Notifications
Clear all
Topic starter
20/08/2026 5:52 pm
TL;DR: LLM routing now acts as the decision layer between applications and model providers, with the strongest production setups tying provider selection to tracing, online scoring, experiments, and live traffic quality, according to Braintrust. That shifts the real question from simple failover to whether routing policies preserve reliability, cost discipline, and measurable answer quality at scale.
NHIMG editorial — based on content published by Braintrust: Best LLM routers and model routing platforms in 2026
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes.
Questions worth separating out
Q: How should security teams govern LLM routing in production?
A: Treat routing as a policy-controlled layer.
Q: Why do model routers create new NHI risks?
A: Because routers depend on API keys, project keys, and provider tokens that behave like non-human identities with access and lifecycle requirements.
Q: What do teams get wrong about LLM failover?
A: They often assume fallback is purely an availability control.
Practitioner guidance
- Inventory model-routing credentials as NHIs List every provider key, project-scoped key, custom endpoint secret, and gateway credential used in model routing.
- Require traceable routing decisions Ensure each routed request records the selected model, route reason, latency, token usage, and downstream score or feedback.
- Tie production routing to evaluation gates Do not move a routing policy into production until the candidate model has been compared on representative traffic with scoring criteria that reflect business-critical quality thresholds.
What's in the full article
Braintrust's full article covers the operational detail this post intentionally leaves for the source:
- Provider-by-provider feature comparisons for failover, routing rules, observability, and deployment flexibility
- Pricing and packaging detail for each router, including managed versus self-hosted trade-offs
- Workflow-level evaluation features such as tracing, online scoring, experiments, and human review
- Practical procurement considerations for teams deciding between managed gateways and open-source deployment
👉 Read Braintrust's guide to the best LLM routers and model routing platforms in 2026 →
LLM routing and governance: are your controls keeping up?
Explore further
20/08/2026 6:31 pm
Model routing is becoming a governance layer, not just an optimisation layer. Once routing decides which model handles which request, it influences data handling, cost, resilience, and evidence of control. That puts it in the same governance conversation as workload identity and secrets management, especially in environments where API keys, provider accounts, and trace data govern model access. Practitioners should stop treating routing as plumbing and start treating it as policy.
A question worth separating out:
Q: How can organisations tell if routing is actually working?
A: Measure whether routed requests meet the intended quality, cost, and latency targets while remaining traceable end to end. If teams cannot explain why a route was chosen, which credential was used, and how the result was scored, the routing policy is not operating as a control.
👉 Read our full editorial: LLM routing platforms are becoming the control plane for model governance
