How Much Does It Cost to Build An AI agent?
Realistic cost to build an AI agent: USD 15k–400k depending on scope. Features, tech stack, timeline, team and monetisation…
How Much Does It Cost to Build an App Like Runway ML?
Realistic cost to build an app like Runway ML: USD 15k–400k depending on scope. Features, tech stack, timeline, team and monetisation explained.
If you are thinking about building an app like Runway ML, the first question is almost always the same: what will it cost? The honest answer is "it depends" — but that is not very useful, so this guide puts real numbers on it.
The short answer
Building an app like Runway ML typically costs USD 15,000 to USD 120,000 for most businesses, with large-scale platforms going beyond USD 140,000. Here is how that breaks down:
| Version | What you get | Typical cost | Timeline |
|---|---|---|---|
| MVP | Core user journey on one or two platforms, basic admin | $15k – $40k | 6–12 weeks |
| Full product | All main apps, payments, notifications, analytics | $50k – $120k | 14–24 weeks |
| Advanced platform | Scale, automation, AI features, integrations | $140k – $400k+ | 28–48 weeks |
These figures assume a professional team in India working at typical market rates. The same scope built entirely in the US, UK or Australia usually costs two to three times more.
How Runway ML works
AI products like Runway ML combine powerful models with the data, interface and guardrails that make them useful and safe. Model usage costs are ongoing, so architecture choices affect your margins for years.
We are not affiliated with Runway ML; we reference it only as a familiar example. You do not need to copy any product — the goal is to understand the building blocks and design something that is better for your own market.
Core features and the apps you will need
User experience
- Chat or task interface
- File and data upload
- Answers with sources
- History and workspaces
- Feedback controls
AI layer
- Model integration and routing
- Retrieval over documents (RAG)
- Tool use and agents
- Guardrails and safety filters
Platform
- Usage metering and billing
- Admin and permissions
- Evaluation and monitoring
Advanced features that add cost (and value)
Once the core works, these are the upgrades that typically move a AI product product from good to category-leading. Each can add anywhere from a few thousand to tens of thousands of dollars:
- Fine-tuned or self-hosted models
- Multi-agent workflows
- Voice and vision
- Enterprise SSO and data residency
Where the budget goes
For a full product of around USD 85,000, a typical split looks like this:
| Phase | Share | Approximate cost |
|---|---|---|
| Discovery, UX and UI design | 12% | USD 10,200 |
| Mobile and web front ends | 33% | USD 28,100 |
| Back end, APIs and integrations | 30% | USD 25,500 |
| Quality assurance and testing | 13% | USD 11,100 |
| Project management and DevOps | 12% | USD 10,200 |
How location changes the price
The same features cost very different amounts depending on where your team sits. Indicative market rates:
| Region | Typical hourly rate (USD) | Notes |
|---|---|---|
| India | 25 – 50 | Deep talent pool; best value for full product teams |
| Eastern Europe | 40 – 75 | Strong engineering, good EU overlap |
| UAE & Saudi Arabia | 50 – 100 | Local presence, higher overheads |
| United Kingdom | 70 – 150 | Agencies in London at the upper end |
| Australia | 80 – 160 | Senior local talent is scarce and expensive |
| United States | 90 – 180 | Silicon Valley and New York at the top of the range |
A hybrid model — product leadership close to your market and engineering in India — is how many companies get the best of both.
Recommended technology stack
There is no single correct stack, but a sensible, well-supported starting point for AI product products is:
- Apps and front end: Flutter for mobile and Next.js for web dashboards
- Back end: Node.js (NestJS) or Python (FastAPI)
- Database: PostgreSQL with Redis for caching
- Cloud: AWS, Azure or Google Cloud, with a region close to your users
- Also relevant: OpenAI, Anthropic Claude, Google Gemini, Llama, Mistral, LangChain
The team you need
A typical team for the full-product version:
- 1 product manager / business analyst
- 1 UI/UX designer
- 2–4 mobile and front-end developers
- 2–3 back-end developers
- 1–2 QA engineers
- A part-time DevOps engineer and solution architect
Timeline
| Phase | Duration |
|---|---|
| Discovery and design | 2–4 weeks |
| MVP build | 6–12 weeks in total |
| Full product | 14–24 weeks in total |
| Post-launch iteration | Ongoing, in 2-week sprints |
How to make money
Common monetisation models for AI product products:
- Subscriptions
- Usage-based pricing
- Enterprise licences
- API access
Compliance to plan for
Depending on where you launch, budget time for emerging AI regulations (EU AI Act for European users), data privacy and copyright considerations for training data. Building these in early is far cheaper than retrofitting them after launch.
Ongoing costs after launch
- Maintenance: budget roughly 15–20% of the build cost per year for updates, OS changes and fixes
- Cloud hosting: from a few hundred dollars a month at launch, scaling with usage
- Third-party services: maps, SMS/OTP, payments, email and analytics fees
- Marketing and support: often larger than the tech budget for consumer products
How to reduce the cost without cutting corners
- Automate testing early to keep later releases cheap
- Launch an MVP in one city or segment before expanding
- Defer advanced AI features until you have real user data
- Use cross-platform frameworks unless native performance is truly required
- Release the admin console as a simple internal tool first
Get an estimate for your idea
Every product is different. Share a few lines about your idea through our contact page or try the app cost calculator for a quick ballpark. If you want to go deeper, see our Generative AI Development page.