Custom AI Support Agent vs Off-the-Shelf (Fin, Sierra, Decagon): What Does Each Really Cost in 2026?

Short answer: off-the-shelf AI support agents like Intercom Fin (from $0.99 per resolution), Sierra (roughly $1.50 per resolution, six-figure minimums) and Decagon (median annual contract value around $432,750) are cheaper than building your own below roughly 3,000–5,000 resolved conversations a month — figures reported in Aissist's 2026 AI agent pricing benchmark. Above that — or once a vendor's enterprise floor kicks in — a custom-built agent on your own LLM stack (typically $25,000–$80,000 to build, plus $1,500–$5,000/month to run) usually wins on total cost, and you own the data and the roadmap.

Every vendor in this space now bills on "outcomes" rather than seats, which sounds fair until you read the fine print. Below, we break down what Fin, Sierra and Decagon actually charge, what it costs to build the equivalent agent yourself, and the volume at which the maths flips.

What do Fin, Sierra and Decagon actually charge?

The short answer is: it depends heavily on how each vendor defines a billable "outcome," and none of the three publish full pricing on their websites except Fin.

Vendor	Pricing model	Unit price	Floor / minimum
Intercom Fin	Per resolution	$0.99 per outcome ($9.99 for a qualified lead)	50 outcomes/month minimum; $29–$139 per seat/month if bundled with Intercom's helpdesk
Sierra	Per resolution (custom quote)	~$1.50 per resolution	Annual contracts typically start around $150,000, with $50,000–$200,000 implementation fees (Aissist, 2026)
Decagon	Per-conversation or per-resolution (custom quote)	~$0.99 per conversation reported at the median deal	Median annual contract value ~$432,750, range $105,000–$923,183 (Aissist, 2026)

Two things stand out. First, Fin is the only one of the three with a self-serve price list; Sierra and Decagon require a sales call — Decagon does not publish pricing at all, and their reported minimums put them firmly in enterprise-contract territory even for a lean, venture-backed team. Second, "resolution" is not a standard unit — Decagon itself says most of its customers actually prefer flat per-conversation pricing over per-resolution billing, precisely because outcome definitions are so easy to dispute.

Per-resolution vs per-interaction: what's the catch?

Outcome-based pricing is marketed as "you only pay when it works," but three details change the real bill:

• What counts as an outcome varies by vendor. Fin counts one billable outcome per conversation even across multiple actions, but charges separately for a "qualification" ($9.99) versus a plain resolution ($0.99) — a distinction worth checking against your own use case before you estimate volume.
• Per-conversation pricing bills you even when the bot fails. A platform charging per interaction rather than per resolution gets paid whether or not the customer's issue is actually solved, which is why Decagon says most of its own customers choose that model — it is more predictable for the vendor, not necessarily cheaper for you.
• Enterprise floors dominate at low-to-mid volume. At 2,000 monthly resolutions, Sierra's usage cost alone would be roughly $3,000/month — but you are contractually on the hook for a chunk of that $150,000+ annual minimum regardless (Aissist, 2026). Below a few thousand conversations a month, you are effectively paying for capacity you are not using.

For context on what "AI-native" support economics look like when they work: platforms built for this from day one report first-contact resolution rates of 55–70% and average handling times under three minutes, driving cost-per-resolution down to roughly $1–$3 — versus a Gartner-cited median of $13.50 for a traditional agent-assisted contact. That gap is the whole business case for AI support; the question is which delivery model captures it most cheaply for your specific volume.

What does it cost to build your own AI support agent?

Building on your own LLM stack (typically Claude or GPT-class models plus a retrieval layer over your help centre, macros and CRM) breaks down into rough tiers based on scope, per DestiLabs' 2026 build-cost analysis of 50+ AI agent projects:

Tier	Capability	Build cost	Ongoing monthly cost
1. Conversational agent	Answers from your knowledge base, help docs, product catalogue	$8,000–$25,000	$500–$2,000
2. Task-execution agent	Adds real actions — refunds, CRM updates, booking changes via API	$25,000–$80,000	$1,500–$5,000
3. Multi-agent system	Specialised agents handling research, drafting and execution together	$80,000–$200,000	$4,000–$12,000

For most seed-to-Series-A support use cases, Tier 1 or Tier 2 covers the job a Fin or Decagon deployment would otherwise do. The ongoing monthly figure covers LLM API calls, vector database hosting and basic maintenance — the same inference costs a vendor is quietly folding into their per-resolution price, minus their margin.

At what volume does building beat buying?

Take a startup resolving 3,000 support conversations a month with AI (a realistic number once you're past 50–100 employees or have a self-serve product with real usage). Roughly, year-one costs compare like this:

• Fin (standalone): 3,000 × $0.99 ≈ $2,970/month ≈ $35,600/year — still the cheapest option at this volume, with no build time.
• Sierra: usage cost alone would be ~$4,500/month, but the contractual floor (~$150,000/year) plus implementation ($50,000–$200,000) means year one lands around $200,000–$350,000—regardless of whether you use the full capacity.
• Custom build (Tier 2): $25,000–$80,000 to build plus ~$2,000/month to run ≈ $49,000–$104,000 in year one, then roughly $18,000–$60,000/year after that as a fixed run-rate that barely moves with conversation volume.

The crossover is rarely about volume alone — it's about which side of an enterprise floor you land on. Under a few thousand resolutions a month, a self-serve tool like Fin is usually the pragmatic choice: no build risk, no minimum commitment. Once you're quoted a six-figure Sierra or Decagon contract, or once your support volume is genuinely enterprise-scale, a custom build on your own model stack starts to win on cost and gives you full ownership of the data, the prompts and the escalation logic, rather than a vendor's black-box "resolution" definition.

When off-the-shelf still wins

Buying makes sense when: you need to ship in weeks not months; your support volume is low enough that you'll never clear a vendor's usage-based floor; or you lack in-house ML/engineering capacity to maintain a custom agent's evals, guardrails and observability over time (a real, ongoing cost most build-cost estimates leave out). If any of those apply, start with Fin's self-serve pricing rather than an enterprise quote from Sierra or Decagon — you can always migrate to a custom build later once volume and requirements are proven. For a deeper look at how much a first AI build typically costs before you commit either way, see our breakdown of AI MVP costs in 2026.

FAQ

Is Intercom Fin cheaper than Sierra or Decagon?

Usually, yes, at low-to-mid volume. Fin's published $0.99-per-outcome rate with a 50-outcome minimum has no enterprise floor, whereas Sierra and Decagon both report typical annual contracts starting around $150,000–$432,750, which only pays off at genuinely high resolution volumes.

What counts as a "resolution" for billing purposes?

It varies by vendor and is one of the biggest hidden costs in this category. Fin bills one outcome per conversation regardless of how many actions it takes; Decagon offers both per-conversation and per-resolution models and reports that most customers prefer per-conversation because it avoids disputes over what counts as "resolved."

At what point should we build our own AI support agent instead of buying one?

As a rule of thumb, once you're resolving more than roughly 3,000–5,000 AI conversations a month, or once you've been quoted a six-figure annual minimum by an enterprise vendor, a custom build (typically $25,000–$80,000 plus $1,500–$5,000/month to run) usually costs less over 12–18 months and gives you full control of the logic.

Does building our own agent save money if support volume is small?

No. Below a few thousand resolutions a month, the fixed build cost and ongoing maintenance overhead of a custom agent almost always exceed a self-serve, per-resolution tool like Fin. Build only once volume or vendor floors justify it.

What's missing from most build-vs-buy cost comparisons?

Ongoing evals, guardrails and observability — the work that keeps a custom agent from hallucinating or going off-script in production. This is genuine, recurring engineering time that off-the-shelf vendors bundle into their per-resolution price but that a DIY cost estimate frequently omits.

Get an honest build-vs-buy read on your support stack

If you're weighing a Sierra or Decagon quote against building your own agent, UZO Lab runs a paid AI Prototyping & R&D Sprint (day rate £500, or £2,000 for a full week) to prove out a working prototype against your real support data before you commit to either a six-figure contract or a custom build. For ongoing support once you've decided, our LLM Integration and Automations & Internal Tools work sits under Growth retainers from £5,000/month. Book a free 30-minute workshop to walk through your ticket volume and get a straight answer on which path is cheaper for you.

Sources: Fin AI Agent Pricing; AI Agent Pricing Benchmark 2026, Aissist.io; Pricing the AI Agent Economy, Decagon; AI Agent Development Cost in 2026, DestiLabs; 30 AI Customer Service Statistics for 2026, Lorikeet; Decagon Pricing Breakdown, eesel AI.