AI Development Cost Breakdown: What Startups Should Budget in 2026

The most common question we receive from startup founders is some version of: "How much will this cost?" The second most common question, usually asked after the first has been answered, is: "Why does it cost that?"

Both questions deserve real answers. This post provides them.

The figures here draw on ITviec's 2024–2025 Vietnam Developer Salary Report, US Bureau of Labor Statistics occupational data for 2025, GoodFirms' global AI project research covering more than 400 project engagements, and ODSEA's own project history across more than 60 AI-powered product builds. Where we cite a figure, we cite the source. Where we give a range, we explain what determines where in that range a given project falls.

The honest conclusion: AI development costs in 2026 have dropped dramatically at the MVP and lean application level compared to 2023, primarily due to AI-augmented development tools that compress engineering hours per feature. They have not dropped proportionally at the production system level, where the cost drivers are complexity, compliance, and reliability engineering rather than raw coding hours.

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Part 1: The Four Stages and Why Stage Matters More Than Total Budget

Asking "how much does AI development cost?" without specifying the stage is like asking "how much does a car cost?" The range is real and determined by what you are actually buying.

The four relevant stages for startup AI development are:

Stage 1: Discovery — Defining what to build, producing a technical specification, validating that the proposed solution is technically feasible, and estimating scope.

Stage 2: Lean Application / MVP — A working, deployable product with core features that validates the business hypothesis. Not production-grade in every dimension, but real enough to test with real users.

Stage 3: Production System — A system designed for reliability, scalability, and security at production scale. Includes monitoring, error handling, security controls, and the operational practices required to run it sustainably.

Stage 4: Enterprise Application — A system built to enterprise-grade standards: formal compliance, SLAs, audit trails, integration with enterprise identity systems, and procurement-ready documentation.

Most startup founders ask for Stage 3 pricing when they need Stage 2. And many founders who need Stage 3 are building toward enterprise requirements that actually require Stage 4. Getting the stage right is the most important pricing decision.

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Part 2: Stage 1 — Discovery ($0–$2,000)

What discovery does

A proper discovery process takes three to four weeks and produces: a technical specification, a system architecture diagram, wireframes for the key user flows, a data model, an estimate of engineering scope, a risk assessment, and a recommended stack.

Discovery is the most valuable thing you can buy for an AI project. The reason: AI projects have higher architecture-level risk than traditional software projects. The choice of LLM, the agent orchestration approach, the context management strategy, and the evaluation framework all have downstream consequences that are expensive to reverse. Decisions made in discovery week two are much cheaper to change than decisions discovered in implementation week ten.

What discovery costs

Free: Many agencies offer a free 60-90 minute discovery call that produces a rough proposal and ballpark estimate. This is a sales conversation, not a discovery process. It does not produce a technical specification.

$500–$1,500: A constrained discovery process with one dedicated session and a written output (usually a requirements document and rough estimate). Appropriate for projects where the scope is well-defined and the technical risk is low.

$1,500–$5,000: A full discovery engagement with multiple sessions, a complete technical specification, architecture diagrams, wireframes, and a detailed estimate. Appropriate for projects with significant scope complexity or technical uncertainty. This cost is typically deducted from the project fee if you proceed.

ODSEA's discovery engagements are in the $2,000–$3,500 range for typical Gulf market AI projects, deducted from project cost on proceeding.

The cost of skipping discovery

GoodFirms' research shows 53.8% scope creep across software projects. The majority of scope creep is attributable to inadequate discovery. A project that adds $30,000 in scope creep after month two has effectively lost the cost of a thorough discovery process many times over.

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Part 3: Stage 2 — Lean Application / MVP ($3,000–$25,000)

The 2026 MVP cost compression

MVP development costs have dropped dramatically since 2023. The primary driver: AI-augmented development tools. Cursor, GitHub Copilot, and custom agent configurations have compressed the engineering hours required per feature by 40–70% for certain categories of work. A senior developer using AI tooling in 2026 produces output equivalent to what a team of two to three produced in 2022.

This compression is real but uneven. It applies most strongly to:

• CRUD interface development (forms, lists, admin panels)
• Boilerplate setup (authentication flows, database migrations, API scaffolding)
• Standard integrations (payment gateways, email providers, SMS)
• Test generation for well-understood logic

It applies less to:

• Novel AI feature design (the architecture choices for LLM integration remain complex)
• Security-critical code (AI-generated code needs careful review for security vulnerabilities)
• Domain-specific business logic (the AI tools do not know your domain)
• Performance optimization at scale

What an MVP costs by type

$3,000–$8,000: Simple lean application (2–3 weeks) A focused single-purpose application with 2-4 core features, basic authentication, a simple database, and deployment to a managed platform. No mobile app. No advanced AI features. Examples: a lead capture and management tool, a simple scheduling application, a basic content management system with an AI writing assistant.

The "under $5,000" range is achievable for genuinely simple applications with narrow scope. It requires a team using AI-augmented workflows, a well-defined specification before work begins, and a client who makes decisions quickly.

$8,000–$20,000: Core AI-powered application (3–5 weeks) A focused AI-powered application with a defined set of features: a customer support chatbot integrated with your knowledge base, an AI document processing pipeline with a review interface, a recommendation system for a specific domain. Includes proper authentication, error handling, a working deployment, and basic monitoring.

$20,000–$50,000: Multi-feature platform MVP (6–12 weeks) A platform with multiple user types, complex data relationships, AI features integrated across the core product flows, and enough reliability engineering to be used by real customers in a soft launch. Includes staging and production environments, automated testing, and operational monitoring.

The tooling stack that enables this cost structure

The operational cost of a well-built MVP on this stack at early user volumes is below $300/month. This is a step-change from 2022 when equivalent infrastructure required $1,000–$3,000/month in cloud spend.

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Part 4: Stage 3 — Production System ($10,000–$80,000)

What production-grade means

A production system differs from an MVP in several dimensions:

Reliability engineering. Error handling that prevents cascading failures. Retry logic for external dependencies. Graceful degradation when an LLM API is unavailable. SLA-relevant uptime monitoring and alerting.

Security controls. Input validation at all boundaries. Authentication and authorization tested against OWASP Top 10. Secrets management through a dedicated system (not environment variables embedded in configuration files). Penetration testing.

Operational observability. Structured logging, metrics, and tracing. Dashboards that enable the engineering team to diagnose production issues without reading raw logs. Alerting for anomalous behavior.

Scale design. Database query optimization, connection pooling, caching layers, and CDN configuration that ensures the application performs under the load it is expected to handle at production.

Compliance. For Gulf market applications: data residency verification, PDPL compliance review, payment card industry compliance for payment processing, and sector-specific requirements (QCB for fintech, HIPAA-equivalent for health, etc.).

Production system cost ranges

$10,000–$30,000: Single-product production application A focused production application built to enterprise-adjacent quality standards. Includes all the reliability, security, and observability engineering described above, without the complexity of multiple product lines or enterprise integration requirements.

$30,000–$80,000: Multi-feature production platform A platform with multiple user types, multiple product lines, or significant integration complexity. Includes formal security review, compliance documentation, load testing, and operational runbooks.

The GoodFirms average for AI project engagements — $36,000 over 4.5 months — aligns with this tier. These are mid-market production applications, not MVPs.

What drives cost within this range

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Part 5: Stage 4 — Enterprise Application ($50,000–$300,000+)

Enterprise applications have a different cost structure. The engineering cost is only part of the total. Additional costs include:

Compliance and certification: SOC 2 Type II audit ($30,000–$60,000), ISO 27001 certification, sector-specific regulatory compliance.

Security review and penetration testing: Enterprise procurement requires documented security testing. A full penetration test from a qualified firm costs $10,000–$40,000 depending on application complexity.

Integration with enterprise systems: SAP, Salesforce, Oracle, Active Directory, and similar enterprise systems have complex integration requirements. Each enterprise integration adds $10,000–$50,000 to the project cost.

Formal documentation: Enterprise procurement requires technical documentation, user manuals, operational runbooks, and compliance evidence packages. This is 10–20% of the engineering cost.

Legal and contract review: Enterprise contracts require legal review of the development agreement, IP assignment, liability provisions, and SLA terms. Budget $5,000–$15,000 for legal fees.

Enterprise AI applications for Gulf government or large enterprise clients are typically in the $150,000–$500,000 range for initial builds, with ongoing costs of $20,000–$80,000 per year.

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Part 6: Ongoing Costs After Launch

The build cost is a one-time payment. Ongoing costs are perpetual. Many founders underestimate ongoing costs when planning their financial model.

Infrastructure costs at scale

Development and maintenance costs

Ongoing development ($1,000–$5,000/month): A live product requires ongoing development. New features, bug fixes, performance improvements, dependency updates, and the ongoing evolution of AI model integrations all require engineering time. Budget 1–2 engineer-weeks per month for a typical production application.

LLM API costs: For applications with meaningful LLM usage, API costs scale with usage in ways that can be significant. At current pricing for Claude Sonnet or GPT-4o, a typical user interaction that involves 2,000 input tokens and 500 output tokens costs approximately $0.003–$0.006. At 10,000 daily active users each making 10 AI interactions per day, this is $300–$600/day — $9,000–$18,000/month. Budget LLM API costs carefully before launch.

Monitoring and security: At production scale, add: Sentry error tracking ($26–$80/month), uptime monitoring ($10–$30/month), annual penetration test ($10,000–$20,000), and ongoing security advisory ($500–$2,000/month for high-compliance applications).

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Part 7: The Make vs. Buy Decision

Many founders ask whether they should build AI capabilities in-house or buy a SaaS product that provides AI features.

Buy (SaaS) makes sense when:

• The AI capability is generic and a SaaS product does it well
• The business differentiation is not in the AI feature itself
• The usage volume is low and SaaS per-seat pricing is favorable
• Time to market is critical and building would take too long

Build makes sense when:

• The AI capability IS the product differentiation
• The data the AI processes is proprietary or sensitive and should not leave your infrastructure
• Usage volume makes SaaS pricing uneconomical at scale
• The specific domain knowledge required for the AI feature is not available in general-purpose SaaS products

A useful rule: if a competitor can have the same AI capability by signing up for the same SaaS product, it is not a moat. If the AI capability requires your proprietary data, your domain-specific training, or your custom architecture, it is a potential moat.

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Part 8: What ODSEA's Pricing Looks Like vs. Traditional Agencies

Traditional agency pricing for AI development in 2026:

• US agency (20 person team): $200–$300/hour, typical project: $150,000–$500,000
• UK/EU agency: $120–$200/hour, typical project: $80,000–$250,000
• India outsourcing (large firm): $40–$80/hour, typical project: $50,000–$150,000 but with higher coordination overhead

ODSEA pricing:

• Vietnam-based team with Gulf market expertise
• Fixed-fee, milestone-based (not hourly billing)
• Lean application: $5,000–$25,000 depending on scope
• Production system: $25,000–$80,000
• Ongoing development: $2,000–$5,000/month retainer

The fixed-fee model is important: it aligns our incentives with yours. We want to scope correctly at discovery, not bill more hours through scope creep. Our contracts include milestone-based payment with defined acceptance criteria at each milestone.

Why Vietnam-based pricing enables the lean application range

ITviec data puts the loaded cost of a mid-level Vietnam developer at approximately $2,200–$2,500/month. A senior developer runs $3,500–$5,000/month loaded. Compare to BLS 2025 data showing US software engineer fully-loaded cost of $14,000–$18,000/month.

A five-person Vietnam team building for six weeks costs approximately $35,000–$45,000 in direct labor. The equivalent US team costs $180,000–$270,000 for the same six weeks. This is not a quality trade-off when the Vietnam team is using identical tools, identical frameworks, and has been specifically selected for Gulf market experience.

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The Number to Remember

The single most useful number for startup AI development budgeting in 2026:

A focused lean application with a well-defined scope built by an AI-augmented team: $5,000–$15,000 in four to six weeks.

Everything else — the production hardening, the compliance work, the scale engineering, the enterprise integration — is additive on top of that foundation. Get the lean application right, validate the business hypothesis with real users, and then invest in production-grade infrastructure based on demonstrated demand.

The founders who waste money on AI development are not the ones who underspend on the first build. They are the ones who spend $150,000 on a production system before they know whether anyone wants the product.

Talk to ODSEA about your budget and what you can build →

AI Development Cost Breakdown: What Startups Should Budget in 2026

The most common question we receive from startup founders is some version of: "How much will this cost?" The second most common question, usually asked after the first has been answered, is: "Why does it cost that?"

Both questions deserve real answers. This post provides them.

The figures here draw on ITviec's 2024-2025 Vietnam Developer Salary Report, US Bureau of Labor Statistics occupational data for 2025, GoodFirms' global AI project research covering more than 400 project engagements, and ODSEA's own project history across more than 60 AI-powered product builds. Where we cite a figure, we cite the source. Where we give a range, we explain what determines where in that range a given project falls.

The honest conclusion: AI development costs in 2026 have dropped dramatically at the MVP and lean application level compared to 2023, primarily due to AI-augmented development tools that compress engineering hours per feature. They have not dropped proportionally at the production system level, where the cost drivers are complexity, compliance, and reliability engineering rather than raw coding hours.

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Part 1: The Four Stages and Why Stage Matters More Than Total Budget

Asking "how much does AI development cost?" without specifying the stage is like asking "how much does a car cost?" The range is real and determined by what you are actually buying.

The four relevant stages for startup AI development:

Stage 1: Discovery and Technical Scoping What you are buying: clarity on what to build, confirmation that it is buildable, and a specification detailed enough to estimate the other stages accurately.

Stage 2: Lean Application (MVP) What you are buying: the smallest working version of your product that real users can use, with sufficient quality to gather meaningful feedback.

Stage 3: Production System What you are buying: a system with the reliability, security, observability, and scalability required for a product that users depend on.

Stage 4: Ongoing Maintenance and Iteration What you are buying: a team that keeps the system running, fixes bugs, adds features, and prevents technical debt from compounding.

Most cost comparisons confuse these stages or collapse them into a single number. A $50,000 quote can be a well-scoped Lean Application build or an under-scoped attempt at a Production System. Understanding which stage you are buying is prerequisite to evaluating whether the price is fair.

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Part 2: Discovery and Scoping — $2,000 to $8,000

Discovery is the most consistently undervalued stage by founders and the most consistently valuable by the engineers who have lived through skipped discovery.

A proper discovery phase produces:

• A written problem definition with the specific user scenario being addressed
• A technical feasibility assessment confirming that the proposed approach is achievable within the proposed constraints
• An architecture proposal with component choices and rationale
• A scope document with acceptance criteria for each major feature
• A cost and timeline estimate for the subsequent stages, with stated assumptions

The cost range of $2,000–$8,000 reflects scope: a focused single-product discovery for a well-defined problem sits at the low end; a multi-product discovery involving market research, technical investigation of novel integrations, and multi-stakeholder alignment sits at the high end.

What skipping discovery costs: GoodFirms' research documents that 53.8% of software projects experience scope creep — cost overruns attributable to requirements that were unclear or incomplete at the start. A discovery phase that eliminates scope ambiguity for $5,000 is insurance against a 50%+ probability of a materially more expensive outcome. By expected value, it is the highest-return investment in the entire development budget.

The agencies that offer free discovery: Some agencies bundle discovery into the initial engagement without a separate charge. This is sometimes a legitimate service model; sometimes it means discovery is not actually happening — the agency is making assumptions to close the deal faster. Ask to see the discovery output: the architecture proposal, the scope document, the acceptance criteria. If those artifacts do not exist after "discovery," it did not happen.

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Part 3: Lean Application (MVP) — $3,000 to $25,000

The lean application range is the most compressed it has ever been, and the compression is entirely attributable to AI-augmented development tooling.

In 2022, a functional AI-powered web application — user authentication, a core feature set, a basic admin interface, a database, a deployment pipeline — required approximately 400–600 engineering hours at a loaded team cost of $8,000–$18,000 for a Vietnam-based team. The low end was achievable only with a simple feature set and no AI integration complexity.

In 2026, the equivalent scope requires approximately 120–200 engineering hours, because the boilerplate that previously occupied 50–60% of development time — authentication scaffolding, CRUD API patterns, standard UI components, testing utilities, deployment configuration — is now generated by AI-augmented tools and verified rather than hand-written. Cursor and GitHub Copilot compress the authoring time; the engineer's time shifts toward review, architecture, and integration logic.

The loaded cost of a senior full-stack developer in Vietnam in 2026 is approximately $1,700–$2,400 per month per ITviec data. At 22 working days per month and 8 hours per day, that is $9.60–$13.60 per hour loaded. A 120-hour lean application build costs approximately $1,150–$1,630 in direct developer time. Adding project management overhead (15%), QA (20%), and agency margin (30–40%), a fair market price for a simple lean application is $3,000–$5,000.

A lean application with meaningful AI integration — an LLM-powered feature set, an agent workflow, a document processing pipeline — requires additional integration work that does not compress as dramatically with AI tooling, because the integration logic is specific to the use case. Add $3,000–$8,000 for AI-specific integration at the lean application stage.

Lean Application Cost Ranges by Type

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Part 4: Production System — $25,000 to $150,000+

The production system level is where AI development costs have not compressed proportionally to the lean application level. The cost drivers here are not coding hours — they are engineering discipline.

A production system differs from a lean application in these dimensions:

Reliability engineering: Retry logic, circuit breakers, graceful degradation, disaster recovery. A lean application that goes down loses a prospective user. A production system that goes down loses paying customers and triggers SLA penalties.

Observability: Structured logging, distributed tracing, alerting, dashboards. Understanding what your system is doing at any given moment costs real engineering time to build and maintain.

Security: Authentication hardening, authorization at the data layer (row-level security, not just API-level), secrets management, penetration testing, audit logging for compliance.

Scalability: Load testing, database query optimization, caching strategies, CDN configuration, rate limiting. A lean application that works for 100 users may fail structurally at 10,000.

Compliance: For regulated industries (fintech, health tech, legal tech), compliance engineering adds 30–50% to the base production system cost. KYC/AML integration, HIPAA-compliant data handling, audit trails for financial transactions — these are engineering problems with real costs.

Production System Cost Ranges by Scope

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Part 5: Tool Costs — The Infrastructure That Runs Your Product

A production AI application has a fixed tooling cost that operates independently of development fees. These are monthly ongoing costs that continue as long as the product runs.

Infrastructure (Vercel + Supabase — standard ODSEA stack):

• Vercel: $0 (Hobby) → $20/month (Pro, required for production) → $150/month (Enterprise for advanced features)
• Supabase: $0 (Free, limited) → $25/month (Pro, includes 8GB database, 250GB bandwidth) → $599/month (Team, required for advanced RLS and custom domains)

For most lean applications: $45/month total infrastructure cost.

AI model API costs:

• OpenAI GPT-4o: $2.50 per 1M input tokens, $10 per 1M output tokens
• Anthropic Claude Opus: $15 per 1M input tokens, $75 per 1M output tokens
• Anthropic Claude Sonnet (mid-tier): $3 per 1M input tokens, $15 per 1M output tokens

For a lean application with moderate AI usage (10,000 GPT-4o API calls per month, ~500 tokens average): approximately $15/month in API costs.

Development tooling (if the team continues using AI-augmented tools post-launch):

• Cursor Pro: $20/month per developer
• GitHub Copilot: $19/month per developer

Monitoring and observability:

• Sentry (error tracking): Free → $26/month
• Posthog (product analytics): Free → $450/month based on events

Total monthly operating cost for a lean application: $50–$150/month at low traffic. Scales with usage.

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Part 6: The Salary Benchmark — DIY vs. Agency

The make-vs-buy decision for AI development deserves a rigorous cost comparison. Here are the real numbers.

Option A: Hire in-house (US)

Per BLS 2025 data, the median US salary for a software developer is approximately $130,000 per year. For a senior developer with AI specialization in a major market, expect $160,000–$220,000. Fully loaded (benefits, equity, equipment, recruiting amortized over 2-year tenure, management overhead): multiply by 1.4–1.6x.

A single senior US AI developer: $224,000–$352,000 per year fully loaded. To build a production AI system, you typically need a minimum of two developers, a product manager, and QA coverage — putting the minimum viable in-house team at $800,000–$1,200,000 per year.

Option B: Hire remotely (Vietnam)

Per ITviec 2024-2025 data, a senior full-stack developer in Ho Chi Minh City with AI specialization earns $2,800–$4,000/month base salary. Fully loaded with employer social insurance contributions (10.5%), equipment, office allocation, and management: $3,500–$5,000/month.

A two-developer plus QA team in Vietnam: approximately $100,000–$150,000 per year fully loaded.

Option C: Agency engagement (Vietnam-based)

Agency pricing builds in team flexibility (surge capacity for complex sprints), project management, QA, deployment infrastructure, and the ability to scale engagement up or down without hiring cycles. For a defined-scope project, agencies offer price certainty that salary-based hiring does not.

ODSEA pricing model:

• Lean Application: $3,000–$25,000 (fixed-fee by scope)
• Production System: $25,000–$150,000 (milestone-based)
• Ongoing Maintenance Retainer: $2,000–$6,000/month

The crossover point where hiring in-house is more cost-effective than agency engagement is approximately $150,000–$200,000 of annual development budget, assuming a stable, well-defined product with consistent development needs. Below that level, agency engagement is typically more cost-effective because it avoids the fixed cost of salaries during periods of low development activity.

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Part 7: The Real Factors That Determine Where Your Project Falls in the Range

Every cost range in this post has a low end and a high end. The factors that determine where a specific project falls:

Specification quality at start. Projects that begin with clear, detailed specifications scope and estimate accurately. Projects that begin with "we'll figure out the details as we go" consistently land in the upper half of the range due to change orders and rework.

Integration complexity. A standalone web application is cheaper than an application that integrates with five external APIs, each with different authentication schemes and rate limit behaviors. Every integration adds surface area for bugs and adds engineering time for error handling.

Compliance requirements. Regulated industries add 30–50% to production system costs. If your product touches financial transactions (fintech), health data (HIPAA/PDPL), or government data, compliance engineering is non-optional.

Performance requirements. A product that needs to respond in under 500ms at 10,000 concurrent users requires different architecture than a product with no formal performance requirements. The performance requirements should be defined in discovery — surprises in this dimension are expensive.

Client involvement. Feedback cycles that take days rather than hours slow development. Projects where the founder is available for daily review cycles run faster and cheaper than projects where reviews take a week each. This is a real cost factor that is underappreciated.

Technical debt tolerance. Some agencies build fast and cheap by accruing technical debt. The lean application is delivered quickly, but the production system migration costs double because the debt has to be paid before hardening. Others build clean from the start and move more slowly on the lean application. Know which model your agency uses and make sure it matches your situation.

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Part 8: The $3,000 to $150,000 Path — What ODSEA Delivers at Each Level

For founders who want to understand specifically what ODSEA builds at each budget level:

$3,000–$5,000 (Lean App, Simple): A single-flow web application with user authentication, a core feature, and a deployment on Vercel. Appropriate for: validating a specific user behavior, demonstrating a concept to investors, building the first version of a direct-to-user product. Timeline: 2–4 weeks.

$8,000–$15,000 (Lean App, AI-Integrated): A web application with one AI-powered feature — a chat interface, a document processor, an AI-generated output workflow. Appropriate for: a product where AI is the core value proposition and you need to validate that users will engage with it before scaling. Timeline: 4–8 weeks.

$25,000–$50,000 (Production System, Standard): A production-grade SaaS application with full authentication, row-level security, structured logging, monitoring, performance testing, and a CI/CD pipeline. Appropriate for: a validated product moving toward paying customers. Timeline: 8–16 weeks.

$50,000–$150,000 (Production System, Complex): A production-grade system with multi-agent workflows, compliance engineering (fintech/health), advanced observability, or enterprise integrations. Appropriate for: a product with regulatory requirements, large enterprise customers, or complex AI system architecture. Timeline: 12–24 weeks.

For every engagement, the price is fixed at the scope agreed during discovery. Change orders go through a defined process with explicit client approval before any out-of-scope work begins.

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The most useful single piece of advice on AI development budgeting: scope discovery before anything else. A $5,000 discovery phase that produces a clear architecture and scope document changes the entire cost conversation from a range to a number. It is the investment that makes all subsequent investment more predictable.

If you want to start with discovery, a 30-minute scoping call is the first step. We describe the full engagement model on the services page and the specific AI agent capabilities we build on if you want to understand what we do before the call.