Sierra vs Decagon: AI Agent Pricing, Sierra AI Competitors and Decagon Pricing Compared

Sierra and Decagon are the two enterprise AI customer service agents that show up on almost every US shortlist, and neither one will tell you what it costs. Both pricing URLs return a 404. So we compared them on the only hard evidence either company publishes: the performance numbers in their own customer case studies. We pulled every case study from both sitemaps on September 2, 2026 and counted.

The result is the most useful thing in this article. Sierra publishes a median resolution rate of 70% across the 14 case studies that carry one. Decagon publishes a median of 70% across the 8 case studies that carry an absolute rate. The two vendors' own marketing produces the same number. Whatever separates them, it is not the performance either one is willing to put in print.

Sierra vs Decagon at a glance

What Decagon publishes about its own results

Decagon runs a leaner case study program than Sierra, 22 pages against 83, but it is noticeably more willing to attach a number to each one. Sixteen of its 22 case studies carry a hard metric. Here are the absolute deflection and resolution rates, as Decagon labels them.

A second group of Decagon case studies reports a change rather than a level, which is a different and weaker claim: ClassPass, Dutchie and Rippling each report a 32% increase in deflection, and Notion reports a 2x increase. An increase of 32% tells you nothing about where the number ended up. When you read either vendor's material, sort the levels from the deltas before you compare anything.

Decagon also publishes cost outcomes that Sierra mostly does not: Chime reports a 60% decrease in customer support costs, Curology a 65% reduction, and Notion an "ask for human" rate of 3.4%. That last metric is the honest inverse of a deflection rate and it is the one worth asking both vendors for, because it cannot be flattered by a generous definition of resolved.

Sierra's published rates, for comparison

Sierra's numbers sit in the same band. Funnel Leasing at 94% and Ramp at 90% anchor the top, Airtable is at 80%, Wilson 77%, Casper 74%, and a cluster of Homebase, OluKai, SoftBank and StorEase all land on exactly 70%. ScottsMiracle-Gro is 65%, AOL 64% and Capital on Tap 60%. The full table with Sierra's own labels is in our Sierra AI pricing breakdown, alongside the per-contact arithmetic.

One difference in disclosure is worth naming. Of the 51 Sierra case studies we retrieved, 31 publish no performance metric at all. SiriusXM's page publishes that SiriusXM has 34 million subscribers, which is a fact about SiriusXM. Sierra has the more impressive logo wall and Decagon has the higher proportion of pages that commit to a number. Neither of those is the same as being better, but if you are building a business case from public evidence, Decagon gives you more to work with per case study and Sierra gives you more reference customers to call.

The real difference is the pricing shape, not the performance

Since the published performance is a tie, the decision moves to how each vendor wants to bill you, and here they genuinely differ.

Sierra sells per resolution only. You pay when the agent resolves an issue, and Sierra states that unresolved conversations and human escalations are, in most cases, not charged. Decagon offers per conversation or per resolution, and argues on its own comparison page that most enterprises pick per conversation for "predictable costs and to avoid negotiating what counts as a 'resolution.'"

That is a competitor arguing its own book, and it should be read that way. It is also the sharpest framing of the real trade-off in this category. A per-conversation meter is countable by both sides on day one and produces a forecast your CFO can hold you to. A per-resolution meter aligns the vendor's incentive with your outcome, which is genuinely better in principle, but it makes your invoice depend on a definition. Who decides a conversation was resolved? Does a customer coming back two days later create a second billable resolution? Does a resolution the customer rated one star still bill?

There is no universally right answer. There is a right answer for your organization, and it depends on whether you would rather have budget certainty or incentive alignment. Teams with a hard annual number to hit tend to choose the conversation meter. Teams that have been burned by a bot that deflected tickets without solving anything tend to choose the resolution meter and accept the forecasting pain.

What either one is likely to cost

Neither vendor publishes a rate, so the only honest anchor is the vendor that does. Fin, formerly Intercom, publishes $0.99 per outcome with a 50-outcome monthly minimum and states plainly that there are no setup, integration or platform fees. Salesforce Agentforce publishes $2.00 per conversation, charged whether or not anything is resolved, or Flex Credits at $0.005 each with a standard action costing 20 credits.

Use those as your negotiating floor. If a Sierra or Decagon quote lands materially above $0.99 per resolved contact once you have amortized the platform fee across your actual volume, you are paying a premium for the forward-deployed engineering and the enterprise complexity, and you should be able to name what that premium buys you. Sometimes it clearly does buy something: a regulated financial services rollout across chat and voice in four languages is not the same product as a help center bot, and pretending otherwise leads to bad decisions in the other direction.

Voice is where the premium is easiest to justify and hardest to budget. Both vendors sell voice agents, and voice adds infrastructure that has nothing to do with the AI: numbers, routing, carrier costs and call recording. Teams piloting a voice agent often find they need a proper cloud phone system with local and toll-free numbers in place before the agent has anywhere to answer, and that line item rarely appears in the vendor's proposal.

Which one should you shortlist?

Shortlist Sierra if you are a large brand with complex, regulated or multi-channel support, you want the vendor's own engineers to configure and own the agent, and you have the volume to make a six-figure commitment rational. Its published top-end results, Ramp at 90% and Funnel Leasing at 94%, come from exactly this profile.

Shortlist Decagon if you want a per-conversation meter you can forecast, you want your own team to own and iterate the agent rather than filing requests with the vendor, and you need to be live in weeks. Decagon claims most enterprises go live in weeks rather than months and that agent logic stays in its Agent Operating Procedures rather than moving into an SDK, which is a real portability argument if you believe it. Verify it in a reference call.

Shortlist neither if your annual contact volume is small enough that a platform minimum would dominate your bill. Below roughly 100,000 contacts a year, a published-rate vendor with no floor, or a ready-made agent you can deploy and cancel, will almost always cost less and take less of your time. You can browse AI agents for customer service by the job you need done, and our AI agent platforms comparison puts the published rates of thirteen platforms side by side.

The short version

On published performance, Sierra and Decagon are a tie at a 70% median. On disclosure, Decagon attaches a number to a higher share of its case studies and Sierra has more reference customers. On pricing, both hide the rate, but Decagon will sell you a per-conversation meter and Sierra will not. Benchmark whatever either one quotes against Fin's published $0.99 per outcome, negotiate the definition of a resolution harder than the rate itself, and ask both vendors for an ask-for-human rate rather than a deflection rate.