FOR COMPANIES SPENDING $1M+ A YEAR ON AI
YOUR AI BILL
KEEPS
GOING.
Inference calls. Growing context. Agent retries.
Let’s see what it costs to finish the work.
Up to 76% savings achieved for existing Happiest Labs customers.
SCROLL TO FOLLOW THE BILL01 / THE WORK ADDS UP
ONE TASK.
MANY
MODEL CALLS.
A completed task can include long prompts, tool calls, retries, and follow-ups. The cost of one model call only tells part of the story.
More useful work is the goal.
More spend doesn’t have to be.
02 / CHANGE HOW THE WORK RUNS
KEEP THE
WORK.
CUT THE
OVERHEAD.
The Happiest Labs local AI platform combines our own inference engine, runtime, and orchestration harness. We assess where it can reduce repeated inference work and move suitable tasks off metered APIs.
- Reuse stable context.
- Run calculations with tools.
- Evaluate suitable local inference.
03 / MEASURE THE DIFFERENCE
YOUR WORK.
YOUR
BENCHMARK.
Compare your current setup with the local AI platform on the same tasks. Measure cost, successful completion, and latency against requirements your team defines.
See the assessmentThe receipt is an illustration.
Your assessment makes it specific.
FROM CUSTOMER RESULTS TO YOUR WORKLOAD.
LOWER COST.
START WITH
THE EVIDENCE.
Happiest Labs has achieved up to 76% savings for existing customers. Your opportunity starts with the models, requests, and workflows behind your bill.
Request an inference cost assessmentFor US companies spending $1M+ a year on AI · By Happiest Labs
THE INFERENCE COST ASSESSMENT
What does a successful task cost?
Start with one production workflow. Agree on what good looks like. Then measure the difference.
Establish your baseline.
Review a representative billing period and usage by workflow: models, input and output tokens, cache use, retries, and effective rates.
OUTPUTA cost breakdown tied to completed work.
Test the same work.
Compare your current setup with the local AI platform on representative tasks. Set success criteria, end-to-end latency limits, and expected concurrency before testing.
OUTPUTComparable results for cost, quality, and speed.
Build the savings case.
Count retries, failures, fallback calls, and operating costs. Separate measured test results from projected savings and check which bills can actually fall.
OUTPUTA scoped recommendation with costs and tradeoffs.
THE PRIMARY COST METRICCost per successful task
Total cost of all attempts ÷ tasks that meet your agreed quality and latency criteria.
Read the assessment methodologyBefore we assess your stack.
How can the local AI platform lower inference costs?
Happiest Labs brings together its own inference engine, runtime, and orchestration harness. The harness coordinates model calls, context, and tools. Reusing stable context, reducing repeated model work, and using deterministic tools for calculations are opportunities to evaluate. Suitable workflows may also move to local inference. The assessment tests which changes meet your requirements at a lower cost.
Will our company save 76%?
Happiest Labs has achieved up to 76% savings for existing customers. That result is not a forecast for your company. We need to evaluate your workflow, current costs, required quality, and operating constraints before estimating your savings.
What do you need to assess our inference costs?
Start with your current inference setup and one high-spend workflow. For a technical evaluation, we agree on a representative billing and usage sample, task success criteria, and latency requirements. Share only a high-level summary in the request form; billing exports or task samples can follow through an agreed channel.
Who is the assessment for?
US companies spending $1M or more annually on AI, including finance, technology, and security leaders. Company-wide spend qualifies the engagement; the evaluation focuses on the cost of specific inference workloads. Inference Savings is an initiative by Happiest Labs.