Woyce
Indicative ranges for custom AI agent development — every project is scoped individually, so treat these as budgeting guides, not quotes.
A single-channel agent with basic RAG and 1–2 integrations. Proves value fast before you scale.
A production agent across multiple channels with custom RAG, evals, and the integrations your workflow needs.
Multi-agent systems, complex integrations, and compliance/security work for regulated or high-volume use.
Each CRM, database, or third-party API the agent touches adds scope — this is usually the biggest swing.
Clean, well-structured data is cheap to ground on. Messy or large knowledge bases need pipelines and re-ranking.
One web widget is simpler than web + WhatsApp + voice + Slack, each with its own deployment and testing.
Frontier models (GPT-4, Claude) cost more per call than open-source; the right pick depends on accuracy needs.
Production agents need eval suites, guardrails, and monitoring — the difference between a demo and something you trust.
A Q&A bot is far simpler than a multi-step agent that takes actions, uses tools, and recovers from errors.
The build is one-time; running an agent in production is not. Budget for these from day one.
Pay-per-token to OpenAI/Anthropic, scaling with traffic. Often the largest recurring line.
Hosting, vector database, and observability tooling — modest but continuous.
Tuning prompts, refreshing data, and adapting to new model releases. Usually a monthly retainer.
Want a team that handles all of this? Hire dedicated AI developers or see our AI agent development services.
Most custom AI agents fall between $8k for a focused pilot and $60k+ for a multi-channel production system. The final number depends on integrations, data complexity, channels, and how much autonomy the agent needs.
Integrations are usually the biggest swing — every CRM, database, or API the agent connects to adds scope. After that, data quality for RAG, the number of channels, and the level of evals and safety work drive the cost.
Yes. Plan for LLM API usage (pay-per-token, scaling with traffic), infrastructure like hosting and a vector database, and a maintenance retainer for tuning, data refreshes, and adapting to new model releases.
Off-the-shelf platforms are cheaper to start but charge per seat or message and limit customization. A custom agent costs more upfront but is cheaper at scale and owns your data and IP. We help you model the break-even before you decide.