As of June 5, 2026, a DeepInfra private deployment GPU-hour break-even calculator should begin with route selection, not a generic inference-hosting ranking. The worksheet separates shared per-token API calls, private deployment GPU-hours, autoscaling settings, B200 GPU instance hours, retry and evaluation overhead, and Makefun editorial QA before any buyer compares vendors.
The short answer: shared inference is easier to model when workload is bursty or token volume is uncertain. Private deployments need enough predictable work, custom weights, isolation, batching, or latency value to justify running GPUs. B200 GPU instances are a different row for teams that need SSH or container control and can manage lifecycle risk.
DeepInfra official source snapshot
Publisher refreshed the official DeepInfra pricing page, private model deployment overview, custom LLM deployment docs, GPU instances page, and GPU instance docs. Treat those pages as the current source of truth because model rows, GPU availability, and deployment terms can change.
| Cost route | DeepInfra row to model | Makefun caveat |
|---|---|---|
| Shared inference API | Input tokens, cached input tokens where listed, output tokens, selected model, retries, eval calls, and prompt-pruning work. | Do not compare raw token rates with GPU-hour rows until the same workload is normalized. |
| Private deployment | GPU type, num_gpus, min_instances, max_instances, max_batch_size, active hours, idle hours, weekly billing, and scale-up availability. | GPU-hour billing can continue while capacity is running, so idle time is a real row. |
| B200 GPU instance | 1x, 2x, 4x, or 8x B200 instance hours, minute billing, container setup, termination workflow, and storage or data-loss controls. | This is not the same as a managed private deployment endpoint. |
| Makefun operations | Source refresh, OCR or transcript cleanup, support triage, media metadata QA, classification, post-publication checks, and editor review. | Vendor bills are only part of the publication and support workflow cost. |
Shared token API worksheet
shared_api_monthly = input_tokens * input_rate + cached_input_tokens * cached_rate + output_tokens * output_rate + retries + eval_calls + prompt_cleanup + Makefun_QA
- Use the current DeepInfra pricing page for the exact model row at publication time.
- Separate cached input from normal input only when the chosen model row lists a cached input rate.
- Add retries, source extraction failures, evaluation calls, and editorial review instead of treating the API bill as the whole workflow.
- Use shared routing when demand is spiky, custom weights are not required, or the team is still validating prompt volume.
Private deployment GPU-hour worksheet
private_deployment_monthly = gpu_hour_rate * num_gpus * (active_hours + idle_hours) * deployed_instances + setup_QA + retries + evals + publication_work
- Model min_instances as the floor that can create idle spend before traffic arrives.
- Model max_instances as an availability and queue-control setting, not a free scaling promise.
- Add max_batch_size and queue tolerance because batching can change latency and utilization assumptions.
- Keep weekly billing, GPU limits, and scale-up availability caveats visible in the worksheet.
B200 GPU instance budget worksheet
The current DeepInfra GPU instance page lists B200 rows for 1x, 2x, 4x, and 8x instance shapes. Use the page at publication time for exact hourly prices, then add minute billing behavior, setup time, shutdown discipline, data persistence, and any container or storage work. A B200 instance can fit training, fine-tuning, custom environments, or large batch inference, but it needs a lifecycle owner.
| Scenario | Likely route | Decision check |
|---|---|---|
| Daily SEO source refresh | Shared inference or small private deployment after volume stabilizes. | Use private deployment only if repeated load and QA value justify GPU uptime. |
| Support triage classifier | Shared API while categories change; private deployment after steady traffic and custom weights. | Track idle hours and false-positive review cost. |
| OCR or transcript cleanup | Shared API for bursty jobs; B200 instance for controlled batch windows. | Shutdown discipline matters more than a raw hourly price. |
| Media metadata QA | Shared API or private endpoint depending on queue size and latency tolerance. | Add reviewer time and rejected-media handling. |
Break-even logic
A practical break-even row compares the same task, same month, same prompt shape, and same quality gate. First estimate shared token cost for the workload. Then estimate private deployment GPU-hours including active and idle time. Finally add B200 instance hours only when direct container control or custom environment work is part of the requirement.
break_even_volume = private_gpu_hour_monthly_cost / shared_api_cost_per_successful_job. This is only a planning formula. It should be recalculated when the selected model, cached-token row, GPU type, min_instances, max_instances, max_batch_size, or QA pass rate changes.
Same-use comparison rows
Use adjacent Makefun pages as comparison lanes, not same-intent duplicates: Hugging Face Inference Providers Routed Request Cost Calculator for routed multi-provider inference, Replicate Deployments Hardware Autoscaling Cost Calculator for deployment autoscaling, Runpod Serverless GPU Worker Idle Timeout Cost Calculator and Cerebrium Serverless GPU Cold Start Cost Calculator for GPU worker cold-start and idle rows, Nebius Token Factory Batch Inference Cost Calculator for batch inference, Groq Batch Flex Prompt Cache Cost Calculator for direct batch and cache economics, and Requesty AI Gateway Cache Routing Cost Calculator for gateway routing.
Direct-provider cache and batch alternatives still need their own same-workload rows. Start with Gemini Batch API Context Cache Cost Calculator and Claude Batch API Prompt Cache Cost Calculator when a team is deciding between a provider-native path and DeepInfra-managed routing. Do not turn these links into a cheapest, fastest, best, safest, or most reliable claim without current same-scenario evidence.
Shutdown and spend controls
- Name the owner for private deployment scaling changes and B200 termination.
- Keep idle-hour and failed-run rows separate from successful inference cost.
- Record the GPU type, num_gpus, min_instances, max_instances, max_batch_size, and selected model before a forecast is approved.
- Refresh official pricing and docs before publication or procurement reuse.
- Route any security or compliance claim to primary docs and legal review instead of implying dedicated infrastructure solves it.
Publisher checklist
- Target URL is absent before publish and WordPress search has no same-intent DeepInfra page.
- Official DeepInfra pricing, private deployment, custom LLM, GPU instance pricing, and GPU instance docs are refreshed on publication day.
- Final body links point only to canonical Makefun URLs returning 200.
- Featured media is a site-owned permanent worksheet PNG, not media ID 7037, a temporary MakeFun URL, a hotlink, a copied UI, or a provider logo.
- Post-publish checks verify REST publish status, blog category id 2, Yoast fields, canonical URL, cache-busted URL, sitemap, and body links before any public success notice.
FAQ
How should a DeepInfra private deployment calculator start? Start with the route: shared per-token inference, private deployment GPU-hours, or B200 GPU instances. Then add token volume, selected model, GPU type, num_gpus, min_instances, max_instances, active hours, idle hours, max_batch_size, retries, evals, QA, and shutdown discipline.
When can DeepInfra private deployments beat shared token pricing? Only when enough load, custom weights, isolation, latency, or batching value justifies GPU uptime. The worksheet should compare the same workload and same time window instead of comparing raw token and GPU-hour rates directly.
Are B200 GPU instances the same as private deployments? No. B200 GPU instances give SSH or container control and are useful for training, fine-tuning, custom environments, or large batch inference. Private deployments expose managed model endpoints with deployment settings and autoscaling. Both need lifecycle controls to avoid idle spend.
