01
The API bill
What the provider invoices for the calls that produce this workload.
LLM cost optimization · for $10K+ monthly API spend
Stop optimizing tokens. Start optimizing accepted output.
Omev routes the repeatable share of your AI workload through its own models, checks the result and measures generation, retries and review as one business number. Keep difficult work on your current provider.
$5 free credit · 10 days · no card · Keep your current provider connected
The invoice is only the first line
A lower token price can still produce a higher operating cost.
If a cheaper draft is rejected more often or takes longer to fix, the saving disappears outside the provider dashboard. Compare the entire path from generation to work your team can actually use.
02
Retries
The same job paid for again because the first result could not ship.
03
Rework
Editor or operator time spent correcting facts, format, voice or structure.
04
Accepted output
The result that passes your rules and can move to the next production step.
LLM cost calculator
Put accepted work—not token volume—at the centre of the estimate.
Answer three questions about one repeated workload and compare the cost of work your team can actually accept.
Step 1 of 3
Start with the bill
Your starting point
How large is the AI bill you want to examine?
Pick the closest number or enter the actual monthly invoice. We will leave the share you do not route exactly where it is.
One bill · two routes
Keep the work that needs your strongest model. Move the work that repeats.
Cost optimization is not a command to downgrade everything. Preserve the current route for difficult judgement, named-model requirements and work that is expensive to get wrong. Give Omev one repeatable workload with clear rules and compare what passes.
Omev chooses between its own models and model sequences inside the routed workload. It is not a marketplace for third-party models, and it does not silently replace the rest of your AI stack.
Where the saving can come from
Five levers worth testing before a company-wide provider change.
The rate card matters. The shape of the workflow matters more. These are the parts a benchmark can turn from opinion into an operating number.
- 01
Separate work by task
Do not pay the same model premium for every job. Keep difficult reasoning on the current route and isolate the repeated work with a stable brief.
- 02
Give each task a pass condition
Define the facts, format and quality checks before comparing models. Otherwise a cheaper draft and a usable result look identical in a spreadsheet.
- 03
Repair the failed part
A full regeneration pays for the whole answer again. A task-aware workflow can check the miss and repair only what failed.
- 04
Count review time
A model saving that adds ten editor hours is not a saving. Put loaded reviewer cost beside the API line item.
- 05
Expand from measured traffic
Move a small share first. Increase it only when acceptance, review time and failure handling remain predictable in production.
From estimate to evidence
Prove one workload before you touch the rest of the stack.
Registration starts the benchmark. You bring a representative sample; we help turn the team's acceptance rules into a fair comparison and return the raw outputs with the operating math.
- 01
Choose one costly repeat
Bring a workload with meaningful volume, stable input and an output your team already knows how to approve.
You leave with: A bounded test, not a migration project
- 02
Agree what passes
Turn the team's unwritten judgement into clear checks for facts, structure, language and delivery format.
You leave with: A shared definition of accepted output
- 03
Run both routes
Use the same representative prompts on the current workflow and Omev, then record API cost, acceptance and review time.
You leave with: A like-for-like benchmark
- 04
Route a small production share
Keep the current provider connected. Expand only after real traffic confirms the economics and the operational fit.
You leave with: A decision based on production evidence
Strong first candidate
High volume. Stable input. Clear approval.
- ✓ $10K+ monthly API spend or one workload large enough to matter
- ✓ Repeatable jobs with a known brief and expected format
- ✓ A team that can label accepted, rejected and rewritten work
- ✓ A willingness to start with a measured production slice
Keep it on the current route
Low volume. Unclear outcome. Named model required.
- — Small invoices where integration effort outweighs the saving
- — One-off strategic judgement with no stable pass condition
- — Work that contractually requires a named third-party model
- — Workflows nobody can consistently approve today
Questions finance, product and engineering ask
LLM cost optimization FAQ
What does LLM cost optimization mean?
It means reducing the total cost of a production AI workflow, not just selecting the cheapest token rate. That includes which work goes to which route, how often a result is retried, how much review it needs and how many outputs are accepted without a rewrite.
How much does one million LLM tokens cost?
There is no single price. Input and output tokens are priced differently, and the rate changes by model, provider, cache policy and batch mode. For an operating decision, start from the invoice you already pay and compare the same real workload on both routes.
Are LLM costs going down?
Published token prices often fall as new models arrive, but a lower rate card does not guarantee a lower production cost. More retries, rejected drafts and reviewer time can erase the difference. Measure cost per accepted output before moving more traffic.
How expensive are LLMs to run in production?
The answer depends on volume, model mix, prompt size, output size, retries and human review. The calculator on this page begins with the monthly bill and adds the operating cost of one repeatable workload, so you can test your own scenario instead of relying on a generic token example.
Does Omev replace our current AI provider?
It does not have to. Keep difficult, reasoning-heavy or named-model work where it is. Start by routing one repeated workload to Omev and expand only when the benchmark holds up in production.
Does Omev route between third-party models?
No. Omev routes work between its own models and model sequences, then checks and repairs the result inside that route. Your team can keep its existing provider or gateway as an independent path for the work that stays outside Omev.
What is cost per accepted output?
It is the cost of generation, retries and human review divided by the outputs that meet your requirements. This makes a cheaper draft less attractive when it creates more rejected work, and makes an apparently higher rate defensible when it reduces cleanup.
How do we compare Omev with negotiated enterprise rates?
Use your actual monthly spend in the calculator, then benchmark a representative set of production prompts. The public estimate uses list rates only; the benchmark is where your discounts, acceptance criteria and reviewer time become part of the decision.
Continue the evaluation
Related tools and buying guides
How the Omev AI router works
See how a business task keeps its own rules, route, checks and repair flow.
Read more →LLM API price comparison
Compare current public input and output token rates before adding workflow cost.
Read more →How to reduce LLM API costs
A practical guide to measuring and reducing the cost behind repeated AI work.
Read more →One workload · your real prompts · both routes
Find out what an accepted result really costs.
Register with your work email and bring a representative sample. We will compare the outputs, acceptance rate, review time and cost before you move more traffic.
Benchmark My Workload →$5 free credit · 10 days · no card · No platform migration