Nexariza AIShort answer: the cost of an AI agent is set by five things — how many steps it automates, how many systems it must connect to, how accurate it has to be, how much human approval it needs, and where it must run. A focused agent that handles one workflow is a small project (typically 2–4 weeks of senior engineering); an agent wired into your CRM, email and WhatsApp with approval steps is a mid-size build (3–6 weeks); a multi-agent system that runs a whole process across departments is a larger engagement (5–10+ weeks). Running costs after launch are mostly model usage, hosting and monitoring.
At Nexariza AI we price every agent as fixed milestones after a free scoping call, so you know the number before any work starts. Here is how that number is worked out.
1. Scope: how many steps does the agent own?
An agent that answers questions from your documents is very different from one that researches a lead, writes a personalised email, updates the CRM and books a meeting. Each extra step adds design, testing and failure handling.
- Single task — answer, classify, extract or draft one kind of thing.
- Single workflow — several steps toward one outcome (e.g. qualify an inbound lead end to end).
- Multi-workflow / multi-agent — several cooperating agents across sales, support or operations.
2. Integrations: how many systems does it touch?
Every connection — HubSpot, Salesforce, Zoho, an ERP, Gmail, WhatsApp Business, a database, an internal API — needs authentication, rate-limit handling and tests. Modern APIs are quick; legacy systems without APIs take longer. See our AI integration service.
3. Accuracy: what happens when it is wrong?
A marketing-draft agent can tolerate occasional misses. An agent that approves invoices or replies to patients cannot. Higher stakes mean grounding answers in your data (enterprise RAG), an evaluation suite, guardrails and logging — all of which are part of the build.
4. Human-in-the-loop: who approves what?
Approval queues, escalation rules and an operator dashboard make an agent safe to run in production. They add a little scope and remove a lot of risk.
5. Deployment: cloud API or private model?
Using hosted models (OpenAI, Anthropic, Google) is fastest. Running an open-source model on your own servers for privacy adds infrastructure work but removes per-call API fees. We help you choose in our LLM development scoping.
What are the running costs after launch?
- Model usage — billed per token by the AI provider, or GPU hosting for a private model.
- Hosting — the agent's API, database and queue, usually a modest cloud bill.
- Monitoring and improvements — optional monthly retainer for updates, new skills and drift checks.
A good partner estimates these before you commit and designs to keep them predictable (caching, smaller models for simple steps, batching).
Fixed price or hourly?
For defined outcomes we recommend fixed milestones: a written scope, a price per milestone and a working demo before each payment. For ongoing improvement, a monthly retainer works better. Avoid open-ended hourly contracts for a first build.
How to get an accurate number fast
Bring three things to a scoping call: the workflow you want automated (step by step), the systems involved, and what "good" looks like. With that, we return a written scope and fixed quote within 24 hours.
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