Knowledge / Software & AI

Software & AI · Pricing & scoping · Written and maintained by Haink’s AI adoption team · Updated August 2026 · 8 min read

How Much Does It Cost to Build a Custom AI or LLM Solution?

A custom AI or LLM project typically costs from roughly $15,000–$50,000 for a focused proof of concept or a single retrieval-augmented (RAG) assistant, $50,000–$250,000+ for a production system with integrations and evaluation, and more for multi-product or custom machine-learning programs. The price is driven less by the model itself than by problem complexity, data readiness, integration depth, accuracy requirements, and whether the system runs on cloud APIs or your own GPUs.

Because most teams over-estimate the model work and under-estimate the surrounding engineering, the cost ranges below are best read as scoping guidance, not quotes. The reliable way to control cost is to start with a narrow, high-value use case that reaches working results in weeks, then expand with evidence.

What drives the cost of a custom AI project

Five factors account for most of the variation in price:

• Problem complexity. A RAG chatbot over existing documents is far cheaper than a novel computer-vision, optimization or signal-processing system that needs custom modeling.
• Data readiness. Clean, labeled, accessible data lowers cost; messy, scattered or unlabeled data adds a data-engineering phase that can rival the AI work itself.
• Integration depth. A standalone tool is cheap; embedding AI into live ERP, CRM and workflows — with authentication, audit trails and error handling — is where real cost accrues.
• Accuracy and risk. High-stakes use cases (finance, healthcare, legal) need more evaluation, guardrails and human-in-the-loop review, which adds engineering and ongoing oversight.
• Inference footprint. Token volume and latency targets determine whether you pay per API call or operate your own GPUs — and that choice dominates the running cost.

Typical cost ranges by project type

Approximate 2026 ranges for scoping. Actual figures depend on the factors above, and on region and team seniority.

Build cost vs run cost — and why the token price is not your problem

One-time build cost is half the picture, and the other half is smaller than the industry's obsession with token pricing suggests. Take a $150,000 build amortised over three years — $50,000 a year — and set the inference bill beside it, at a blended frontier-tier price of about $3.64 per million tokens:

Below roughly 1.5 billion tokens a year, inference is under a tenth of what the system costs you. Most document workloads never come close: a site processing a thousand documents a day at a few thousand tokens each lands in the low hundreds of millions a year. The token price a team spends a week negotiating is, for them, a rounding error against the engineers who built the thing.

The number that does move is people — the data engineering, the evaluation, the integration, and someone owning the system after handover. Which is the same conclusion from the other direction as the deployment question: self-hosting rarely wins on inference cost either, and is bought for residency, change control or an air gap instead. That arithmetic is in on-premises versus cloud LLM deployment, where the crossover sits near 340 tokens per second sustained around the clock.

Hidden costs most buyers miss

• Data engineering to make data usable — routinely the largest single line, and on genuinely messy data it can match or exceed the AI work itself. If nobody can tell you where the documents live and in what condition, assume this dominates the first phase.
• Evaluation — building the test sets and scoring that let you ship confidently and catch regressions.
• Monitoring and retraining — models drift, so production systems need ongoing oversight, not just a launch.
• Change management — getting people to actually adopt the new workflow. A system nobody uses has a cost and no return, which makes this the only hidden cost that can take the payback to zero rather than merely reducing it.
• Guardrails and security — prompt-injection defenses, access control and audit trails for anything customer-facing.

How to control the cost

1. Run a short discovery phase to scope the problem and audit the data before committing budget.
2. Pick one narrow, high-value use case and reach working results in weeks.
3. Use proprietary model APIs where they win on accuracy and speed; switch to open-weight models when volume or data residency justifies it.
4. Invest in evaluation early so you ship on evidence instead of over-building.
5. Phase the roadmap so each stage delivers value and informs the next.

Related Resources

• LLM Applications & RAG
• AI & Machine Learning
• How Much Does AI Adoption Cost? — the portfolio view rather than one project

Frequently Asked Questions

How much does it cost to build a custom AI solution?

Roughly $15k–$50k for a proof of concept or single RAG assistant, $50k–$150k for a production LLM application with integrations, and $80k–$250k+ for custom ML systems or multi-product programs. Cost is driven mainly by data readiness and integration depth, not the model. Most engagements reach first working results in 2–4 weeks.

Why is custom AI so variable in price?

Because the cost lives in the surrounding engineering — data preparation, integration, evaluation, guardrails and monitoring — which varies enormously between a clean standalone tool and a high-stakes system embedded in live business workflows.

Is it cheaper to use cloud AI APIs or run our own models?

For low or bursty usage, cloud APIs are cheaper and faster to start. For steady high-volume inference, or when data must stay private, running open-weight models on your own GPUs usually lowers cost per request and total cost of ownership.

What is the most expensive part of an AI project?

People, and specifically not the model. Data engineering on messy data, deep integration into existing systems, and the evaluation and monitoring needed for high-accuracy use cases are the biggest cost drivers. Inference is usually a rounding error: against a $150,000 build amortised over three years, a workload of 500 million tokens a year costs about $1,820 to serve, or 3.5% of the annualised total.

How much of the running cost is the AI model itself?

Less than most budgets assume. At a blended frontier-tier price near $3.64 per million tokens, and against a $150,000 build amortised over three years, inference stays under a tenth of annualised cost up to roughly 1.5 billion tokens a year. Most document workloads never approach that — a thousand documents a day at a few thousand tokens each is in the low hundreds of millions. Run cost only starts to dominate above about ten billion tokens a year, or where latency targets force dedicated capacity that idles outside working hours.

Why do document automation projects quote from $150,000 when a production LLM app can cost $50,000?

Because the phrase covers two different purchases. A grounded support assistant is the lower figure. A document layer in a process whose output can be contested — by a regulator, an auditor, a customer or a counterparty — needs a deterministic check layer that is specified and tested, an evidence chain retained per record, exception routing a person can work, and integration into a system of record that was not designed to receive it. None of that is model work, all of it is engineering, and it is why those engagements start at $150,000 over four to six months rather than finishing there.

Can we start small to control budget?

Yes — the recommended approach is a discovery phase plus a narrow first use case that reaches working results in weeks, so you validate value before committing to the full roadmap.