What We Learned Building an AI Platform for SMBs
Enterprise AI platforms ignore small businesses. Here's what we learned building Alpha Agent for the companies that need AI the most.
The gap nobody talks about
Enterprise AI platforms are built for companies with 500+ employees and six-figure software budgets. Their pricing pages say “Contact Sales.”
Then there’s everyone else. The 10-person agency. The solo consultant who just hired employee number two. These businesses need AI just as much — often more, because they don’t have headcount to absorb repetitive work. But enterprise platforms don’t work for them, and consumer tools don’t take security seriously enough for client data.
We built Alpha Agent to fill that gap. Here are five things we got wrong before we got them right.
1. Security shouldn’t be gated by pricing tier
Early on, we considered the standard playbook: basic security on the starter plan, “enterprise-grade” security for the top tier.
Then we talked to a four-person law firm. They needed container isolation, encrypted secrets, and audit logging — not because a compliance officer told them to, but because exposing a client’s legal strategy could end their practice. Their budget was a few hundred dollars a month.
The businesses with the least security resources often have the most to lose. A data leak doesn’t bankrupt a Fortune 500 company. It can bankrupt a five-person firm.
So we made every security feature available on every plan. Container isolation, KMS encryption, read-only filesystems, dedicated resource limits. The $29/month Individual plan gets the same security architecture as a team of 100. Security is infrastructure, not a feature add-on.
2. Per-task pricing beats per-seat pricing for small teams
Per-seat pricing works when usage is roughly equal across users. In a 500-person company, the average evens out. In a 5-person company, it does not.
A marketing agency: the founder used AI 40+ times a day. The junior designer used it twice a week. Under per-seat pricing, they’d both pay the same. Small teams can’t afford full price for occasional users — but they can’t restrict access either, because the designer who suddenly needs to research a client pitch shouldn’t hit a paywall.
Per-task billing solves this. Everyone gets access. You pay for what you use. We detailed how this compares to flat-rate platforms separately, but the core insight is that per-task pricing aligns cost with value in a way per-seat never can for small teams.
3. Setup time matters more than feature count
Most SMBs evaluate tools in the first 30 minutes. Not the first month. Thirty minutes.
A 12-person consulting firm tried three AI platforms before us. Each required configuring SSO, setting up user provisioning, and scheduling onboarding calls. By the time they could use the tool, the urgency that drove the evaluation had passed.
We rebuilt onboarding around one constraint: a new user should run their first AI task within 10 minutes of signing up. Not configuring. Doing productive work.
That meant opinionated defaults instead of configuration wizards. Pre-built integrations that activate with a single OAuth flow. A workspace that works out of the box and customizes later.
Feature count is a vanity metric. Time-to-value determines whether a small business adopts a tool or abandons it.
4. Provider choice prevents lock-in (and saves money)
When we launched, we defaulted users to a single AI provider. Simpler to build, simpler to document.
Users pushed back immediately. A development shop wanted Claude for code review but GPT for client-facing content. A research firm wanted Gemini for multimodal analysis but Claude for summarization. Every team had different preferences grounded in performance differences they’d actually measured.
Provider lock-in is also a cost problem. Forcing a team to use a $15-per-million-token model for a task a $0.50 model handles just as well is wasteful. Small businesses feel that waste immediately.
Alpha Agent now supports 15+ providers. Users bring their own API keys, choose models per task, and switch without losing their workspace. The platform is the workflow layer, not the model layer.
5. Cost visibility is table stakes, not a premium feature
We almost got this wrong. Early discussions included making cost analytics a “Pro” feature, because that’s what competitors did.
Then we looked at churn data. The users who left earliest couldn’t tell whether the platform was worth the money. Not because it wasn’t — because they had no way to verify it. A monthly invoice that says “$147” tells you nothing about value delivered.
So we made per-task cost tracking, provider breakdowns, and budget alerts available to every user. When a solo consultant sees that AI-assisted proposal writing costs $3.20 per proposal and saves two hours, they don’t churn. When an agency owner sees a scheduled task burning money with no output, they cancel the task, not the subscription.
Cost visibility isn’t a premium analytics feature. It’s the basic information people need to decide whether to keep paying you.
The common thread
Every one of these lessons comes down to the same realization: small businesses are not small enterprises. They don’t evaluate tools the same way or absorb costs the same way. Building for them means rethinking assumptions, not adjusting price points.
The companies that need AI the most — the ones without the headcount to absorb manual work — are the ones least served by the current market. That’s the gap we’re building for.
See our pricing to understand how per-task billing works for teams of every size, or start with the Individual plan and see the cost of every task from day one.