Coco
Register for Beta

Credits vs seats — SaaS pricing for AI tools

Seat-based pricing charges per active user — a model that originated with productivity software and worked well when software was a passive tool a person used to do their own work. Credit-based pricing charges per unit of work — a model better suited to software that does the work itself, like AI agents. The shift matters for buyers because seat pricing under-prices heavy users and over-prices light ones, while credit pricing maps cost directly to output. For AI tools doing actual work autonomously, credits often expose the true unit economics and let buyers scale spend with usage instead of with team size.

Seat pricing — the legacy default

Seat-based pricing means each active user pays a recurring fee — monthly or annual, often with discounts for longer terms. The pattern originated in the on-premises software era and carried over to SaaS as the default revenue model for productivity tools.

Where seat pricing works well:

  • Productivity software. A word processor, spreadsheet, design app, project management tool. Each user does similar work; the software is a passive tool; the value per user is roughly proportional to access.
  • CRMs and engagement platforms. Each rep needs access; activity is roughly comparable; the seat-to-value ratio is consistent.
  • Communication tools. Slack, Teams, Zoom — value scales with how many people can participate; per-seat pricing maps to network effects.
  • Predictable per-user usage. Where consumption is bounded by working hours and the user can't scale their own throughput.

Where seat pricing breaks:

  • Heavy-use scenarios. A single user driving 10x average usage pays the same as average, transferring cost to the vendor.
  • Light-use scenarios. Occasional users pay the full seat price, transferring cost to the buyer.
  • Software that does work autonomously. AI co-workers don't have the natural usage bound of working hours. A team of 5 with AI could plausibly drive the output of 50 humans — paying for 5 seats.
  • Burst-usage. A team running heavy one month and light the next pays the same in both.

The fundamental problem with seat pricing for AI is that the unit of value isn't the seat — it's the work the AI does. A seat is a proxy that breaks down when the software is doing the work instead of helping a human do it.

Credit pricing — the AI-era model

Credit-based pricing charges per unit of work. The credit is the abstraction: each action the software takes consumes a defined number of credits, and the buyer purchases credit packs or subscribes to a credit allowance.

What credits typically represent:

  • Units of compute. API calls to an LLM, processing cycles for a heavy task.
  • Units of output. A drafted email, an enriched record, a generated brief, a translated document.
  • Units of action. An external send, a CRM write, a third-party API call with per-call cost.
  • Units of a vendor service that has variable per-action cost. Where the vendor pays a real per-action cost downstream (data enrichment, ad-buying, AI inference), credits pass that cost structure through to the buyer.

The mapping makes economics transparent. The buyer sees the per-action cost in the platform before approving the action. The vendor's margin is built into the credit-to-action conversion, but the unit cost is visible.

Credit pricing maps better than seat pricing to AI work because:

  • The unit of value matches the unit of cost. Both are the work the AI does.
  • Usage scales independently of team size. A small team running heavy AI work pays for the work, not for being a small team.
  • Bursty usage is fair. A team that runs heavy in one month and light in the next pays the actual amount in each.
  • Hiring doesn't trigger a tax. Adding a new team member to an AI co-worker doesn't add a new seat cost if the team's overall AI usage is unchanged. This is why GTM automation increasingly prices by credit.

The trade-off is predictability. Seat pricing is predictable; credit pricing is variable. Most credit-priced products mitigate this with monthly allowances, caps, and rollover policies that smooth the variability.

Hybrid models

Many AI tools use hybrid pricing that combines elements of both.

Seat + usage tier. A flat monthly seat fee includes a credit allowance; usage above the allowance is metered. Most enterprise AI tools land here. The seat fee anchors the revenue; the metering captures variable value.

À la carte credit packs. A base subscription with a fixed credit allowance, plus the ability to purchase additional credit packs on demand. Useful for spiky usage patterns.

Pooled credits across a team. Team-level credit allowance shared across all users. No per-seat tax; the team optimizes credit usage across members. This is the model that scales best for teams growing through hiring.

Tiered usage with overflow. A flat fee covers a defined usage tier; exceeding the tier triggers a higher fee structure or per-unit overflow billing. Common in cloud infrastructure; increasingly common in AI tools.

Each hybrid model trades off predictability against value-aligned pricing. The right model depends on the team's usage profile and the buyer's tolerance for variable billing.

Buyer math — when each makes sense

Concrete scenarios:

  • Small, even-usage team. A 5-person sales team with consistent monthly outreach. Seat pricing is simple: "5 seats × monthly rate." Credit pricing adds variability that may not be worth it.
  • Heavy-usage team. Thousands of drafts, tens of thousands of enrichments per month. Credit pricing is typically dramatically cheaper, because seat vendors build in margin that assumes lower per-seat usage.
  • Bursty usage. Heavy months and light months. Credit pricing is fairer because the buyer pays for actual usage.
  • Team scaling rapidly. Credit pricing avoids a seat tax on each new hire; pooled credits let the team grow without proportional cost growth.
  • Light, exploratory usage. Credit-based products often have free or low-cost entry tiers. Seat-priced products have fixed cost regardless of usage, making exploration more expensive.
  • Enterprise procurement. Annual contracts often favor seat pricing because finance teams prefer fixed line items. Credit pricing requires usage forecasting some procurement teams aren't set up for.

Neither model is universally better. The right choice depends on usage profile, growth trajectory, and procurement preferences.

Credit pricing pitfalls

Credit pricing isn't immune to its own failure modes.

Opaque costs. Some products advertise credits but hide the per-action conversion. The buyer signs up, runs a workflow, and finds out the action cost more than expected.

Hidden multipliers. Variable credit costs for similar actions, with the multiplier depending on factors the buyer can't predict.

Low credit-to-action conversion. A credit allowance that sounds generous but buys very few real actions. The math reveals itself after the buyer has committed.

No usage caps. A buyer running an autonomous workflow that consumes credits faster than expected hits an unexpected bill. Good credit pricing includes soft and hard caps.

The right credit model surfaces the per-action cost up front, before approval. The buyer sees what the workflow will cost before deciding to run it. This transparency is what makes credit pricing worth its variability. Compare how AI SDRs price differently from AI co-workers for a concrete example of how the model choice shapes buyer math.

How Coco prices

Coco prices by credit. The cost of each workflow is visible in the plan card before any run.

  • About 4-6 credits per drafted outreach email. 100 drafts/month ≈ 400-600 credits.
  • About 1 credit per enriched contact. 1,000 records/month ≈ 1,000 credits.
  • About 8 credits per pre-meeting brief. 50 meetings/month ≈ 400 credits.
  • About 25 credits per design-partner search. 4 list-builds/month ≈ 100 credits.

Plans:

  • Hobby. $0/month with 1,000 credits. No card. Enough to run real workflows before deciding whether to pay.
  • Founder. $40/month with 5,000 credits. Adds voice training, scheduled runs, à la carte top-ups, unlimited tool connections.
  • Team. Custom pricing with pooled credits across the team, shared memory, admin audit trail, SOC 2 / DPA on request. No per-seat tax.

Every external action gates on approval at the start, so the buyer sees the credit cost before the action runs. See the full pricing page → or why Coco → for the broader argument behind the credit-based model.

Frequently asked questions

Why are some AI tools still priced per seat?

Several reasons: legacy revenue models from the SaaS era, predictable line items for finance teams, simpler procurement for enterprise buyers, and in some cases a margin advantage for the vendor when heavy users subsidize light users. None of these are bad reasons; they just match different buyer profiles.

How do I compare seat vs. credit pricing?

Estimate your team's expected usage in concrete terms (drafts per month, enrichments per month, briefs per month, etc.). Run the math both ways: seat pricing × team size vs. credit pricing × estimated usage. Add a margin for forecast error (typically 20-50%). The cheaper option for your usage profile is the cheaper option. For most credit-pricing fits — heavy usage, bursty usage, or rapid team growth — the gap is large enough that the comparison is unambiguous.

Are credit-priced tools more expensive?

Depends on usage. For heavy users, credit pricing is typically dramatically cheaper than the seat equivalent — sometimes 50-80% cheaper. For light users with predictable usage, a flat seat fee is sometimes cheaper than the credit equivalent. Run the math against your specific usage profile.

Do credits expire?

Varies by vendor. Coco's credits roll over for 30 days, so an unused month doesn't punish the buyer for a slow week. Some vendors expire credits at the end of the billing cycle; some allow longer rollover or none at all. Worth checking explicitly during evaluation.

Can I cap monthly credit spend?

Most credit-priced tools offer caps. Coco supports both soft caps (alerts when a threshold is hit) and hard caps (limits that block further runs) on the Founder tier and above. Hard caps are useful as a safety net against runaway autonomous workflows; soft caps are useful for visibility into usage patterns.