pm
TL;DR: Helicone is stronger for observability while LiteLLM is stronger for provider abstraction, but TrueFoundry argues the real enterprise issue is governance across models, tools, and agents in production, according to TruFoundry. The decision is no longer just about routing versus logging, because AI gateways are becoming control points for policy enforcement, auditability, and MCP access.
Editorial analysis by NHI Mgmt Group, based on content published by TruFoundry: “Helicone vs LiteLLM: A Practical Comparison for Engineering Teams in 2026”.
Key questions
Q: What breaks when AI gateway traffic is not logged and traced end to end?
A: Without end to end logging and tracing, teams lose visibility into which model, agent, or tool produced a risky action.
Q: Why do electronic signatures need identity and access controls, not just cryptography?
A: Cryptography confirms that a signature can be verified, but it does not by itself prove the right person signed it or that the signer was properly authorised.
Q: What are the signs that an AI governance programme is too shallow?
A: A shallow programme usually shows up when teams rely on accuracy alone, lack clear metrics for fairness or explainability, and cannot verify how models behave under stress.
Practitioner guidance
- Define gateway ownership and control scope Assign a named owner for every AI gateway that brokers model, tool, or agent traffic.
- Map MCP permissions to NHI lifecycle controls Treat MCP access the same way you treat service accounts and tokens.
- Separate routing decisions from enforcement decisions Use routing features to choose providers, but require a distinct enforcement layer for budgets, auditability, and tool approval.
Bottom line: AI gateways are moving from traffic brokers into governance layers, which means routing and logging are no longer enough on their own.
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20/08/2026 6:39 pm
AI gateway governance is now an identity problem, not just an observability problem. Once gateways control access to models, tools, and agents, they inherit the same governance expectations that already apply to workload identity and privileged access. Logging alone does not prevent misuse if the control boundary cannot enforce who may call what, under which policy, and with which credentials. The practitioner conclusion is that gateway architecture must be evaluated as part of IAM and NHI governance.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: Should teams use one platform for routing, observability, and policy enforcement?
A: Only if the platform can genuinely enforce all three without weakening auditability or operational separation. In many environments, routing and observability are easier to adopt first, while policy enforcement requires stricter ownership and lifecycle discipline. The safer approach is to verify that the control boundary matches the risk boundary before consolidating functions.
👉 Read our full editorial: Helicone vs LiteLLM shows the governance gap in AI gateways