How much does AI automation cost? A transparent pricing guide

Most AI consultancies hide their pricing behind a "book a call" button. You will click through five pages, fill out a form, sit through a 30-minute discovery call, and only then hear a number that may or may not resemble what you actually pay.

We understand why companies do this. AI automation is genuinely custom work and quoting a fixed price without understanding the problem is irresponsible. But refusing to publish any numbers at all is not protecting the client. It is protecting the consultancy from price comparison.

This post is our attempt to fix that. We will share real pricing ranges (ours and the broader market's), explain what drives costs up or down, and cover the ongoing expenses that most providers conveniently leave out of the initial quote.

Why AI automation pricing is so opaque

Before we get into numbers, it helps to understand why this industry is so secretive about cost.

Every project is different. A chatbot that answers FAQs from a static knowledge base is a fundamentally different project than an AI agent that reads incoming contracts, extracts key terms, cross-references them against internal policies, and flags risks for a legal team. Quoting both at the same price would be dishonest.

Scope changes everything. The difference between "automate our invoice processing" and "automate our invoice processing and integrate it with our ERP, CRM, and accounting platform" can be a 5x cost difference. Most consultancies do not want to publish a number that becomes an anchor when the real scope turns out to be larger.

The market is immature. AI automation is still a young industry. Pricing norms have not settled. Some firms charge by the hour, some by the project, some take a percentage of cost savings, and some bundle everything into a monthly retainer. This inconsistency makes comparison nearly impossible.

All of that is true. None of it justifies hiding every number. You deserve ballpark ranges to know whether you are looking at a $5,000 project or a $500,000 project before you invest time in sales calls.

The three tiers of AI automation

The market broadly breaks into three categories. Where you land depends on your technical capacity, budget, and how custom your needs are.

Tier 1: DIY tools ($0 to $500/month)

These are off-the-shelf platforms where you build automations yourself using visual interfaces, templates, and pre-built connectors.

Examples: Zapier, Make (formerly Integromat), n8n, Microsoft Power Automate, ChatGPT with custom GPTs.

Best for: Small businesses with straightforward needs, teams with some technical comfort, simple "if this then that" workflows.

Limitations: You are constrained by what the platform supports. Complex logic, custom data processing, or multi-step reasoning tasks hit walls quickly. You also own the maintenance. When something breaks at 2 AM, it is your problem.

Real cost breakdown:

• Platform subscription: $20 to $500/month
• Your time building and maintaining: 5 to 20 hours/month (often underestimated)
• API costs for AI models: $10 to $200/month depending on volume

Tier 2: Integration platforms and low-code solutions ($500 to $5,000/month)

These are managed platforms or lightweight consulting engagements where someone helps you connect AI capabilities to your existing tools.

Examples: AI-enhanced CRM automations, managed chatbot platforms, pre-built industry solutions with some customization.

Best for: Mid-size businesses with common use cases (customer support, lead qualification, document processing) that roughly fit existing templates.

Limitations: You get 80% of what you need out of the box. The last 20%, the part that makes it actually fit your workflow, is either impossible or costs disproportionately more. Vendor lock-in is real.

Real cost breakdown:

• Platform fees: $200 to $2,000/month
• Setup and customization: $2,000 to $10,000 one-time
• Ongoing support: $500 to $2,000/month

Tier 3: Custom AI development ($5,000 to $100,000+)

This is where a development team builds AI automation specifically for your business, your data, your workflows, and your edge cases.

Examples: Custom AI agents, bespoke workflow automation systems, proprietary data pipelines with ML components, multi-system integrations with intelligent routing.

Best for: Businesses with complex workflows, unique data, compliance requirements, or needs that do not fit neatly into existing platforms. Also for organizations where the automation is a competitive advantage, not just an efficiency gain.

Limitations: Higher upfront investment. Requires clear scope definition. Takes weeks to months, not hours.

This is the tier siasola operates in. We build custom AI agents and automation systems. Here are our actual ranges.

siasola's pricing ranges

We price by project scope, not by the hour. Every engagement starts with a discovery call where we understand your workflow, data, and goals before quoting anything. But here are the ranges we typically see.

Simple automation: $5,000 to $15,000

A single workflow with one or two integrations. The AI handles a well-defined task with clear inputs and outputs.

Examples:

• An AI agent that monitors an email inbox, categorizes messages, and routes them to the right team
• Automated document extraction from a consistent format (invoices, receipts, applications)
• A customer-facing chatbot with a defined knowledge base and escalation rules

Timeline: 2 to 4 weeks

What you get: A working system, documentation, a handoff session, and 30 days of post-launch support.

Medium complexity: $15,000 to $40,000

Multi-step workflows with three to five integrations. The AI makes decisions, handles exceptions, and coordinates across multiple systems.

Examples:

• An AI agent that processes incoming leads, enriches them with external data, scores them against your criteria, and creates personalized outreach drafts
• An internal operations bot that manages scheduling, resource allocation, and status reporting across multiple tools
• Automated compliance checking that reviews documents against regulatory requirements and flags issues with explanations

Timeline: 4 to 8 weeks

What you get: Everything above plus more extensive testing, integration documentation, and training for your team.

Complex systems: $40,000 to $100,000+

Multiple AI agents working together, custom machine learning components, enterprise integrations, or systems that need to handle high volume with high reliability.

Examples:

• A multi-agent system where specialized AI agents handle different aspects of a workflow and coordinate with each other
• Custom ML models trained on your proprietary data, deployed within an automation pipeline
• Enterprise-grade systems with role-based access, audit trails, and compliance logging

Timeline: 2 to 6 months

What you get: Full system architecture, phased rollout, extensive testing, team training, and ongoing support planning.

What drives the cost up (and down)

Understanding these factors helps you estimate where your project falls before you talk to anyone.

Factors that increase cost

Number of integrations. Every system your automation connects to adds complexity. APIs are inconsistent, authentication varies, rate limits differ, and error handling multiplies. Going from two integrations to five does not increase cost linearly; it can double or triple it.

Data quality. If your data is clean, consistent, and well-structured, automation is straightforward. If your data lives in spreadsheets with inconsistent formatting, free-text fields, and duplicate records, a significant portion of the project becomes data cleaning and normalization.

Decision complexity. A workflow where the AI follows simple rules ("if amount > $10,000, flag for review") is far cheaper than one requiring nuanced judgment ("evaluate whether this contract clause introduces unacceptable risk given our existing obligations").

Compliance and security requirements. Healthcare (HIPAA), finance (SOC 2), or government work adds documentation, audit trails, access controls, and testing requirements that increase cost significantly.

Volume and reliability needs. Processing 100 items per day with occasional manual fallback is different from processing 100,000 items per day with 99.9% uptime requirements.

Factors that decrease cost

Clear scope. The single biggest cost reducer. If you can articulate exactly what the automation should do, with specific inputs, outputs, and edge cases, you eliminate weeks of discovery and iteration.

Clean, accessible data. If your systems have good APIs and your data is well-structured, integration is dramatically faster.

Existing documentation. If your team has already documented the workflow you want to automate, including decision criteria and exception handling, development moves much faster.

Willingness to phase. Starting with a focused MVP and expanding later is almost always cheaper than trying to build everything at once. You learn what actually matters in production.

The costs everyone forgets

The project price is not the total cost. Here is what you should budget for beyond the initial build.

API and model costs: $50 to $1,000+/month

Every time your automation calls an AI model (GPT-4, Claude, etc.), you pay per token. For low-volume applications, this might be $50/month. For high-volume document processing or customer-facing agents handling thousands of conversations, it can reach $1,000/month or more.

Ask your provider: What AI models will this use, and what are the estimated monthly API costs at our expected volume?

Hosting and infrastructure: $50 to $500/month

Your automation runs somewhere. Cloud hosting, databases, queue systems, and monitoring tools all have monthly costs. Simple automations on serverless infrastructure might cost $50/month. Complex systems with dedicated servers cost more.

Maintenance and updates: $200 to $2,000/month

AI models get updated. APIs change. Your business processes evolve. Someone needs to monitor performance, fix issues, and make adjustments. Budget 10 to 20% of the initial project cost annually for maintenance.

At siasola, we offer ongoing support agreements tailored to the system's complexity. We are transparent about what is included and what costs extra.

Monitoring and error handling: often $0 initially, expensive later

If your automation processes critical business data, you need monitoring. Alerts when things fail. Logs to diagnose issues. Dashboards to track performance. This is often overlooked in initial scoping and becomes urgent after the first production incident.

Training and change management: variable

Your team needs to understand the new system. How to use it, when to intervene, what the limitations are. Budget time and potentially budget for documentation and training sessions.

Red flags when evaluating AI automation pricing

After working in this space, here are the warning signs we would want you to watch for, even if you do not choose to work with us.

No pricing information anywhere. If a company cannot give you even a ballpark range before a sales call, they are either pricing based on what they think you can afford or they do not have enough experience to estimate.

"It depends" with no follow-up. Yes, it depends. But an experienced provider can say "projects like what you are describing typically fall in the $X to $Y range" after a 15-minute conversation.

Quoting hourly rates without estimated hours. "$200/hour" means nothing without knowing whether the project takes 40 hours or 400. Always ask for a total project estimate or a capped budget.

No mention of ongoing costs. If the proposal only covers the build and says nothing about API costs, hosting, or maintenance, you are getting an incomplete picture.

Percentage-of-savings pricing. Some firms charge a percentage of the money their automation saves you. This sounds appealing but creates misaligned incentives, is nearly impossible to audit accurately, and can cost far more than a fixed-price project over time.

Pressure to sign before scoping. Any provider pushing you to commit before thoroughly understanding your needs is prioritizing their pipeline over your outcome.

Vague deliverables. "We will build you an AI solution" is not a deliverable. You should see specific workflows, integrations, success criteria, and timelines before signing anything.

No discussion of what happens when it breaks. Every system has failure modes. If your provider has not discussed error handling, fallback procedures, and support response times, the plan is incomplete.

How to budget for AI automation

If you are considering AI automation for the first time, here is a practical framework.

Step 1: Start with the problem, not the technology. What manual process is costing you the most time or money? What would change if that process were 80% automated? This gives you a ceiling for what the automation is worth.

Step 2: Get a rough tier estimate. Based on the ranges above, does your project look like a $5K project or a $50K project? This helps you set expectations before talking to providers.

Step 3: Budget for the full first year. Take the project estimate and add 12 months of operating costs (API usage, hosting, maintenance). This is your real cost of ownership.

Step 4: Talk to 2 to 3 providers. Get scoping calls, compare approaches, and ask every provider the same questions. Evaluate not just price but clarity of communication and understanding of your problem.

Step 5: Start small. If you have not done AI automation before, start with a focused project in the $5,000 to $15,000 range. Learn how the process works, what your team needs to adapt to, and what the real impact is before committing to larger investments.

How we handle pricing at siasola

We do a free discovery call. No pitch deck, no hard sell. We listen to your workflow, ask questions, and tell you honestly whether AI automation makes sense for your situation. Sometimes the answer is "you do not need us. Here is a $50/month tool that does what you need."

If custom development is the right fit, we provide a detailed proposal with specific deliverables, a clear timeline, a fixed project price, and estimated ongoing costs. No hidden fees. No surprise invoices.

If you want to have that conversation, reach out through our contact page or book a discovery call.

We would rather lose a deal by being honest about cost than win one by being vague.

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Related reading: Is workflow automation worth it? How to calculate the ROI and AI agents vs. chatbots: what your business actually needs.