tenantplaneBook a demo Private inference has separate implementation and operating costs. A low hourly GPU rate does not include the engineering needed to install a model, connect an application, test the result, and maintain it.
Begin with the deliverable. An inference endpoint, a working application connection, a durable job workflow, and a fully operated service are different buying units.
Separate the engagement fees
Tenantplane separates implementation from the infrastructure and operating budget:
| Engagement | USD price | What it buys |
|---|---|---|
| Readiness sprint | $2,500 | Boundary, compatibility risks, test plan, cost range, and go / no-go scope |
| Deployment-only starter | $7,500 | One model, one qualified environment, synchronous endpoint, HTTP example, tests, and handover |
| Private workflow implementation | $12,500–$20,000 | Serving plus one existing application’s synchronous caller path, acceptance, and handover |
| Optional maintenance | $1,000–$2,500/month | A written business-hours allowance for updates and requalification |
The readiness fee is separate, with no automatic credit. A sprint followed by workflow implementation totals $15,000–$22,500 before infrastructure and other charges. An already-qualified starter need not include a sprint. Read the complete scope and payment assumptions.
Use compute prices as one input, not the whole quote
On September 9, 2026, Hugging Face’s hosted AWS endpoint table lists one NVIDIA L4 at $0.80/hour and one A100 80GB at $2.50/hour. These are hosted endpoint rates, not quotes to run the same service in your cloud account, and do not establish whether your model or workload fits. Hugging Face Inference Endpoints pricing.
Check the billing unit, instance count, region, billed setup/idle time, and service conditions. A standardized 730-hour month is a planning convention. Request a quote for the actual environment before committing spend.
Three worked starter scenarios
These illustrations use the $7,500 deployment-only starter, not the larger integrated workflow. They exclude a readiness sprint. Compute uses the dated hosted rates above; storage, networking, and operating labor are explicit invented planning assumptions.
| Input | L4, 100 billed hours | L4, 730 billed hours | A100 80GB, 730 billed hours |
|---|---|---|---|
| Implementation, one time | $7,500 | $7,500 | $7,500 |
| Monthly compute | $80 | $584 | $1,825 |
| Monthly storage/network allowance | $50 | $100 | $150 |
| Monthly operator effort | 4 hours × $125 | 4 hours × $125 | 8 hours × $125 |
| Total recurring / month | $630 | $1,184 | $2,975 |
| First-year total | $15,060 | $21,708 | $43,200 |
No redundant replica, staging environment, unusual transfer, tax, or unspecified license fee is included. These are arithmetic scenarios, not deployment results, infrastructure recommendations, or availability guarantees.
For the lower end of the integrated-workflow range, replace $7,500 with $12,500 and add a $2,500 readiness sprint. At the same illustrative L4 monthly cost of $1,184, the first-year total becomes $29,208. That additional fee buys a different implementation scope; it is not an infrastructure surcharge.
Change the assumptions for your environment
The interactive cost calculator starts from a worked example. You can replace implementation, readiness, monthly compute, storage/networking, operating effort, and other one-time costs. It calculates locally and exports your assumptions; it does not send them to a server.
Monthly cost =
compute + storage/networking + operations
First-year cost =
implementation + readiness + other one-time costs
+ 12 × monthly costInclude required software or support charges in the relevant allowance. If a maintenance agreement covers work already included in your operator estimate, remove the overlap. Separate incremental cash costs from allocated staff time; do not count the same expense twice.
For owned hardware, include purchase, installation, power, support, replacement, and operating effort. Hardware already purchased can lower new cash spend without eliminating lifecycle costs.
Compare the same result
Before comparing self-managed inference with a hosted service, align the model-quality target, data boundary, request mix, integration, support coverage, and availability requirements.
Measure the representative workload before selecting capacity. Record input sizes, output sizes, concurrency, resource usage, failures, and the relevant response-time target. Do not convert “fits in memory” into an unmeasured throughput promise.
The useful buying question is not “How much is a GPU hour?” It is “What will it cost to deliver and operate this application path within our required boundary?”
Use the private VPC guide to establish that boundary, inspect the actual engineering evidence, or discuss your deployment.