Methodology
We aggregated pricing data from 512 AI engagements delivered by 84 agencies, 37 boutiques, and 140+ senior freelancers between January 2023 and January 2026. Every project was re-coded against a common scope rubric (model integration depth, data complexity, evaluation rigor, deployment target) so pricing could be compared apples-to-apples. Confidential submissions were anonymized; pricing is expressed in 2026 USD with inflation adjustment where disclosed.
Key findings
The signals that shaped the rest of the analysis — each one backed by the full methodology in the download.
$18,500
Average AI proof-of-concept
Median POC cost rose 12% year-over-year, driven by eval tooling and observability standards that didn't exist in 2023.
$47,000
Median production MVP
Production-grade MVPs with auth, evals, and basic observability clustered tightly between $38k and $62k.
2.8x
Agency-to-freelancer premium
Full-service agencies charged 2.8x more than equivalent freelance teams for identical scope — with measurable quality and warranty differences.
43%
Of budgets hit a hidden-cost surprise
Evals, guardrails, and SOC 2 readiness were the top three line items founders forgot to budget for.
$22,000
Average RAG implementation
Retrieval-augmented generation with a real vector store, chunking strategy, and ingestion pipeline — not a weekend demo.
68%
Of projects underestimated data prep
Teams allocated 15% of budget to data work on average; actual consumption was closer to 35%.
Inside the report
A scannable chapter guide. Each chapter includes data tables, charts, and practitioner commentary in the full PDF.
Benchmark costs for agents, copilots, RAG systems, classifiers, and fine-tuned models across POC, MVP, and production tiers.
How regulated industries (healthcare, fintech, legal) price 25–60% higher than unregulated SaaS due to compliance overhead.
North American agencies command a 1.9x premium over Eastern European teams for equivalent scope, but close the gap once post-launch support is included.
We broke down the price delta by scope, support window, warranty, and observed bug rate after launch.
Evals, guardrails, observability, compliance, data prep, on-call, and LLM token spend — each modeled as a percentage of line-item scope.
Which contract levers (milestones, source code ownership, maintenance windows) vendors will concede to and which they will walk from.
Agentic workflows are compressing classifier and RAG pricing while simultaneously expanding the top end of custom-agent pricing.
Full report
All the data, charts, and appendices — no gate, no drip campaign, no retargeting pixel circus.