How Much Does It Cost to Develop an AI Assistant?

What actually drives the cost

Three variables move the price of an AI assistant more than anything else: how much scope it covers, what compliance posture it needs, and where it has to live.

Scope is the number of workflows the assistant touches and how many systems it has to read from or write to. A single-purpose assistant that drafts one type of document is a different build than one that reads a chart, calls a vision model, and writes back into a clinical record.

Compliance is whether the data involved is regulated. An assistant that touches protected health information, cardholder data, or government records needs signed data agreements, audit logging, and access controls that a marketing chatbot never has to think about.

Hosting is where the thing runs once it is built. A shared cloud environment is cheap. A client-owned VPC with dedicated compute and no cross-tenant data path is not, and somebody has to own that infrastructure whether it is a full-time hire, a vendor, or a pod.

Scope: what changes at each price point

We price capacity, not features, which means the honest way to answer "what does scope cost" is to look at what a month of engineering capacity actually includes at each tier.

Key numbers

• Builder Pod: $5,000/month, one active build track, a pod lead plus a two-engineer bench, weekly ship
• Growth Pod: $10,000/month, two concurrent build tracks, a pod lead plus a three-engineer bench, hosting discount and priority support
• Enterprise Organization Pod: custom pricing, three or more parallel tracks, a dedicated senior lead plus 3-8 engineers, hosting included
• All three are month-to-month with 30 days' notice, no per-hour billing, no change orders

A single-workflow AI assistant, say a support-ticket triage bot or a document-summarization tool with one data source, is a Builder Pod problem: one build track is enough, and the two-engineer bench gives redundancy without idling capacity you are not using. See the full breakdown on the pods page.

An assistant that needs to run in parallel with a second initiative (an internal tool while the assistant ships, or a client-facing surface and an admin surface at the same time) needs two build tracks, which is what the Growth Pod buys along with architecture planning and bi-weekly strategy calls to keep the two tracks coherent.

An assistant program spanning multiple departments, each with its own workflow and its own compliance boundary, is an Enterprise conversation: three or more tracks running at once under one dedicated senior lead who owns the architecture across all of them. The pricing page has the full comparison.

Compliance: the driver that changes everything in regulated domains

If the AI assistant touches health records, financial account data, or government data, compliance is not an add-on line item, it is a design constraint from the first sprint.

We sign Business Associate Agreements on request and build to HIPAA-aligned controls from day one. We do not claim a "HIPAA certification," because HIPAA has no certification to hold; the honest claim is a signed BAA plus controls that hold up under audit. We also make a SOC 2 Type II report available under NDA on request. Full detail is on the security page.

Two shipped examples show what that looks like in practice. A compounding-pharmacy platform we built runs prescription routing across clinic, patient, and admin portals with a seven-year immutable audit log and 490+ unit tests, because a missed routing failover or a gap in the audit trail there is a compliance liability, not a bug to patch later. A separate build, a Medicare/Medicaid medical-billing audit dashboard, runs against a regulated claims dataset with the same posture: deployment inside the client's own cloud, access controls scoped to role, and an audit trail that survives a real review.

Neither of those examples is a pricing benchmark. They are process illustrations: what "compliance-driven cost" concretely buys is an audit log, role-based access, a signed BAA, and a deployment boundary that does not leak, not a bigger invoice for its own sake.

The same logic shows up outside healthcare. A public-sector spend-auditing engine we built runs eight fraud detectors entirely offline, in air-gapped mode, with zero external API calls, because the agency's financial data could not leave the premises under any circumstance. That is a hosting decision driven by compliance, and it is the same category of constraint an assistant handling PHI or cardholder data runs into: where the model runs and where the data sits are not separable questions once the domain is regulated.

Hosting and deployment: the line item people forget

Hosting is a real cost whether you build in-house, hire an agency, or run a pod, and it does not disappear just because nobody quotes it up front.

On a Builder Pod, hosting is a separate line item: you own the cloud account, you pay your own infrastructure bill, and the pod builds and deploys into it. On a Growth Pod, hosting comes with a discount as part of the plan. On an Enterprise Organization Pod, hosting is included as part of the custom terms, along with a priority SLA for anything that needs an urgent fix.

In every tier, code and infrastructure ship into the client's own cloud account and repository from week one, not a shared environment we control. If the engagement ends, the assistant keeps running; nothing in it calls a service that only we operate. That ownership structure is described in more detail on the how it works page.

Build vs. hire: what the numbers actually say

The alternative to a pod is not "free." It is a hiring cycle with its own cost and its own timeline, and the two do not compare on price alone.

Factor	In-house hire	Freelancer	Agency (fixed-bid)	Asaasin pod
Cost	~$250,000+/year fully loaded (salary, benefits, recruiting, overhead)	Hourly, highly variable by scope	Project-priced, usually opaque per hour	$5,000-$10,000/month, custom above that
Time to start	3-6 months to hire	Days to weeks	Weeks (scoping + contract)	Within 5 business days
Billing model	Salary + benefits	Per hour	Statement of work, change orders	Flat monthly, no per-hour billing
Compliance built in	Depends on the hire's background	Rarely a given	Varies, often extra cost	BAA, HIPAA-aligned controls, SOC 2 Type II report on request
Cancellation	Severance, notice period	Contract-dependent	Contract penalties common	30 days' notice, no long-term lock-in

The in-house figure of roughly $250,000 a year fully loaded is our own reference point from the pricing page, not a competitor benchmark, and it already includes benefits, recruiting, and overhead, so it should not be treated as a base salary you then mark up further.

A worked example: six months, one assistant

Take a medium-scope AI assistant project, the kind that reads from an existing system, calls a model, and writes back into a workflow, sized for a Growth Pod running two build tracks over six months.

Pod cost: $10,000/month x 6 months = $60,000, plus the hosting discount that comes standard on that tier. Work starts within five business days of the initial session and prototype approval, and the first shipped feature typically lands in week one or two, per the process described on how it works.

In-house hire cost over the same window: a fully loaded senior AI engineer runs roughly $250,000 a year, or about $20,833 a month prorated. Over six months of actual building time, that is $125,000, more than double the pod cost for the same calendar window, and that figure does not include the 3-6 months typically spent hiring before that engineer's first day. Add that hiring lag to the six months of building, and the in-house path takes roughly nine to twelve months to reach the same point the pod reaches in six.

Two things follow from that math. First, the pod is cheaper in raw dollars for a project sized to fit inside one to two build tracks. Second, and often the bigger factor for a founder or VP Eng under a deadline, the pod removes the hiring lag entirely, since the team is already assembled and starts within days rather than months. Neither number is a claim about what any specific competitor charges; it is the comparison between our own published pricing and our own published hiring-cost reference point.

What we cannot tell you

There is no independently sourced, dated benchmark for "what an AI assistant costs" across the market that we can cite here. Vendor pricing for AI assistant builds varies widely by scope, region, and whether the quote is per-project or per-hour, and none of that data comes with a URL we can point to. We would rather leave that gap open than print a number we cannot back. What we can state precisely is our own pricing, because it is published and does not change per conversation: Builder Pod at $5,000 a month, Growth Pod at $10,000 a month, Enterprise custom above that, all detailed on the pricing page.

The short version

An AI assistant's cost tracks scope, compliance, and hosting, in that order of visibility. Our own pricing is exact and published: Builder Pod at $5,000/month for one build track, Growth Pod at $10,000/month for two tracks with hosting discounted, Enterprise custom for three or more tracks with hosting included. Compliance (a signed BAA, HIPAA-aligned controls, a SOC 2 Type II report on request) is built into every tier rather than billed on top. Against a fully loaded in-house hire running roughly $250,000 a year and 3-6 months to onboard, a pod is both cheaper for a project sized to fit and faster to start, with work beginning within five business days.