Segment tasks
Group prompts by risk, ambiguity, output type, latency need, and customer-visible failure cost.
LLM routing for production teams
Route every LLM request to the cheapest model that can win.
Keep frontier fallback for the work that needs it. Stop paying frontier prices for everything else.
ProfitMaxx benchmarks your real prompts, quality bar, retries, and fallback policy so routing can target 99% AI quality at 10% cost.
- Quality
- 99%
- Cost
- 10%
- Review
- Free
Routing before migration
Do not replace your stack. Reprice the request path.
LLM routing should not be a random model switch. It needs task classes, quality gates, fallback rules, latency constraints, and rollout thresholds. ProfitMaxx gives the benchmark map before the production change.
How the routing benchmark works
Keep the best model where it matters, move the rest.
Test model paths
Compare frontier APIs, lower-cost models, retries, validators, and fallbacks against real examples.
Define routing rules
Leave with a traffic split, escalation path, and validation plan for a 99/10 rollout.
LLM routing
model routing
LLM router
route LLM requests
Best fit
For teams already feeling model spend in margin.
The routing review is most useful when your team has enough volume to prove the pattern and enough quality examples to know what can move.
- $10k+ monthly AI spend or 100k+ monthly AI requests.
- Repeatable prompts, product copilots, support workflows, or agents.
- Current use of OpenAI, Anthropic, Gemini, open models, or a mix.
- Clear examples of good output and unacceptable failure modes.
Free qualified review
Find the route mix for 99% quality at 10% cost.
Send enough workload context for us to qualify whether an LLM routing benchmark is worth doing. Same-day follow-up for strong fits.
Best fit: $10k+/mo AI spend, measurable quality bar, and repeatable traffic.