AI project cost estimates are frequently wrong by an order of magnitude - usually because they account for model inference costs but miss the engineering work, data preparation, integration, testing, and ongoing operations that make up the majority of total project cost.

This guide provides a realistic cost framework for enterprise AI projects, from prototype to production deployment.

Cost Categories

Enterprise AI project costs fall into five categories:

1. Model inference costs - What you pay per API call or per token to the model provider (Bedrock, Anthropic API, OpenAI API) or the compute cost to run a self-hosted model.

2. Infrastructure costs - Vector databases, storage, compute for data processing pipelines, API gateways, monitoring, logging.

3. Engineering labor - The largest cost category for most projects. Includes data engineering, application development, integration, testing, and deployment.

4. Data costs - Data acquisition, cleansing, labeling, and ongoing maintenance. Often underestimated significantly.

5. Ongoing operations - Monitoring, incident response, model updates, knowledge base maintenance.

Prototype to Production: Cost Stages

Prototype (4-8 weeks)

Goal: Demonstrate that AI can solve the problem with acceptable quality on real data.

Typical costs:

  • Engineering: 1-2 engineers for 4-8 weeks ($20,000-$80,000 fully loaded)
  • Model inference during development: $100-$500 (development volumes are low)
  • Infrastructure: minimal, using managed services with free tiers
  • Total range: $25,000-$100,000

This stage answers whether the use case is technically viable. Many projects are killed at this stage - which is the right outcome if the AI quality is insufficient.

Pilot (8-16 weeks)

Goal: Deploy to a small group of real users, validate quality and adoption, measure impact.

Typical costs:

  • Engineering: 2-3 engineers for 8-16 weeks ($60,000-$240,000)
  • Data preparation: $10,000-$50,000 depending on data quality and volume
  • Infrastructure (pilot scale): $500-$2,000/month
  • Model inference (pilot traffic): $500-$5,000/month
  • Total range: $80,000-$350,000

Production Deployment (4-8 weeks post-pilot)

Goal: Harden the system for production load, compliance, and reliability requirements.

Typical costs:

  • Engineering (hardening, compliance, integration): $40,000-$120,000
  • Infrastructure setup (production environment, monitoring, disaster recovery): $5,000-$20,000 one-time
  • Security and compliance review: $10,000-$30,000
  • Total range: $55,000-$170,000

Ongoing Operations (per year)

Typical costs:

  • Model inference: varies widely by volume. Rule of thumb: $1,000-$10,000/month for moderate enterprise workloads (millions of tokens/day)
  • Infrastructure: $1,000-$5,000/month
  • Engineering maintenance (bug fixes, updates, knowledge base maintenance): 0.25-0.5 FTE/year ($30,000-$60,000/year)
  • Total range: $50,000-$200,000/year

The Most Common Budget Mistakes

Underestimating data work - “The data is in our systems” is almost never true in the sense that means “the data is ready to use.” Extraction, cleansing, normalization, and ongoing governance typically take 30-40% of the total project budget.

Not budgeting for evaluation - Building a test set of labeled examples, running systematic quality evaluations, and fixing quality issues discovered in production are significant ongoing costs. Budget at least 20% of the initial build cost for evaluation and quality work.

Ignoring knowledge base maintenance - RAG-based systems require ongoing content maintenance. If the knowledge base goes stale, the system degrades. Budget 2-4 hours per week of someone’s time for knowledge management.

One-time vs ongoing confusion - Model costs are ongoing. They grow with usage. Build financial models that project 1-year and 3-year total cost of ownership, not just build cost.

When to Build vs Buy

Pre-built AI tools (Salesforce Einstein, ServiceNow AI, Microsoft Copilot) are typically cheaper and faster for use cases within their scope than custom builds. Evaluate pre-built solutions before committing to custom development. The custom build is justified when: pre-built solutions do not cover your use case, integration requirements exceed what pre-built tools support, or data and compliance requirements preclude using external tools.