How does self-hosting change the math?

Self-hosting Llama 3.3 70B on AWS p4d ($32/hr) costs ~$16/M tokens at full utilization. DeepSeek V3 API is $0.30/M tokens. Self-hosting wins only at 1B+ tokens/month consistent.

Should I switch to a smaller model?

Probably yes, after testing. Mini / Haiku tier handles 60-70% of production tasks adequately at 5-10x lower cost. Test on your specific workload, then route only failures to the larger model.

What about prompt caching and batch discounts?

Prompt caching saves 50-90% on cached input tokens (OpenAI: 50%; Anthropic: up to 90% with 5-minute cache). Batch API: 50% off async jobs. Combined, can drop bills 70-80% for cache-friendly workloads.

Is this calculation accurate at scale?

Public-rate-card calculators are accurate within 10-15% for typical workloads. Variance comes from prompt-cache hit rates, batch-API usage, and rate-limit retry overhead.

How does this compare to GPT-4o or Claude Opus 4?

GPT-4o, Claude Opus 4, and Gemini 2.5 Pro are roughly comparable on quality for general tasks; their pricing differs by 30-50% so test on your specific workload before locking in.

What hidden costs am I missing?

Output tokens (3-5x input cost), rate-limit retry overhead (20-40% extra), failed-request charges, and the engineering time to maintain the integration. Budget 1.5-2x the headline rate.

AI & Prompt Tools · Free tool

AI Consulting ROI Calculator

Estimate the ROI of an AI consulting engagement before signing. Inputs: project fee, hours saved per week, hourly rate, ongoing API cost.

Updated June 2026

Consulting project fee ($)Hours saved / week (post-launch)Across all impacted team membersLoaded hourly rate ($)Salary + benefits + overheadImplementation duration (weeks)No savings during this rampOngoing software / API cost ($/mo)LLM API, vendor SaaS, observabilityDiscount rate %SMB hurdle ~10–15%

12-month projection

Annual labor savings (full-rate): $66,300
Annual ops / API cost: $4,800
Year-1 net (after project fee + ramp): $11,300
Payback period: 9 months

3-year outlook

3-year cumulative net cash flow: $134,300
3-year NPV @ 10% discount: $107,305

Verdict

Strong positive ROI

Projected NPV exceeds 50% of the project fee. Most consulting engagements at this profile pay back.

Export:

Heuristic projection. Doesn’t price implementation risk, quality uplift, or revenue acceleration. Validate with a smaller pilot before committing on the full project fee.

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What it does

Estimate the ROI of an AI consulting engagement before signing. Inputs: project fee, hours saved per week, hourly rate, ongoing API cost. AI cost and capability tradeoffs have stratified into clear tiers (frontier, mid-tier, fast-and-cheap, open-source).

Prompt-engineering and tool-selection often deliver bigger quality gains than switching models. The gap between “rough estimate” and “defensible number” is exactly where good tooling earns its keep — the math is reproducible, but knowing which inputs matter and what the result means is half the work.

Always benchmark on YOUR actual workload, not synthetic benchmarks. Models that score high on MMLU may underperform on your specific task. A common pitfall: skipping prompt-cache eligibility analysis. Treat the tool’s output as a starting point and validate against authoritative sources for any consequential decision.

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How to use it

Enter your inputs (the values relevant to ai consulting roi calculator).
Pick the relevant options or scenarios.
Read the calculated outputs — primary number plus context.
Adjust inputs to test different scenarios side by side.
Cross-check critical numbers against authoritative sources before relying on the result.

When to use this tool

Production cost forecasting based on traffic projections.
Prompt-engineering optimization to reduce token consumption.
Vendor selection between OpenAI, Anthropic, Google, and open-source.
Pre-launch budget planning for an LLM-powered feature.

When not to use it

When the workload is unique enough that public benchmarks don’t apply.
For non-frontier image, video, or audio model pricing (those use per-asset billing).
When you have negotiated enterprise pricing not reflected in public rate cards.
For hyper-bursty traffic where peak load determines architecture, not average.

Common use cases

A freelancers using AI in client work working through ai consulting roi calculator for a real decision.
A product managers scoping AI capabilities working through ai consulting roi calculator for a real decision.
A indie creators experimenting with AI tools working through ai consulting roi calculator for a real decision.
A ML engineers optimizing inference costs working through ai consulting roi calculator for a real decision.

Frequently asked questions

How does self-hosting change the math?: Self-hosting Llama 3.3 70B on AWS p4d ($32/hr) costs ~$16/M tokens at full utilization. DeepSeek V3 API is $0.30/M tokens. Self-hosting wins only at 1B+ tokens/month consistent.
Should I switch to a smaller model?: Probably yes, after testing. Mini / Haiku tier handles 60-70% of production tasks adequately at 5-10x lower cost. Test on your specific workload, then route only failures to the larger model.
What about prompt caching and batch discounts?: Prompt caching saves 50-90% on cached input tokens (OpenAI: 50%; Anthropic: up to 90% with 5-minute cache). Batch API: 50% off async jobs. Combined, can drop bills 70-80% for cache-friendly workloads.
Is this calculation accurate at scale?: Public-rate-card calculators are accurate within 10-15% for typical workloads. Variance comes from prompt-cache hit rates, batch-API usage, and rate-limit retry overhead.
How does this compare to GPT-4o or Claude Opus 4?: GPT-4o, Claude Opus 4, and Gemini 2.5 Pro are roughly comparable on quality for general tasks; their pricing differs by 30-50% so test on your specific workload before locking in.
What hidden costs am I missing?: Output tokens (3-5x input cost), rate-limit retry overhead (20-40% extra), failed-request charges, and the engineering time to maintain the integration. Budget 1.5-2x the headline rate.

Show the math + sources

Formula

Annual labor savings = hours_saved_per_week × 52 × loaded_hourly_rate. Year-1 net = ((52 − implementation_weeks) / 52) × annual_savings − annual_ops_cost − project_fee. Payback (months) = first month where cumulative net cash flow ≥ 0, given an implementation ramp during which inflows are zero. 3-year NPV = Σ CF_t / (1+d)^t for t=1..3, where CF_1 includes the project fee and CF_2..3 are net annual savings.

What this assumes

Linear ramp from project fee paid upfront. Hours saved are realized at the loaded hourly rate (salary + benefits + overhead). Ongoing ops cost (LLM API, vendor SaaS, observability) is a fixed monthly recurring. No quality uplift, revenue uplift, or implementation risk premium is priced — those are real but project-specific and easily double-counted with hours-saved. Discount rate defaults to 10% (typical SMB hurdle); override per your cost of capital.

Sources

Methodology last verified: 2026-05-03

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