AI Development
How Much Does AI Development Cost?
AI development typically costs from around $15,000 for a focused feature or RAG integration into an existing product, and $50,000 to $150,000 or more for a full AI product with custom evaluation and MLOps infrastructure. The final number depends on scope: the number of AI capabilities, data pipeline complexity, integrations, and the accuracy bar your product must meet.
Last updated June 2026
What drives the cost of AI development
Five factors move the number most: the number and complexity of AI capabilities, how ready your data is, the accuracy bar the product must clear, the depth of integrations with your existing systems, and whether you need ongoing MLOps such as retraining, drift detection, and monitoring.
A single AI feature on clean data is inexpensive. A multi-capability product on messy data with a high accuracy requirement and tight integrations costs more, because the engineering effort scales with reliability, not just features.
Typical AI project price ranges
- Focused AI feature or RAG integration: from around $15,000, added to an existing product over roughly 6 to 8 weeks.
- Full AI product build: $50,000 to $150,000 and above, with custom capabilities, evaluation, and MLOps, typically over 12 to 20 weeks.
- Two-week AI audit: a fixed-scope diagnostic for teams not yet ready to commit to a full build.
These are starting points. We scope every project before quoting, so the figure you receive reflects your actual requirements rather than a generic estimate.
How to keep AI costs under control
Most cost overruns come from unbounded model usage and rework from poor accuracy. We control the first with caching, prompt compression, model routing, and spend dashboards. We control the second with an evaluation framework built before the product, so quality is measured rather than assumed.
Starting with a focused integration or audit also caps early spend while proving value, which makes the case for a larger investment far easier to justify.
How MarsDevs scopes and prices AI work
We are outcome-first. We define deliverables, accuracy targets, and integrations up front, then price against that scope. Every engagement begins with a free scoping call, and you always know the cost before committing.
Explore our AI development services, or book a scoping call to get a number for your specific product.
Related questions
Why is AI development priced by scope instead of hourly?
Scope-based pricing aligns cost with outcomes and removes the risk of open-ended hourly bills. We define the deliverables, accuracy targets, and integrations up front, then price against that scope so you know what you are paying for before committing.
What is the cheapest way to start with AI?
The lowest-risk entry points are a focused AI feature added to your existing product or a short two-week AI audit. Both deliver value quickly without committing to a full product build, and they surface exactly where AI will move your metrics.
Are there ongoing costs after an AI product launches?
Yes. Beyond the build, expect model or API usage costs, infrastructure, and monitoring or MLOps for systems that need retraining and drift detection. We design caching, model routing, and spend dashboards so these costs stay predictable.
How do you prevent surprise cloud and model bills?
We implement caching layers, prompt compression, and model routing that sends simple tasks to smaller cheaper models and reserves larger models for complex reasoning. Real-time spend dashboards mean you see cost by feature and catch regressions early.
Do you provide a fixed price?
For well-defined scope, yes. Focused integrations and audits are typically fixed price and fixed scope. Larger product builds are scoped and priced at kickoff. Every engagement starts with a free scoping call before any commitment.
