AI Development Cost Calculator

Estimate AI development cost from a scope you can defend.

Use RaftLabs' current first-phase starting prices to plan a proof of concept, RAG or LLM integration, AI agent, or generative AI workflow. The estimate becomes useful after you name one workflow, its data, integrations, evaluation method, and operating constraints. It is a planning range, not an instant or binding quote.

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Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.

The brief

Start with what is not working.

Good software decisions begin with the constraint, not a list of features or a preferred technology.

01

Are broad AI cost ranges too wide to support a budget decision?

02

Are vendor estimates difficult to compare because they assume different data, integration, and production work?

Plain answer

AI development cost depends on the workflow, data, integrations, evaluation, and operating controls. RaftLabs' current starting points are $9,500 for a proof of concept, $15,000 for focused RAG or LLM integration, and $20,000 for one AI agent workflow. These are planning figures, not fixed quotes.

A cost range is only useful when its assumptions survive the meeting.

One estimate assumes clean data and one tool. Another includes interfaces, integrations, evaluation, and support. Price one named workflow so those differences become visible.

Current planning points

starting price for a bounded AI proof of concept
$9.5K
Feasibility and quality test, not a production promise
starting price for focused RAG or LLM integration
$15K
One agreed knowledge or product workflow
starting price for one AI agent workflow
$20K
Tools, controls, evaluation, and handover scoped together

These are RaftLabs' public starting prices as of September 2026, not market averages or fixed quotes. Scope can increase them. Third-party charges remain separate unless a proposal says otherwise.

This estimator fits a team that can name one workflow and wants a first budget before detailed scoping.

Use discovery first when the problem, users, data, or acceptable output are still contested.

A fit
01

A buyer can describe who uses the workflow, what enters it, and what useful output looks like.

02

Relevant data sources and system owners can be identified, even if access still needs preparation.

03

The team needs a planning range and wants the proposal to state assumptions and exclusions.

Not a fit
01

The request is an organisation-wide AI strategy with no first workflow selected.

02

A single number is expected without revealing data, integration, security, or operating constraints.

03

The estimate must include unknown usage charges or compliance certification without separate review.

Cost model

What a defensible AI estimate needs to include

  • 01

    Workflow and product surface

    Define the user, trigger, input, output, review step, and interface. Similar models can support workflows with very different product and error costs.
  • 02

    Data and model path

    Inspect source quality, permissions, retrieval, labels, model options, and examples. Expose unknown data work as an assumption or investigation.
  • 03

    Integrations and controls

    Map APIs, authentication, tool permissions, limits, human approval, fallbacks, and tool-call records. Connections add behaviour outside the model.
  • 04

    Evaluation and operation

    State the test set, unacceptable errors, latency, cost, monitoring, feedback, incident ownership, and release approach.

Which starting range matches the first decision?

Proof of concept vs production workflow

Proof of conceptProduction workflow
DecisionCan the approach meet a defined quality bar?Can users rely on the workflow in normal operation?
DataRepresentative sample with known limitationsApproved sources, permissions, updates, and recovery paths
IntegrationMocked or narrow connection when possibleReal authentication, error handling, limits, and observability
EvaluationTest set and feasibility resultRelease checks, monitoring, feedback, and regression coverage
Planning pointAI proof of concept from $9.5KRAG/LLM from $15K; agent workflow from $20K

Choose a proof of concept to test feasibility. Choose production work when integration, safe failure, and operation are the larger questions.

Current price and timeline anchors

A proof of concept starts at $9,500 and usually takes 3 to 6 weeks. Focused RAG starts at $15,000 and takes 4 to 8 weeks; focused LLM integration starts at $15,000 and takes 6 to 10 weeks. An agent workflow starts at $20,000 and takes 4 to 8 weeks. A generative AI workflow starts at $25,000 and commonly takes 6 to 10 weeks. Scope changes after data, integrations, controls, and review are inspected.

Scoping

From AI idea to a priceable first phase

The estimate becomes narrower as assumptions turn into named decisions.

  1. Decision 1
    01

    Choose one workflow

    Name the user, trigger, input, output, and result. Separate the smallest useful workflow from the broader roadmap.

  2. Decision 2
    02

    Set data and evaluation boundaries

    Identify approved sources, privacy constraints, examples, failure cases, and the quality bar. Record data work that still needs investigation.

  3. Decision 3
    03

    Map integration and operating cost

    List systems, permissions, human review, usage, observability, and support. Separate build work from variable third-party charges.

  4. Decision 4
    04

    Price the first phase

    Choose a proof-of-concept or production boundary. State deliverables, acceptance, assumptions, exclusions, dependencies, timeline, and price.

What makes the estimate unreliable?

The use case is still a category
Build an AI assistant is not a workflow. Name the user, information, action, and review point before comparing estimates.
Data readiness is assumed
Availability does not prove quality, permission, structure, or coverage. Sample the real sources and expose repair work before promising production behaviour.
Demo quality is treated as acceptance
A persuasive example can hide common failures. Define representative cases, unacceptable errors, latency, and operating cost before deciding that the result is ready.
Third-party spend is mixed into build cost
Model, speech, image, vector, cloud, data, and security services often bill by usage or tier. Show them as assumptions so future volume does not invalidate the build estimate.

Planning range

Start at $9,500 when feasibility is the biggest unknown.

A focused production integration starts at $15,000, an AI agent workflow at $20,000, and a first generative AI workflow at $25,000.

Monthly embedded AI engineering is a different buying model and is planned at $6,000 to $6,500 per person. Choose it when an existing team can direct an evolving backlog.

Starting investment

AI PoC from $9.5K

A scoped proof of concept is the smallest published starting point. Final price and timeline follow the workflow, data, integration, evaluation, security, and operating review.

Assumptions stay visible

The proposal states the data, integrations, environments, client dependencies, third-party charges, and exclusions used to form the price.

The first phase has an evidence boundary

The scope names what will be delivered, how it will be reviewed, and which production claims the phase is not intended to prove.

Continue planning

Compare the cost to the delivery path

Common questions

RaftLabs' current starting points are $9,500 for an AI proof of concept, $15,000 for focused RAG or LLM integration, $20,000 for one AI agent workflow, and $25,000 for a first generative AI workflow. These are planning figures. Data condition, integrations, evaluation, interface work, security, and operating controls determine the proposal.

A useful estimate states the workflow, data sources, model or retrieval approach, interfaces, integrations, evaluation, deployment path, observability, human review, and handover expected in the phase. The proposal should also name assumptions, client dependencies, exclusions, acceptance evidence, and which third-party charges remain outside the build fee.

Cost moves when source data needs repair, permissions are complex, several systems must be integrated, model output needs strict evaluation, the workflow can take consequential actions, or production requires new monitoring and review tools. A smaller model is not automatically a cheaper project if the surrounding product and operating work remains large.

Not by default in these starting prices. Model tokens, speech or image APIs, vector databases, cloud services, licensed data, security products, and high-volume testing can create variable third-party costs. We identify likely services and usage assumptions during scoping so the build fee and expected operating spend are not confused.

A fixed proposal becomes defensible after one workflow, data boundary, integrations, evaluation method, delivery environment, client dependencies, and acceptance conditions are known. If feasibility is uncertain, start with a $9,500 proof of concept. Its purpose is to resolve the largest technical or quality uncertainty before pricing a production phase.

Work with us

Bring one AI workflow and the number you need to defend.

We will review the data, integrations, evaluation, and production boundary, then tell you which starting range applies and what remains unknown.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.