Updated: March 31, 2026 By: Marios
Everyone says AI saves time. Nobody shows the receipts.
I spent 30 days tracking every AI-related cost on two WordPress sites — a content blog publishing 12 posts per month and a WooCommerce store with 340 products. I logged every API call, every plugin subscription, every minute saved and every minute wasted on AI outputs that needed fixing.
Here’s the full breakdown: what I spent, what I saved, and whether AI on WordPress is actually worth the money in 2026.
The setup: two real sites, three AI providers
To make this useful rather than theoretical, I ran the experiment on two active WordPress sites with different use cases.
Site A is a content-focused blog in the design and marketing niche. It publishes 12 articles per month, each averaging 1,800 words. The AI workflow covers first-draft generation, SEO optimization, meta descriptions, featured image creation, and social media copy.
Site B is a WooCommerce store selling digital products. It has 340 active products. The AI workflow covers product description rewrites, customer support chatbot, FAQ generation, and automated review responses.
Both sites run WordPress 7.0 with the official AI provider plugins for OpenAI, Anthropic (Claude), and Google Gemini connected through the Connectors screen. The AI-powered plugins in use across both sites include Rank Math Content AI for SEO, AI Engine for the chatbot and content generation, Jetpack AI for in-editor drafting, and AI Featured Image Generator for image creation.
I tracked costs using each provider’s billing dashboard, cross-referenced with the plugins’ internal usage logs where available.
The 30-day cost breakdown
Total spend across both sites: $47.83
That number surprised me. Before running this experiment, I would have guessed AI costs for two active WordPress sites would land somewhere between $100 and $200 per month. The actual number was less than half my low estimate.
Here’s where every dollar went.
API costs by provider
OpenAI: $28.40
OpenAI handled the bulk of the work — content drafts, image generation, and the WooCommerce chatbot. The breakdown by task:
Content drafts (12 blog posts via AI Engine): approximately 480,000 input tokens and 720,000 output tokens using GPT-4o. These are first drafts that I heavily edited — not publish-and-forget. Cost: approximately $8.40.
Featured image generation (12 images via DALL-E): at roughly $0.04 to $0.08 per image depending on resolution. Cost: approximately $0.72.
Meta descriptions and social copy (36 variations via Rank Math and AI Engine): short prompts, short outputs. Cost: approximately $1.10.
WooCommerce chatbot (AI Engine, ~1,400 conversations over 30 days): the chatbot used GPT-4o Mini for simple queries and escalated to GPT-4o for complex product questions. This was the biggest single expense. Cost: approximately $14.20.
Product description rewrites (85 products updated via AI Engine): batch-processed overnight. Cost: approximately $3.98.
Anthropic (Claude): $12.60
I used Claude Sonnet 4.6 specifically for editorial tasks — rewriting AI-generated drafts to sound more human, generating alternative headlines, and producing FAQ content. Claude consistently produced more natural-sounding prose than GPT-4o in my testing, which is why I split the workload this way.
Editorial rewrites (12 posts, second pass): approximately 360,000 input tokens and 180,000 output tokens. Cost: approximately $3.78.
FAQ generation for WooCommerce (34 product FAQs): Cost: approximately $2.40.
Long-form product comparisons (3 comparison articles): these required Claude’s larger context window to process multiple product specs simultaneously. Cost: approximately $6.42.
Google Gemini: $0.00
Google’s free tier covered everything I needed from Gemini. I used it for quick translation drafts (3 posts translated to Spanish), basic content summarization, and testing image generation with Imagen. The free tier provides enough requests per minute for a site publishing 12 posts per month without hitting the limit.
If I were running a higher-volume operation (50+ posts per month or a multilingual site), I’d start hitting the paid tier. But for the scale of these two sites, Gemini was genuinely free.
Plugin subscription costs: $6.83
Rank Math Pro: $4.92/month (annual plan divided monthly). The Content AI features require the Pro plan. This was the only paid plugin subscription specifically needed for AI features.
AI Engine: $0.00. The free version on WordPress.org covers all the features I used — chatbot, content generation, and MCP support. The Pro version adds fine-tuned models and priority support, but the free tier was sufficient.
Jetpack AI: $1.91/month (equivalent from the annual plan). Used for quick in-editor drafts and tone adjustments within Gutenberg. The 20 free monthly requests ran out by day 8, so the paid tier was necessary.
AI Featured Image Generator: $0.00. The free version works with OpenAI’s API key. The Pro version adds Google Imagen support, but DALL-E covered my needs.
WordPress AI Provider Plugins: $0.00. All three official provider plugins (Anthropic, OpenAI, Google) are free. They’re just connectors — the cost is in the API usage, not the plugin itself.
The time breakdown: what I actually saved
This is where the experiment gets more interesting than the cost data. Tracking dollars is easy. Tracking time requires honesty about what “saved time” actually means — because AI didn’t just save me time. It also cost me time when outputs needed fixing.
Site A (content blog): 23.5 hours saved per month
Here’s the task-by-task breakdown.
First drafts: 14 hours saved. Without AI, writing 12 articles at 1,800 words each takes me roughly 2.5 hours per article for the first draft — approximately 30 hours total. With AI generating a first draft that I then rewrite and edit, the process drops to roughly 1.3 hours per article — approximately 16 hours total. Net time saved: 14 hours. But here’s the caveat: the AI draft is never publishable as-is. Every draft required substantial rewriting — restructuring arguments, adding personal experience, fixing factual claims that were vague or wrong, and replacing generic phrasing with specific examples. The AI draft is a starting point, not a finished product.
SEO optimization: 4 hours saved. Rank Math’s Content AI cut my SEO workflow roughly in half. Generating meta titles, meta descriptions, and getting keyword suggestions used to take about 20 minutes per post manually. With AI, it takes about 8 minutes — mostly reviewing and tweaking suggestions rather than writing from scratch. Across 12 posts: approximately 4 hours saved.
Featured images: 3 hours saved. Finding, licensing, and editing stock photos used to take 15-20 minutes per post. AI image generation takes about 2-3 minutes per image (including prompt refinement and regeneration when the first output isn’t right). Not every AI image was usable — roughly 1 in 3 needed to be regenerated. Across 12 posts: approximately 3 hours saved.
Social media copy: 2.5 hours saved. Generating 3 social media variations per post (Twitter, LinkedIn, Facebook) used to take about 15 minutes per post. AI drafts these in seconds, and editing takes about 2-3 minutes. Across 12 posts: approximately 2.5 hours saved.
Time lost to AI errors: -3 hours. Two blog posts contained claims that were factually inaccurate (an AI hallucination about a product’s pricing and a misattributed feature). Catching and fixing these took about 1.5 hours per incident. This is the hidden cost nobody talks about: the time you spend fact-checking AI output. Over 30 days, I estimate I spent 3 hours correcting mistakes that wouldn’t have existed if I’d written everything manually.
Site B (WooCommerce store): 18 hours saved per month
Product descriptions: 10 hours saved. Rewriting 85 product descriptions manually would take approximately 12 minutes each — roughly 17 hours. AI generated first drafts in batch mode (overnight processing via AI Engine), and I spent about 5 minutes per description reviewing and adjusting. Total time with AI: approximately 7 hours. Net saving: 10 hours.
Customer support chatbot: 6 hours saved. The AI chatbot handled approximately 1,400 conversations over 30 days. Based on my historical support data, roughly 70% of these would have required a human response averaging 3 minutes each. That’s approximately 980 conversations at 3 minutes = 49 hours of human support time. The chatbot resolved about 65% of those without escalation, saving roughly 32 hours. But I spent approximately 6 hours over the month reviewing chatbot logs, updating training data, and handling the 35% of conversations that required human follow-up. Some chatbot responses were incorrect or unhelpful, and customers who received bad answers needed extra attention. Net saving: approximately 26 hours of raw support time minus 20 hours of maintenance and escalation handling = 6 hours saved.
FAQ generation: 2 hours saved. Claude generated FAQ sets for 34 products based on existing product descriptions and common support queries. Manual FAQ writing would take roughly 10 minutes per product (5-6 hours total). With AI, it took about 3 minutes per product to generate and review. Net saving: approximately 2 hours.
The ROI calculation
Total monthly cost: $47.83
Total monthly time saved: 41.5 hours
Effective hourly cost of AI: $1.15/hour
If I value my time at even $30/hour (conservative for a WordPress professional), the 41.5 hours saved represent $1,245 in time value. Against $47.83 in costs, that’s a return of roughly 26x.
But the honest version includes the time lost to AI errors and maintenance. Subtracting the 3 hours lost to content errors (Site A) and the 20 hours of chatbot maintenance (Site B), the net productive time saved drops to approximately 18.5 hours. That’s still a return of approximately 11.6x at $30/hour.
Even at the conservative, error-adjusted number, AI on WordPress delivers clear positive ROI for these use cases. The key qualification: this assumes you’re editing AI outputs, not publishing them raw. The sites where AI provides negative ROI are the ones publishing unedited AI content that gets penalized by Google or damages customer trust.
What I’d change next month
Drop GPT-4o for first drafts, switch to GPT-4o Mini. The quality difference between GPT-4o and GPT-4o Mini for first-draft generation is smaller than the price difference. Since I’m rewriting everything anyway, the cheaper model is sufficient. Estimated saving: $4-5/month.
Move the WooCommerce chatbot to Claude Haiku. At $1 per million input tokens versus GPT-4o Mini’s $0.15, Haiku is slightly more expensive per token but produces noticeably better conversational responses in my testing. The customer experience improvement is worth the marginal cost increase, and I’d save money by eliminating the need for the GPT-4o escalation path. Estimated saving: $2-3/month from simplified architecture.
Use Google Gemini more aggressively. The free tier is underutilized. I should route more low-stakes tasks (social copy, basic summaries, translation drafts) through Gemini before touching the paid providers.
Set up prompt caching for the chatbot. The WooCommerce chatbot sends the same system prompt and product context with every conversation. Anthropic’s prompt caching feature offers 90% savings on repeated content. If I migrate the chatbot to Claude with caching enabled, the $14.20 chatbot expense could drop to under $5.
With these optimizations, I estimate next month’s total cost would drop to approximately $30-35 while maintaining the same output quality. AI on WordPress gets cheaper the more deliberately you architect your workflows.
The bottom line: is AI on WordPress worth it?
For the way I use it — as a drafting assistant, SEO accelerator, and frontline customer support tool — yes, clearly. The math works at almost any reasonable valuation of your time.
But the math only works if you treat AI as a tool, not a replacement. Every hour I saved on drafting was partially reinvested in editing. Every chatbot conversation I didn’t have to answer manually was offset by the time I spent training the chatbot and reviewing its logs. AI didn’t eliminate work. It changed the type of work — from creation to curation, from writing to editing, from answering to supervising.
The $47.83 monthly cost is real but modest. A single hour of freelance WordPress development costs more than my entire month of AI expenses. If the tools save you even five hours per month, they’ve paid for themselves several times over.
The real risk isn’t cost. It’s complacency. The sites that will get burned by AI in 2026 are the ones publishing unedited AI content at scale and wondering why their Google traffic disappeared. The ones that will benefit are the ones using AI to work faster while maintaining (or improving) the quality of their human judgment.
That’s the trade-off. It’s a good one.
Full cost summary table
| Category | Tool | Monthly Cost |
|---|---|---|
| API — OpenAI | GPT-4o, GPT-4o Mini, DALL-E | $28.40 |
| API — Anthropic | Claude Sonnet 4.6 | $12.60 |
| API — Google | Gemini (free tier) | $0.00 |
| Plugin — Rank Math Pro | Content AI features | $4.92 |
| Plugin — Jetpack AI | In-editor assistance | $1.91 |
| Plugin — AI Engine | Chatbot + content gen | $0.00 |
| Plugin — AI Provider Plugins | Connectors (x3) | $0.00 |
| Plugin — AI Featured Image | Image generation | $0.00 |
| Total | $47.83 |
| Metric | Site A (Blog) | Site B (WooCommerce) | Combined |
|---|---|---|---|
| Hours saved (gross) | 23.5 hrs | 38 hrs | 61.5 hrs |
| Hours lost to errors | 3 hrs | 20 hrs | 23 hrs |
| Hours saved (net) | 20.5 hrs | 18 hrs | 38.5 hrs |
| AI cost | $18.13 | $29.70 | $47.83 |
| Cost per net hour saved | $0.88/hr | $1.65/hr | $1.24/hr |
Want to run this experiment on your own site? I’ve published the tracking spreadsheet template I used — download it below and adapt it to your workflow. If you run your own 30-day cost experiment, I’d love to see your results. Tag us on Twitter or send them over and we’ll feature the best community submissions in a follow-up post.
