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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.
NHIMG editorial — based on content published by TruFoundry: Helicone vs LiteLLM, a practical comparison for engineering teams in 2026
Questions worth separating out
Q: How should teams govern AI gateways that route model and tool traffic?
A: Teams should treat the gateway as the control boundary for identity, spend, logging, and policy enforcement.
Q: Why do AI gateways create new identity governance concerns?
A: AI gateways sit between users, service accounts, agents, and models, so they become the place where identity, authorisation, and data controls either stay coherent or fragment.
Q: How do teams know whether an AI gateway is actually enforcing governance?
A: Look for blocked requests, denied tools, scoped credentials, immutable audit logs, and clear owner assignment for policy decisions.
Practitioner guidance
- Define the gateway control boundary Map which decisions belong in the AI gateway, which belong in IAM, and which belong in application code before production rollout.
- Separate visibility from enforcement Require request logs, prompt traces, and cost analytics, but do not treat them as a substitute for policy enforcement.
- Review MCP permissions as privileged access Inventory every MCP server, tool connection, and delegated capability exposed to agents.
What's in the full article
TruFoundry's full comparison covers the operational detail this post intentionally leaves for the source:
- The exact feature-by-feature breakdown of routing, observability, and enterprise governance differences between the two platforms.
- Deployment and self-hosting implications for teams running gateways in Python, Docker, or managed cloud environments.
- Security and supply chain details from the LiteLLM PyPI incident, including the affected versions and response timeline.
- Cost, licensing, and enterprise feature gating details that matter once teams move from evaluation to rollout.
👉 Read TruFoundry's Helicone vs LiteLLM comparison for production AI teams →
Helicone vs LiteLLM: are AI gateway controls keeping up?
Explore further
20/08/2026 6:39 pm
Observability is not governance, and the market keeps confusing the two. Logging prompts, costs, and latency is useful, but it does not answer who is allowed to invoke tools, what an agent may reach, or whether the request path is subject to policy. The comparison illustrates a category problem: many teams buy visibility when they actually need enforced control. Practitioner conclusion: evaluate AI gateways on decision authority, not just telemetry depth.
A question worth separating out:
Q: What is the difference between AI observability and AI governance?
A: AI observability tells you what the system did. AI governance decides whether it should have been allowed to do it, who approved it, and what happens when it crosses a policy boundary. Observability is a data problem. Governance is an operating model that combines policy, ownership, evidence, and enforcement.
👉 Read our full editorial: Helicone vs LiteLLM shows the governance gap in AI gateways
