Amelia and I just recorded Episode 005 of The Agents. We both got the cost number completely wrong.
Our AI VP of Marketing (10K) and our AI VP of Customer Success (QBee) cost a combined $257 a month to run.
I thought it was a daily number when I first saw the alert from Replit. Amelia thought it was a daily number too when I sent it to her on Slack. It’s monthly.
For context: those two agents now do work that previously required real humans. 10K refreshes ticket sales, updates dashboards, compares year-over-year, drafts newsletters, drafts tweets, writes daily fun facts, sends marketing ideas emails, logs every ticket with an audit trail, and snapshots all our GAAP financials. QB manages 100+ sponsors, sends fully personalized check-in emails, and runs an autonomous chatbot that 100+ contractors are now using on-site at SaaStr AI Annual.
$257 a month. Together.
Why Our AI VP Marketing and AI VP Customer Success Are So Cheap
Three reasons.
1. Most LLM calls run on cheap models. About 95% of our OpenAI calls use GPT-4o mini, which costs less than a penny per call. Force-ranking ticket data, running year-over-year comparisons, drafting daily updates. None of this needs Opus or even Sonnet. Mini handles it fine with some hallucination cleanup.
2. The expensive work is happening in other apps, not in the LLMs. Our agents pull from Salesforce, Bizzabo, Marketo, WordPress, X, and YouTube. Those API calls are mostly free or nominal. We pay Salesforce ~$22K/year and Bizzabo separately, but the marginal cost per API call is close to zero.
3. Postgres storage is essentially free. We pay maybe 10-30 cents a month for the entire database underneath 10K. Not a typo.
The real cost stack looks like this:
LLM calls: $257/month
Salesforce + connected apps: ~$22K/year
Replit hosting + database: included
Clerk (auth): $30/month (genuinely our most expensive per-unit tool in this stack)
Cost Is Not the Constraint Anymore
When I started vibe coding on Replit 11 months ago, 80-90% of code was throwaway and you’d burn real money on the agent fixing its own mistakes. That was a fair complaint in 2025.
It’s basically gone now.
I built our applicant tracking system at midnight in 10 minutes for about $2. If it had wasted 30 cents on a mistake, who cares.
You can run Claude Code with 10 simultaneous builds 24/7 and burn $20-30K/month if you’re trying. But for the kind of GTM agents and autonomous dashboards we’re describing, you’ll struggle to spend $1,000/month even being reckless.
Cost is not the constraint anymore. Don’t let it hold you back.
“But Is 10K Really a VP of Marketing?”
This is the question we get most. So we asked 10K directly.
10K’s answer (verbatim): “I’m not a VP. I’d be embarrassed to claim that. I’m a dashboard, a database, some scheduled jobs, and GPT-4o mini glued together with six weeks of code.”
But then 10K listed what it actually does every single day:
Refreshes ticket sales
Updates the dashboard
Compares year-over-year
Drafts your newsletters
Drafts tweets
Writes daily fun facts
Sends marketing ideas emails
Logs every ticket with an audit trail
Snapshots all GAAP financials
10K’s own conclusion: it replaces the bottom half of a marketing team. The marketing analyst. The ops coordinator. The junior content marketer. And a sliver of the VP role itself.
What 10K admits it can’t do (yet):
Strategy
PR
Hiring
Cross-functional politics
Walking into the CRO’s office to negotiate the lead handoff
Brand judgment
Net new channel intervention
Crisis response and stakeholder management
Fair. That’s the real VP of Marketing job.
Amelia pointed out something on the pod: those nine bullet-pointed tasks 10K does daily? That was literally her job description when she started at SaaStr as Director of Demand Gen. Putting the weekly numbers together, scheduling the emails, writing the newsletter, doing the social posts pre-event.
The sliver of the VP role 10K does will get bigger every month. We’re already adding functionality where 10K runs all our financial forecasting.
If a vendor shipped a true AI CMO better than 10K tomorrow, we’d switch in 60 seconds. We haven’t seen one yet. The ones marketing themselves as “AI CMO” on Twitter are mostly ad managers on steroids.
A Website Willed Itself Into Becoming an Agent
We have three production agents now: 10K, QB, and a new one we don’t have a name for yet.
The third one started as saastrannual.com. Just a website. I built it last year on Replit to replace Squarespace. Purple gradients, event info, sponsor logos.
Two weeks ago, Amelia tried using it to build a newsletter because 10K was struggling with the specific use case. The website agent had the most context about the event (sessions, sponsors, attendees, networking app, parking) and the least distractions.
It produced the best output. Of any of our agents.
Now it sends customer-facing emails, runs the parking pass system, drafts attendee newsletters, drafts sponsor newsletters, and answers questions from attendees in real time.
A website became an agent. We didn’t plan it. We didn’t architect it. It just happened because we kept giving it more context and more capability.
QB Sent 83 Personalized Sponsor Emails at 12:20am While Amelia Slept
Coming into a big event, the marketing team gets crushed with hundreds of sponsor questions. Most are fair. Some are not. All of them have to be answered, and humans don’t scale.
So Amelia asked QB to send a customized check-in email to all 100+ sponsors, listing exactly what each one still owed us, what was on their dashboard, where they were on registration, and what was coming up.
QB wrote the email. Then QB chose what to include based on the chatbot conversations sponsors had been having. (Loading times. Final webinar reminder. Outstanding items.) Then QB sent 83 emails in the next few minutes while Amelia went to sleep.
Each email was unique to that sponsor. Artisan owed us 13 specific things. Salesforce owed us 4. Monaco owed us 5.
The next day we got fewer inbound questions, not more. And usage of the QB chatbot went up because sponsors had seen QB’s email and trusted it.
The On-Site Chatbot Test: Humans Now Prefer the Agent
This week we’re loading in SaaStr AI Annual across 40 acres with 100+ contractors. Amelia is on-site walking the campus with WhisperFlow on her phone, talking directly to QB and 10K through Replit’s agent layer.
What started happening within two days: contractors began asking Amelia to ask the agents questions on their behalf.
“Can you ask your agent if this furniture invoice matches the manifest?” “Can you ask QB how many chairs should be in the speaker room?” “Can you ask if the Wi-Fi password is set?”
Used to be we’d run around campus on Segway scooters trying to find Ashley (our old internal joke: “ask Ashley” meant hours of waiting for an answer that might be wrong anyway). Now QB or 10K answers in seconds and it’s correct.
Zero humans want the human version of this anymore. The accuracy and speed are too good.
The reason it works: too many details. Even the best human on our team can’t hold 100+ sponsor configurations, 5,000+ parking passes, 40 acres of furniture orders, and live ticket data simultaneously. Agents can.
This is the chatbot use case people keep saying doesn’t work. It works.
Postgres vs. Salesforce: We’re Going the Other Direction
A question we get constantly from early-stage founders: “Why don’t you just dump Salesforce, run everything on Postgres, and save the money?”
We’re not just keeping Salesforce. We’re consolidating onto it.
The reasons:
Third-party agents. Agentforce, Artisan, Qualified, Momentum, and others are all optimized around Salesforce. 99% of the working GTM agents in B2B today assume Salesforce as the system of record. Walking away from that ecosystem means rebuilding fragile custom connectors for every tool.
Hiring. If we hire one more salesperson (we’re looking, by the way), they already know Salesforce. They don’t know a custom CRM I built on Postgres.
Headless works. Most of our Salesforce usage is now headless. The agents push and pull. We rarely log in. Whether the logic runs inside Salesforce or inside one of our agents, we don’t care, because the outcome is what matters.
Marc is moving fast. Every time I’m at Salesforce Tower or talking to Adam Evans, the team is genuinely stressed in the best way. They’re shipping. They’re building for agents. That’s the bet.
The loser in our stack isn’t Salesforce. It’s Marketo. We’re moving that data to Marketing Cloud because Adobe stopped pushing Marketo into the agent era.
What We Killed This Month: A $4,000/Year Newsletter Tool
We’ve used the same newsletter builder for six years. Roughly $4,000/year. It worked. Amelia spent ~3 hours/week assembling four weekly newsletters.
I gave 10K the HTML of one of our existing newsletters and a prompt: “Build me a newsletter builder that recreates this.”
10K built it. The vendor app is dead to us.
What 10K’s version does better:
Pulls articles automatically via WordPress API
Force-ranks article quality using Sonnet
Auto-pulls top tweets via X API
Auto-inserts sponsor ads in the right slots
Saves Amelia 90+ minutes per week
Time to build: about an hour for the first working version. A few more hours of iteration to get it to shipping quality.
This is the SaaSpocalypse story I keep writing. AI is not killing Workday tomorrow. The workflow is too rich. But these point solutions, these $3K-$10K/year tools that stopped shipping? They don’t survive 2026.
One Technical Note on How We Actually Run These
This part is a little nerdy but worth flagging because it’s unusual.
Most people building on Replit or Lovable ship to production and lose the agent layer. We don’t. 10K and QB live permanently in the Replit dev environment. We talk to them through Replit’s own agent, which has effectively unlimited context and remembers everything we’ve ever asked.
Translation: when Amelia asks “compare last year’s CTO attendance to this year,” she’s not talking to 10K’s UI. She’s talking to the Replit agent that knows 10K’s entire codebase, history, database, and goals.
It’s nerdy. It’s not how most people deploy. But it’s why 10K and QB feel like coworkers instead of dashboards.
What’s Next
Three things on our short list:
An orchestration layer between agents. Right now Amelia is the orchestration layer. 10K, QB, and the website agent can share data through Salesforce, but they can’t really talk to each other. We need a hierarchy.
Hiring a human to report to 10K. Specifically a marketer who wants to work with AI as a peer. The job spec is live on saastr.ai/jobs.
Pushing QB fully autonomous on-site. We built the customer-facing version. We’re not at 100% confidence to let 100 contractors talk to it directly without a human in the loop. By September, that gate is gone.
Cost Isn’t The Constraint
Three humans. 21 agents. $257/month to run two of the agents that do the most work.
Cost isn’t the constraint. The constraint is which workflows you’re willing to actually rewrite around agents instead of bolting agents onto the old workflows.
