AI Software Development Cost Estimation
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How Much Does AI Cost?
The cost of AI development may range from $30,000 to $4,000,000. Artificial intelligence cost estimation depends on the following factors:
- Solution type and complexity.
- The volume and quality of available data.
- Data type (e.g., structured, unstructured).
- The need to source additional training data and label it.
- The complexity of AI model integration.
- The need for custom AI model development (e.g., instead of using open-source models).
- Outsourced or in-house development.
Sample AI Development Costs
Below, our consultants provide sample AI software costs for solutions of different complexity. AI cost estimation is based on real-life projects of our experts.
$30,000–$200,000
An AI component of larger software (e.g., forecasting module of an analytics solution).
$120,000–$300,000
An AI-powered virtual assistant.
$200,000–$600,000
An AI-driven automation solution of average complexity (e.g., for inventory optimization).
$800,000–$1,000,000+
A complex analytics system powered by AI and big data techs.
How to Optimize AI Development Costs?
During AI project cost estimation, experts usually consider ways to reduce costs without compromising quality. The cost of implementing artificial intelligence can be optimized by customizing pre-trained models, improving training data, outsourcing development to external teams, and more.
Using open-source or pre-trained models
It is possible to tailor open-source AI models, frameworks, and datasets to specific use cases or fine-tune pre-trained AI models. Such an approach helps avoid licensing fees and investments required to develop artificial intelligence from scratch. For example, open-source models work great for text summarization and speech recognition.
Using a multi-model approach for complex tasks
For example, an AI solution for cancer risk assessment can use a computer vision model to analyze a medical image and detect anomalies, while an NLP-powered model will scan the patient's medical history for risk factors. Unlike with one generalized model, each model will process only dedicated aspects of data thus reducing the computation overhead.
Optimizing training data
More data does not always equal higher model accuracy. It is essential to clean the training and validation datasets removing redundant and irrelevant information, which helps optimize infrastructure costs.
Outsourcing development
On average, outsourced development can be 50% more cost-efficient in comparison to cases when organizations decide to build an AI system in-house. With outsourced development, companies benefit from reduced infrastructure investments and specialized expertise of external talents that does not presuppose long-term commitment.
