What AI consulting actually costs in 2026

Why this guide exists

Buyers evaluating AI consulting in 2026 are caught between two failure modes. On one side: the firm that quotes a number on the first call without understanding the work. On the other: the firm that refuses to discuss pricing until after a six-week scoping engagement that itself costs money. Both are signals the buyer should walk.

This guide is the conversation we’d rather have on the first call. It lays out the actual cost ranges for AI consulting work in 2026, the line items inside those ranges, and the variables that move the price up or down. The numbers are ours, calibrated against the engagements we’ve shipped and the proposals we’ve seen lose to firms that quoted differently. They will not match every firm in the market. They will be in the right ballpark for any firm shipping comparable work.

The four cost buckets

Every AI consulting engagement reduces, at the line-item level, to four buckets:

Discovery. Scoping the work, mapping opportunities, drafting the architecture, sizing the build.
Build. Designing, implementing, integrating, and shipping the first production capability.
Operations. Running the system after launch — monitoring, model updates, capacity, incident response.
Infrastructure. The compute, storage, and networking the system actually runs on.

Buyers tend to focus on the build number because it’s the biggest line item on the proposal. The buyers who get burned tend to focus on the build number only and discover the other three buckets after the fact. Read all four.

Discovery: what it costs and what you should get

A serious Discovery Engagement for an enterprise or mid-market AI initiative runs $15,000 to $40,000 and takes two to four weeks.

What that should buy you:

Interviews with the people who actually do the work — not just the leadership sponsor. Eight to fifteen interviews is typical.
A prioritized opportunity map: which AI use cases are highest-value, which are easiest to ship, which should be deferred, which should never be built.
A technical architecture sketch for the top one or two opportunities — including data flows, integration points, deployment model, and model selection.
A realistic investment range for the build phase. Not a fixed bid (no honest firm gives one before discovery). A range with the variables called out.
An honest read on whether the organization is ready to build. Some are not, and a discovery that says so is worth more than one that pretends otherwise.

What should make you suspicious: discoveries that take longer than four weeks for a single-business-unit scope, discoveries that price above $50k without unusual scope, discoveries that produce a slide deck without an architecture sketch, discoveries that conclude the customer needs a multi-year transformation engagement (a tell — the firm has decided the answer before doing the work).

What should make you suspicious in the other direction: firms that “skip discovery” or fold it into the build for free. Discovery is real work; a firm that gives it away is either subsidizing it from the build margin (which means the build is overpriced) or doing it badly.

Build: where the money actually goes

This is the bucket with the widest range. A first-production-capability build — not the full vision, the first capability that real users use — typically falls in one of three brackets:

$50,000 to $150,000. A focused capability built on top of public APIs or a private AI cloud, with limited integrations to existing systems. Typical examples: an internal-search RAG application, a document-processing pipeline, a single-purpose customer-facing assistant. Two to eight weeks to ship. One to three engineers.
$150,000 to $500,000. A more ambitious capability with multiple integrations, custom model selection, retrieval over proprietary data, and meaningful UI work. Eight to sixteen weeks. Three to five engineers. This is where most serious mid-market and enterprise initial engagements actually land.
$500,000 to $2,000,000+. Multi-capability platforms, deep integration with legacy systems (twenty-year-old ERP, custom EHR, Magento, mainframe-adjacent), or workloads with hard regulatory requirements that drive substantial compliance and procurement work. Sixteen weeks and up. Five-plus engineers, often with sub-specialists.

What drives the price up within a bracket:

Integration with old or custom systems. Modern SaaS APIs are easy. Twenty-year-old on-prem ERPs, custom Magento installations, undocumented payment integrations, and case-management systems built in 2007 are hard. Hard integrations are not a small percentage adjustment; they can double a project.
Regulatory and compliance scope. HIPAA, SOC 2 alignment, state-level privacy laws, federal procurement requirements, ITAR — each adds real engineering and documentation work.
Data quality work. “We have all the data” almost always means “we have all the data, in twelve different systems, with twelve different schemas, none of which were designed for what we’re now asking them to do.” Cleaning that up is project work.
Stakeholder count. A project with one decision-maker ships faster than the same project with five. This is the variable buyers underestimate most.

What drives the price down:

A clear, narrow first capability. Shipping one capability and iterating beats shipping the full vision in one go. Always.
Modern integration surfaces. Salesforce, HubSpot, Snowflake, Slack, recent Microsoft 365 — these are easy. If your stack is mostly modern, your build is mostly cheaper.
Data already in shape. A team that has invested in clean data infrastructure before the AI project gets a meaningful discount on the AI project.

Operations: the bucket buyers forget

Most AI engagements that fail did not fail at the build phase. They failed at the operations phase — typically because operations weren’t scoped at all, the firm walked away after launch, and the system silently degraded over months.

Realistic ongoing operations costs for a shipped AI capability:

Self-managed by the customer: $0 in vendor fees, plus the loaded cost of the internal engineer who owns it (typically 0.25 to 1.0 FTE depending on scope). Real cost: $40,000 to $200,000/year.
Managed by the consulting firm: $3,000 to $15,000/month for typical mid-market scope. Includes monitoring, incident response, model evaluations, retrieval tuning, integration maintenance, and quarterly capability reviews. Higher for regulated workloads or high-availability requirements.

The build-and-walk-away pattern — where the firm ships the system and disappears — is the single biggest reason AI initiatives fail in production. Buyers should treat a firm that doesn’t offer ongoing operations as a flag. The team that built the system is the team that should run it. If they’re not willing to, ask why.

Infrastructure: where the architectural choice shows up in the bill

This is the bucket where the deployment model from your architecture decision shows up as a recurring number:

Public AI APIs (OpenAI, Anthropic, etc.): Pay per token. Costs scale linearly with usage. A small internal tool might cost $200/month; a customer-facing application with serious volume might cost $20,000+/month. The unpredictability is the issue, not the absolute number.
Hyperscaler-managed services (Azure OpenAI, Bedrock, Vertex): Same per-token economics as public APIs, with the addition of tenancy and contractual differences that often justify the cost for procurement-sensitive workloads.
Private AI cloud (Skyview or comparable): Capacity-based pricing. Typically $2,000 to $20,000/month depending on workload volume, model size, and tenancy. The cost doesn’t move when end-user behavior does, which most finance teams strongly prefer.
On-premises: CapEx-heavy upfront ($150k to $1M+ for a real GPU build-out, depending on scale), with ongoing facility, power, and operations costs. Pays back at high steady-state volume; doesn’t pay back at low or bursty volume.

A buyer who skips this line item in their proposal evaluation is going to be surprised. We’ve seen quarterly bills from public-API providers exceed the original build cost when usage scaled. The architecture is a business decision because the architecture choice shows up in the infrastructure bill, every month, forever.

What a complete first-year cost looks like

For a serious mid-market AI engagement — discovery + build + first year of operations + infrastructure — the realistic complete first-year cost is:

Lower bound: ~$120,000 (small discovery, focused build, self-managed operations, public API or modest private cloud capacity).
Typical: $300,000 to $700,000.
Upper bound: $1,500,000+ (ambitious multi-capability scope, regulated industry, multiple legacy integrations, managed operations).

These numbers do not match the Big Four “AI transformation” proposals, which typically start at $1M and run multi-year. They are not supposed to. The Big Four engagement model is a different product, sold to different buyers, with different success criteria. We compare them honestly in Big Four vs. boutique AI consulting.

Where Skyview lands

We tend to land in the typical bracket: $300k to $700k for the first year, with the spread inside that range driven by the variables above. We are happy to discuss specifics on a call, including whether a smaller scope or a phased approach is the right answer for your situation.

What we will not do: quote a number on the first call. The number is a function of the work, and the work has to be understood first. A discovery is the cheapest way to find out whether the project is real and what it costs — and a discovery is itself a small fraction of the total. If a firm gives you a fixed number before discovery, ask what they think they’re pricing.

If you’d rather skip the proposal pitch and have a 30-minute discovery call to compare your situation against these ranges, we’ll tell you where you actually fall — and if your number is meaningfully different from the typical, we’ll tell you why.