Competitive comparison

LLM cost optimization that does not sacrifice reliability

LLMWise balances cost, latency, and success rate with explicit goals, then validates impact before rollout through replay lab.

Start FreeRun traffic replayBack to overview
Teams switch because
Need lower cost without random quality regressions
Teams switch because
Need confidence before changing model defaults
Teams switch because
Need ongoing cost governance as traffic changes
Manual Cost Tuning vs LLMWise
CapabilityManual Cost TuningLLMWise
Cost-focused auto routingVariesBuilt-in
Replay impact simulationRareBuilt-in
Policy max cost guardrailRareBuilt-in
Alert on recommendation driftNoBuilt-in
OpenAI-compatible integrationVariesYes

Migration path in 15 minutes

  1. Keep your OpenAI-style request payloads.
  2. Switch API base URL and auth key.
  3. Set routing policy for cost, latency, and reliability.
  4. Run replay lab, then evaluate and ship with snapshots.
OpenAI-compatible request
POST /api/v1/chat
{
  "model": "auto",
  "optimization_goal": "cost",
  "messages": [{"role": "user", "content": "..." }],
  "stream": true
}

Common questions

How quickly can I see cost impact?
Run replay on recent traffic to estimate gains, then evaluate and apply routing policy with guarded rollout.
Can I avoid low-reliability cheap models?
Yes. Set minimum success-rate and latency guardrails while optimizing for cost.
Related pages
OpenRouterPortkeyLiteLLMCloudflare AI Gateway