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19/08/2026 4:05 pm
TL;DR: AI gateways centralize model routing, access controls, caching, logs, and cost visibility across providers, according to Braintrust’s 2026 comparison of six gateways. The real shift is governance, because production AI now needs a control layer that can inspect behavior, not just move traffic.
NHIMG editorial — based on content published by Braintrust: AI gateway comparison of the 6 best ranked options in 2026
By the numbers:
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes and as quickly as 9 minutes in some cases.
Questions worth separating out
Q: What breaks when AI gateway controls are not centralised?
A: Teams lose consistent control over model keys, quotas, logs, and routing policy, which creates blind spots across applications and providers.
Q: Why does AI visibility matter for NHI governance?
A: AI systems can act like non-human identities when they hold credentials, reach data stores, or execute workflows.
Q: What do security teams get wrong about AI gateway deployment?
A: They assume visibility alone equals control.
Practitioner guidance
- Define the AI gateway as a policy boundary Place model routing, provider keys, quotas, and logs behind one controlled endpoint so application teams do not implement their own access logic.
- Map every provider key to an accountable identity Associate each service account, virtual key, or team token with an owner, purpose, rotation rule, and revocation process.
- Require auditable traces for production requests Keep logs that capture caller identity, model used, payload metadata, latency, cache status, and error origin for investigation and release review.
What's in the full article
Braintrust's full article covers the operational detail this post intentionally leaves for the source:
- Side-by-side product comparisons across six gateways, including provider coverage and deployment trade-offs
- Pricing and plan details for teams evaluating budget, logging, and enterprise governance requirements
- Feature-level breakdowns of caching, observability, guardrails, and audit log behaviour
- Guidance on which gateway fits developer routing, infrastructure control, or production evaluation workflows
👉 Read Braintrust’s comparison of the 6 best AI gateways in 2026 →
AI gateway governance: are your controls keeping up with production use?
Explore further
19/08/2026 4:39 pm
AI gateways are becoming governance enforcement points, not just routing utilities. Once model traffic passes through a central layer, the real question is whether that layer controls identity, policy, logging, and spend with the same discipline applied to IAM. Teams that treat gateways as simple proxies will miss the security value of the control point. Practitioners should govern gateways as part of the production identity boundary.
A question worth separating out:
Q: How should organisations decide whether to use a gateway and an evaluation workflow together?
A: Use both when production traffic needs to inform quality decisions, not just routing and cost control. A gateway handles request mediation, while evaluation tells you whether the behaviour is acceptable before release. When those functions are separate, teams can govern traffic without improving it, which is a weak operating model for AI in production.
👉 Read our full editorial: AI gateway governance is becoming a control plane problem for teams
