Pricing transparency in AI services is rare. Most studios refuse to publish ranges because their numbers vary too widely, their average customer is too sophisticated to need anchoring, or, frankly, they do not want competitors knowing their pricing. We are publishing ours.
The headline ranges (US, 2026)
These are Ideas Realized planning ranges, not an industry pricing survey. Another studio may include a different team, ownership model, warranty, infrastructure scope, or change process. Compare what is actually included before comparing totals.
| Engagement type | Typical cost (USD) | Timeline | What you get |
|---|---|---|---|
| Discovery | $5,000 – $15,000 | 1-2 weeks | Written problem statement, recommended approach, build/buy reasoning, cost range, stack proposal |
| Focused first project | $25,000 – $80,000 | 4-8 weeks | One workflow, end-to-end, measurable success metric |
| AI feature in existing product | $40,000 – $150,000 | 6-12 weeks | Production-ready AI feature with eval harness and cost controls |
| Platform build | $100,000 – $400,000 | 3-6 months | Multi-workflow system, custom AI infrastructure, integrations |
| Enterprise transformation | $400,000 – $2M+ | 6-18 months | Multiple platforms, organizational change support |
| Ongoing partnership | $8,000 – $40,000 / month | Rolling | Continued development, optimization, support |
Now let us break down what moves you within those ranges.
The five scope drivers that actually move the price
Most AI projects do not blow their budgets because of bad estimating, they blow because one of these five drivers was underestimated during scoping.
1. Integration count and depth
Each integration adds engineering time and ongoing risk. Approximate ranges:
- Native API integration with a major platform (Salesforce, HubSpot, Slack, Stripe): 1-2 weeks each
- Custom integration with a smaller SaaS or in-house system: 2-4 weeks each
- Legacy system integration (older databases, mainframes, on-prem systems): 4-12 weeks each
A project with six integrations is not simply a project with two integrations multiplied by three. Authentication, rate limits, writeback, retries, sandbox access, data ownership, and failure handling make each connection different. If the scoping conversation does not inspect those details, the estimate is based on an unrealistically clean version of the project.
2. Data quality and accessibility
Data preparation often changes the estimate more than the model choice:
- Clean, structured data with stable API access is the simplest case.
- Mixed structured and unstructured records add mapping, extraction tests, and exception handling.
- Scanned documents, email chains, and production document volume require a representative evaluation set before pricing.
- Multilingual data and legacy systems add format, access, reconciliation, and ownership work that cannot be estimated responsibly from a feature list.
We will not estimate firmly until we have looked at sample data. Any partner who quotes without seeing your actual data is guessing.
3. Compliance requirements
Compliance does not just add features. It changes vendor selection, data flow, logging, retention, access controls, review cycles, documentation, and sometimes deployment geography. A SOC 2-aligned B2B workflow, a HIPAA workflow requiring a BAA, a GDPR data-residency requirement, and a FedRAMP environment need different architectures and review owners. Price the named obligation, not a generic compliance multiplier.
Compliance also constrains your vendor choices for AI providers, which can increase ongoing operating costs (private model deployments cost more than commercial APIs).
4. Real-time vs batch
Real-time AI generally needs streaming, tighter availability targets, concurrency planning, faster fallbacks, and more expensive observability than a queued or scheduled job. Batch, near-real-time, conversational, and high-concurrency systems should be estimated as different operating designs rather than percentage adders.
If your use case actually tolerates batch, embrace it. We have seen projects double in cost solely because someone said "real-time" when the actual user experience needed nothing tighter than a 30-second response.
5. Custom AI features vs orchestration
There is a large difference among orchestrating hosted models, building retrieval and tool workflows, fine-tuning an existing model, and training a domain model. Start with the least complex approach that passes a representative evaluation set. If a partner recommends fine-tuning or model training, ask what measurable failure it solves that retrieval, tools, rules, or a different hosted model cannot.
Ongoing operating costs (not just build cost)
People focus on build cost and forget operating cost. For AI systems, ongoing costs typically split into three buckets:
LLM inference costs
Model cost depends on the provider, model, input and output volume, tool calls, caching, batch eligibility, media, and retry behavior. Use a workload sample against current provider prices and set a budget alert before launch. A monthly estimate copied from another application is not a reliable baseline.
Infrastructure (hosting, observability, third-party services)
Include hosting, databases, queues, storage, observability, authentication, email or messaging, OCR, search, vector storage, and any industry platform fees. Price these from the proposed architecture and current vendor plans.
Maintenance and continued development
Maintenance depends on the system's change rate, uptime target, vendor dependencies, internal ownership, and support window. Some teams operate the system after handoff; others retain delivery support. The proposal should separate required operating work from optional feature development.
What "$50,000" actually buys (a worked example)
Concretely: what does a $50,000 first project look like? This is roughly the size of project we run most often.
Engagement shape: 6 weeks, one focused workflow, two senior engineers part-time + a project lead, fixed scope.
Typical breakdown:
- 1 week discovery (problem definition, data sample review, stack proposal): ~10% of cost
- 2 weeks design and integration architecture: ~20% of cost
- 2 weeks core build and AI integration: ~40% of cost
- 1 week testing, monitoring setup, deployment, training: ~25% of cost
- 5% buffer for edge cases (held back unless used)
What you get:
- Production-ready AI workflow handling one specific business process
- Source code in your repo
- Documentation your team can maintain from
- Eval harness with test cases
- Cost monitoring and alerts
- 2-week post-launch support included
What you do not get:
- A second workflow (separate engagement)
- Ongoing changes after the first 2 weeks (separate retainer)
- Integration with systems we did not scope upfront
This is a planning example, not a claim about a completed project or a percentage of Ideas Realized engagements. The actual team, allocation, deliverables, and support window belong in the written scope.
How to estimate your own project before talking to anyone
Before your first sales call with any studio, do this back-of-envelope:
- Identify the one workflow you would automate first. Not three. Not "AI for our company". One workflow.
- Count its integrations. Be honest. Each integration is real engineering work.
- Assess your data. Is it clean and structured? Or is it PDFs and emails?
- Establish your latency requirement. Real-time? Within an hour? Overnight?
- List your compliance constraints. SOC 2? HIPAA? Nothing specific?
For a workflow with 2-3 integrations, reasonably clean data, no special compliance, and tolerance for non-real-time response: budget $30-60k for the first project.
If compliance, unstructured data, real-time response, or integration count materially changes the architecture, do not apply a generic multiplier. Ask for a revised scope that names the added work, owner, and acceptance evidence.
If you arrive at a number that feels too high, your scope is too big, pick the most valuable single workflow and start there. If you arrive at a number that feels too low, you are missing scope drivers, pressure-test what you have not accounted for.
What to do next
If you want a real estimate against your specific project, book a 30-minute consultation. We will run the five scope drivers against your project on the call, and either give you a same-day rough range or schedule a paid discovery if the project warrants it.
If you are still in the "do I even need this" phase, our free business assessment walks through the high-level decision in about 10 minutes.