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True Cost Calculator
See your real 3-year cost. Build vs. buy vs. hybrid, side by side.
ROI Reality Check
Find out when (or if) your AI project will pay off.
Cost Benchmarks
Real numbers by phase, company size, and deployment model.
Vendor Comparison
Build vs buy framework with cost transparency scores.
Free Programs & Grants
Government & Nonprofit Resources
MEP Manufacturing Extension Partnership
Free or subsidized consulting for manufacturers under 500 employees. AI readiness assessments, technology planning, digital transformation guidance.
SBIR/STTR Federal R&D Grants
Federal grants for small business R&D, including AI/ML projects. Phase I awards $150K–$275K. No equity given up.
SBA SCORE AI Mentors
Free mentorship from experienced business professionals. Some SCORE chapters now have AI-specific mentors who've done enterprise deployments.
NIST AI Risk Management Framework
The definitive playbook for responsible AI deployment. Use it to ask vendors hard questions about risk, bias, and safety.
Free Cloud Credits
Cloud AI Credits for Startups & SMBs
Google Cloud for Startups
$1K–$10K in free AI/ML compute credits. Easiest application process of the three major clouds.
Apply →AWS Activate
Up to $100K in free credits for qualified startups. Includes SageMaker, Bedrock, and all AI services.
Apply →Azure for Startups (Founders Hub)
Up to $150K in credits including Azure OpenAI Service access. Includes mentorship and technical support.
Apply →Open-Source Tools
Build Without Vendor Lock-In
Hugging Face
Free model hosting, free inference API for small models, massive open model library. The antidote to vendor lock-in.
huggingface.co →MLflow
Free experiment tracking and model registry. The "MLOps" vendors charge $2K–$10K/month for what MLflow does for free.
mlflow.org →LangSmith / LangFuse
Free LLM observability and monitoring. If your vendor isn't giving you this level of insight, you're flying blind.
langfuse.com →NVIDIA NeMo Guardrails
Free, open-source AI guardrails. Prevent hallucinations, off-topic outputs, and safety violations in production.
GitHub →Research & Reports
Read Before You Spend
RAND: Root Causes of AI Project Failure
The definitive study on why AI projects fail. Based on rigorous analysis, not vendor surveys. Free PDF.
Read the report →Stanford HAI AI Index Report
Annual report with real benchmarks, adoption data, and cost trends. The best antidote to vendor hype. Free.
Latest report →Microsoft AI Readiness Assessment
Free self-assessment tool. Understand your organization's readiness before talking to any vendor.
Take the assessment →Gartner Reports (Free at Your Library)
Don't pay $5K for a Gartner report. Most public and university libraries have Gartner access through business databases. Ask your librarian.
🚀 What To Do Monday Morning
- Run a $0 AI readiness audit (2 hours). Inventory your top 5 manual processes. For each: What data exists? How clean? What would a 30% efficiency gain save monthly? If nothing justifies $50K+/year in savings, use off-the-shelf SaaS instead.
- Get your free MEP consultation + cloud credits. Call your local MEP center for a free tech assessment. Apply for Google / AWS credits simultaneously. Expert guidance + free compute, zero cost.
- Build a 3-year TCO model before any vendor call. Use our calculator or the benchmarks page. Multiply vendor quotes by 4–6×. If the 3-year TCO still shows positive ROI, proceed. If not, wait — costs drop 30–40% annually.
Terminology
AI Cost Glossary
Speak the language before vendor calls. These are the terms that actually matter for cost decisions.
- TCO (Total Cost of Ownership)
- The real cost over time — not just the vendor quote. What this entire site is about.
- Inference
- Running a trained model on new data. This is the ongoing cost you pay forever, not just once.
- Fine-tuning
- Adapting a pre-trained model to your data. $5K–$80K per round. Not a one-time thing.
- RAG (Retrieval-Augmented Generation)
- Feeding your documents to an LLM at query time. Cheapest way to make AI "know" your business.
- MLOps
- Infrastructure to deploy, monitor, retrain models. If someone says they don't need this, they haven't deployed anything.
- Model Drift
- When accuracy degrades because the real world changes. Why AI is never "done." Budget for quarterly retraining.
- Token
- The unit LLMs process (~¾ of a word). How you get billed. A 10-page document ≈ 4,000 tokens.
- Hallucination
- When AI confidently generates false information. The risk that kills trust. Mitigation costs real money.
- Guardrails
- Rules/filters preventing harmful outputs. Non-negotiable for production. Open-source options exist.
- Agentic AI
- AI that acts autonomously — calls APIs, executes code, makes decisions. Higher capability, dramatically higher risk and cost.