Connecting both Claude and OpenAI in production is the easy part. The hard part is keeping the system stable across the quality-latency-cost triangle.
Without a routing gateway, you usually get latency spikes, runaway bills, and ugly cascading failures.

1) Define routing objectives before writing any policy

Treat routing as an SLO problem, not a vibe-based decision:

  1. Latency target: keep P95 under your business threshold (for example, < 3s)
  2. Cost target: cap per-request token cost (for example, <$0.02)
  3. Quality target: keep task success rate above a hard floor (for example, >= 97%)

Then classify traffic by business value:

  • L0 (high value): checkout, risk controls, escalated support
  • L1 (medium value): normal Q&A, report generation
  • L2 (low value): rewrite, draft, tagging

No tiers, no control.

2) Latency tiers: fast first, stable second

A pragmatic policy stack:

Primary model per tier

  • L0: quality-first primary
  • L1/L2: value-first primary

Soft-timeout failover

  • If no first token in ~1200ms, trigger a hedged request to the fallback provider
  • Return the first successful response, cancel the slower one

Circuit breaker + half-open probes

  • Trip breaker on consecutive failures (for example, 5 failures in 30s)
  • Probe recovery with controlled half-open traffic
type RoutePolicy struct {
    SoftTimeoutMs int
    MaxCostUSD    float64
    Tier          string // L0/L1/L2
}

func Route(req Request, p RoutePolicy) Provider {
    if p.Tier == "L0" {
        return providerWithBestQuality()
    }
    if req.EstimatedCostUSD() > p.MaxCostUSD {
        return cheaperProvider()
    }
    return providerWithLowestP95()
}

3) Cost caps: estimate first, call later

Most teams fail here by watching price per token but ignoring total token volume.

At the gateway layer, implement:

  • Pre-call cost estimation (input + expected output)
  • Hard cap policy (degrade or block before the request executes)

Use 3 thresholds:

  • warn_threshold: alert only
  • degrade_threshold: switch model or reduce max tokens
  • block_threshold: reject sync path and enqueue async job

4) Quality guardrails: A/B is not enough

A/B testing alone misses regressions in edge cases. Use a fixed regression gate:

  1. Gold dataset for high-risk scenarios (refusal, safety, structured outputs)
  2. Weekly regression for every policy/model change
  3. Progressive rollout (1% → 10% → 50% → 100%)
  4. Automatic rollback on metric breach

Minimum metrics to track:

  • success rate and P50/P95 latency by provider/model
  • cost per 1k requests and token consumption
  • task-level quality score (hybrid auto + human)

5) Production checklist

  • L0/L1/L2 traffic tiers finalized
  • Soft-timeout + hedged requests configured
  • Circuit breakers and half-open probes configured
  • Warn/degrade/block cost caps configured
  • Gold set + weekly regression pipeline ready
  • Progressive rollout + auto rollback enabled

6) Common failure modes

Failure mode 1: price-only routing hurts critical quality

Fix: pin L0 traffic to quality-priority pools.

Failure mode 2: serial retry inflates tail latency

Fix: use soft-timeout hedging with bounded parallelism.

Failure mode 3: cost alerts come too late

Fix: estimate and gate budget at request entry.

Summary

A dual-provider Claude + OpenAI stack only works at scale when latency tiers, cost caps, and quality guardrails are first-class routing primitives.
Hard-code policy intent first, then automate execution. That is how you stay stable under real traffic.