AI Consulting Pricing: Fixed Fees, One Guarantee

AI consulting is billed either by the hour or as a fixed fee. Hourly market rates run $150–$350/hr for independent consultants, $150–$300/hr at boutique firms, $300–$500/hr at mid-tier firms, $400–$800/hr at the Big Four and $500–$1,000+/hr at MBB strategy firms (McKinsey, BCG, Bain). AIDOLS prices three transparent fixed-fee tiers: Sprint at $15K–$25K for a diagnostic of 2–3 weeks, Build at $75K–$150K for a production AI deployment in 90 days, and Scale at $25K/month for ongoing engineering support. AIDOLS estimates that Big Four AI programs commonly run $500K–$2M+ over 6–12 months on time-and-materials.

AIDOLS prices every engagement as a fixed fee because hourly billing rewards the wrong behavior: the longer a consultant takes, the more they earn. Fixed fees align incentives — we win when the system ships fast and works, not when the timesheet grows. Each tier carries a defined scope, a defined deliverable, and a defined timeline. If we run over, we eat the cost. That is the discipline that lets us promise a first measurable result in 2–3 weeks and a production AI system in 90 days on the Build tier.

If the deployed system does not generate ROI exceeding its fee within 90 days, the fee is refunded in full. The ROI metric is agreed in writing at kickoff — typically a cost-reduction figure, a revenue-uplift figure, or a cycle-time reduction tied to a verifiable business KPI — and the 90 days are counted from production deployment. Build: the full fee is refunded. Scale tier: prorated refund. Sprint tier excluded (assessment, not deployment).

The Sprint tier is a diagnostic of 2–3 weeks with five outputs: a data-and-infrastructure audit, a prioritized opportunity map with ROI estimates per use case, a target-state reference architecture, a 90-day implementation roadmap with named owners, and an executive readout deck. It answers one question: where does AI move the needle in your operations, and in what order should you build it? Pricing is fixed at $15K–$25K depending on operational complexity and the number of business units in scope.

The Build tier ships a production AI system in 90 days with an end-to-end engineering team: data engineering, model development, MLOps deployment, integration into existing systems, and a post-launch evaluation harness. The fixed fee of $75K–$150K covers all engineering hours, all infrastructure setup, and one production deployment. Scope is locked at kickoff with a written statement of work; out-of-scope changes are handled by a transparent change-order process. The Build tier carries the ROI guarantee.

The Scale tier is an ongoing engineering retainer at $25K/month for organizations operating two or more production AI systems shipped through a prior Build engagement. Coverage includes model retraining, monitoring, drift detection, incident response, evaluation harness updates, and one new feature release per month. The retainer assumes a 12-month minimum term and is billed monthly. It suits organizations that would rather outsource MLOps than hire a senior MLOps engineer.

Big Four firms (Deloitte, PwC, EY, KPMG) bill $400–$800/hr on time-and-materials. By AIDOLS estimates, Big Four AI programs commonly run $500K–$2M+ over 6–12 months, and the deliverable is typically a strategy document plus a roadmap rather than a deployed AI system. AIDOLS prices a production deployment as a fixed-fee Build engagement of 90 days at $75K–$150K, with a deployed production system as the deliverable and the ROI guarantee behind it.

Independent AI consultants typically bill $150–$350/hr and work solo, which works for a discrete model-development task but breaks down for a production deployment that needs data engineering, MLOps, integration, and evaluation in parallel. At those rates, the several hundred engineering hours a production deployment takes can cost as much as a fixed-fee Build, with the added risk that one person cannot cover MLOps, security review, and integration at the same time. The Build tier prices a full team at $75K–$150K fixed.

Cloud infrastructure costs (AWS, GCP, Azure compute, model API spend) are billed directly to the client account and are not included in the AIDOLS fixed fee. Third-party model API spend (OpenAI, Anthropic, Google) is also passed through at cost with no markup. Everything else — engineering hours, project management, architecture, MLOps setup, deployment, evaluation harness, knowledge transfer — is included in the tier price. AIDOLS publishes an estimated infrastructure cost range in every Build statement of work so there are no surprises.

Sprint engagements bill 100% on kickoff. Build engagements bill 50% on kickoff and 50% on production deployment; the ROI guarantee covers the full fee. Scale engagements bill monthly in advance with a 12-month minimum term and 60-day cancellation notice. All invoices are net-30 and paid by wire or ACH. AIDOLS does not require multi-year prepayment, does not charge setup fees beyond the published tier price, and does not bill for sales meetings, scoping calls, or proposal preparation.

AI consulting pricing is driven by six factors: scope (the number of use cases and business units), data readiness, integration complexity, regulatory overhead, infrastructure and model requirements, and whether you need ongoing operation. Time-and-materials firms leave these variables open-ended, which is why, by AIDOLS estimates, Big Four AI programs run $500K–$2M+ over 6–12 months. AIDOLS prices all six into a fixed fee up front — Sprint at $15K–$25K, Build at $75K–$150K with the ROI guarantee, and Scale at $25K/month.