Sierra and Decagon are the two 2023-founded pure-play AI customer-support agents that enterprises actually shortlist against each other. Both deploy autonomous agents that resolve a conversation end to end — a refund, an identity check, a subscription change — and both take the backend action rather than just answering from a help center. They diverge on shape and on who holds the controls. Sierra is a voice-first, outcome-billed CX platform aimed at high-volume consumer brands, where the agent executes transactions inside regulated (PCI-scope) workflows and the vendor runs a white-glove deployment. Decagon is a control-first deflection engine aimed at technical SaaS orgs, where non-engineers author the agent’s behavior as Agent Operating Procedures and the meter runs per resolution. The routing question: do you want a vendor-managed voice-and-transaction agent for a consumer brand, or a structured, lower-floor deflection agent your own ops team authors and tunes?
Where Sierra wins
Voice is the primary channel, and the agent completes the transaction. Sierra’s voice agents have passed text as the main surface, which matters when your contact-center volume is phone-first — most support-agent tools are still chat-led. And because the platform is Level 1 PCI-compliant, an agent can take a payment, process a refund, or save a cancellation inside the call without handing off to a human. That is the difference between deflecting a ticket and resolving the request.
Vendor-managed deployment with fast agent authoring. Sierra runs a high-touch, white-glove model, and its Ghostwriter tool (launched March 2026) builds a production-ready agent from an SOP, a call transcript, a whiteboard sketch, or a plain-English description. For a team standing up a net-new agent without an engineering queue, that shortens the initial build.
Brand, scale, and security posture for consumer enterprises. Sierra reports working with around 40% of the Fortune 50 — WeightWatchers, SiriusXM, Sonos, Nordstrom, ADT — and closed a $950M round at a $15.8B post-money valuation in May 2026 on roughly $200M ARR. For a regulated consumer brand where the security review and the reference list carry the decision, that track record is concrete rather than a roadmap.
Where Decagon wins
Your ops team authors the behavior — AOPs, not a vendor ticket. Decagon’s differentiator is Agent Operating Procedures: non-technical CS and support-ops staff define multi-step workflows in plain language. Once the initial engineering-led integration is wired, the people who own the customer experience change agent behavior directly instead of routing every tweak through the vendor. If you want maximum operational control, that authoring surface is the reason to pick it.
A lower entry floor for upper-mid-market volume. Decagon’s reported structure is roughly a $50K annual platform fee plus usage — materially below Sierra’s six-figure floor. For an org doing tens of thousands of conversations a month that wants ticket deflection without a consumer-brand voice deployment, the payback math starts sooner.
QA and experimentation built into the platform. Watchtower runs always-on QA and Experiments does live A/B testing of agent behavior, so ops teams monitor and tune resolution quality without exporting transcripts to a separate tool. For a data-driven support org that treats agent quality as a metric to move, that closed loop is native rather than bolted on.
Pricing reality
Both are custom-quoted with no public price list, so compare by shape and floor, not sticker. Decagon’s reported model is a ~$50K annual platform fee plus usage — either per-conversation or per-resolution (reportedly around $0.50 per resolution), with conversation minimums on enterprise contracts. Sierra is outcome-based with no per-seat component: you pay when an agent resolves an interaction, saves a cancellation, or completes an upsell, and annual contracts land in the $150K–$350K+ range.
The crossover is floor versus outcome-alignment. Decagon’s entry point is roughly a third of Sierra’s, so on pure deflection volume it pays back faster and is the safer bet under a few thousand conversations a month. Sierra’s outcome billing ties cost to resolved results rather than headcount — attractive at high volume where a resolution is worth far more than the fee — but “resolution” is a negotiated contract definition, so what counts as a billable success is the single most important term to pin down before signing either one. The number buyers under-forecast on Decagon is the usage tail once conversation minimums and per-resolution charges stack; the number they under-forecast on Sierra is that the six-figure floor only pays back if your volume and outcome value are genuinely enterprise-scale.
Implementation effort
Neither is a plug-in. Decagon’s onboarding runs about six weeks from signature to production, and the first weeks are engineering-led — your team builds the API connections to CRM, helpdesk, and data sources before any CX person can author an AOP; budget ops time, not just license spend. Sierra’s is a vendor-managed enterprise project: connecting to billing, CRM, and PCI-scope payment flows means a security review, so scope a single high-volume journey first, set a containment and accuracy baseline against your current tooling, and expand only after the agent beats it on a contained pilot. For either, keep hard limits and human review on irreversible actions — refunds above a threshold, cancellations — and log every agent-executed transaction for reconciliation from day one.
Verdict
Pick Sierra when you are a high-volume consumer brand, when voice is a primary channel, when the agent has to execute transactions inside PCI-scope workflows, and when you want a vendor-managed deployment with an enterprise reference list and outcome-aligned billing. It is the voice-first, transaction-executing, white-glove pick.
Pick Decagon when you want your own ops team authoring agent behavior in plain language, when a lower entry floor makes the deflection math pay back sooner, when built-in QA and A/B testing matter, and when your business is internet-native and ticket-deflection-led rather than voice-and-payments-heavy. It is the control-first, lower-floor, ops-authored pick.
If you can’t decide, default to Decagon: the lower platform floor, per-resolution alignment, and ops-authored AOPs let you prove deflection on your real volume before committing to a consumer-brand-scale contract. Flip to Sierra the moment voice becomes a primary channel or the agent needs to take payments and process refunds inside a regulated flow.
Pick neither when your support volume is low enough that a five- or six-figure floor can’t pay back — a lighter, seat-priced deflector on your existing help desk wins (Zendesk or Intercom Fin) — or when you would rather consolidate agents inside the CRM you already run (Salesforce Agentforce) than add a high-touch specialist on top.
Sierra and Decagon are the two 2023-founded pure-play AI customer-support agents that enterprises actually shortlist against each other. Both deploy autonomous agents that resolve a conversation end to end — a refund, an identity check, a subscription change — and both take the backend action rather than just answering from a help center. They diverge on shape and on who holds the controls. Sierra is a voice-first, outcome-billed CX platform aimed at high-volume consumer brands, where the agent executes transactions inside regulated (PCI-scope) workflows and the vendor runs a white-glove deployment. Decagon is a control-first deflection engine aimed at technical SaaS orgs, where non-engineers author the agent’s behavior as Agent Operating Procedures and the meter runs per resolution. The routing question: do you want a vendor-managed voice-and-transaction agent for a consumer brand, or a structured, lower-floor deflection agent your own ops team authors and tunes?
Where Sierra wins
Voice is the primary channel, and the agent completes the transaction. Sierra’s voice agents have passed text as the main surface, which matters when your contact-center volume is phone-first — most support-agent tools are still chat-led. And because the platform is Level 1 PCI-compliant, an agent can take a payment, process a refund, or save a cancellation inside the call without handing off to a human. That is the difference between deflecting a ticket and resolving the request.
Vendor-managed deployment with fast agent authoring. Sierra runs a high-touch, white-glove model, and its Ghostwriter tool (launched March 2026) builds a production-ready agent from an SOP, a call transcript, a whiteboard sketch, or a plain-English description. For a team standing up a net-new agent without an engineering queue, that shortens the initial build.
Brand, scale, and security posture for consumer enterprises. Sierra reports working with around 40% of the Fortune 50 — WeightWatchers, SiriusXM, Sonos, Nordstrom, ADT — and closed a $950M round at a $15.8B post-money valuation in May 2026 on roughly $200M ARR. For a regulated consumer brand where the security review and the reference list carry the decision, that track record is concrete rather than a roadmap.
Where Decagon wins
Your ops team authors the behavior — AOPs, not a vendor ticket. Decagon’s differentiator is Agent Operating Procedures: non-technical CS and support-ops staff define multi-step workflows in plain language. Once the initial engineering-led integration is wired, the people who own the customer experience change agent behavior directly instead of routing every tweak through the vendor. If you want maximum operational control, that authoring surface is the reason to pick it.
A lower entry floor for upper-mid-market volume. Decagon’s reported structure is roughly a $50K annual platform fee plus usage — materially below Sierra’s six-figure floor. For an org doing tens of thousands of conversations a month that wants ticket deflection without a consumer-brand voice deployment, the payback math starts sooner.
QA and experimentation built into the platform. Watchtower runs always-on QA and Experiments does live A/B testing of agent behavior, so ops teams monitor and tune resolution quality without exporting transcripts to a separate tool. For a data-driven support org that treats agent quality as a metric to move, that closed loop is native rather than bolted on.
Pricing reality
Both are custom-quoted with no public price list, so compare by shape and floor, not sticker. Decagon’s reported model is a ~$50K annual platform fee plus usage — either per-conversation or per-resolution (reportedly around $0.50 per resolution), with conversation minimums on enterprise contracts. Sierra is outcome-based with no per-seat component: you pay when an agent resolves an interaction, saves a cancellation, or completes an upsell, and annual contracts land in the $150K–$350K+ range.
The crossover is floor versus outcome-alignment. Decagon’s entry point is roughly a third of Sierra’s, so on pure deflection volume it pays back faster and is the safer bet under a few thousand conversations a month. Sierra’s outcome billing ties cost to resolved results rather than headcount — attractive at high volume where a resolution is worth far more than the fee — but “resolution” is a negotiated contract definition, so what counts as a billable success is the single most important term to pin down before signing either one. The number buyers under-forecast on Decagon is the usage tail once conversation minimums and per-resolution charges stack; the number they under-forecast on Sierra is that the six-figure floor only pays back if your volume and outcome value are genuinely enterprise-scale.
Implementation effort
Neither is a plug-in. Decagon’s onboarding runs about six weeks from signature to production, and the first weeks are engineering-led — your team builds the API connections to CRM, helpdesk, and data sources before any CX person can author an AOP; budget ops time, not just license spend. Sierra’s is a vendor-managed enterprise project: connecting to billing, CRM, and PCI-scope payment flows means a security review, so scope a single high-volume journey first, set a containment and accuracy baseline against your current tooling, and expand only after the agent beats it on a contained pilot. For either, keep hard limits and human review on irreversible actions — refunds above a threshold, cancellations — and log every agent-executed transaction for reconciliation from day one.
Verdict
Pick Sierra when you are a high-volume consumer brand, when voice is a primary channel, when the agent has to execute transactions inside PCI-scope workflows, and when you want a vendor-managed deployment with an enterprise reference list and outcome-aligned billing. It is the voice-first, transaction-executing, white-glove pick.
Pick Decagon when you want your own ops team authoring agent behavior in plain language, when a lower entry floor makes the deflection math pay back sooner, when built-in QA and A/B testing matter, and when your business is internet-native and ticket-deflection-led rather than voice-and-payments-heavy. It is the control-first, lower-floor, ops-authored pick.
If you can’t decide, default to Decagon: the lower platform floor, per-resolution alignment, and ops-authored AOPs let you prove deflection on your real volume before committing to a consumer-brand-scale contract. Flip to Sierra the moment voice becomes a primary channel or the agent needs to take payments and process refunds inside a regulated flow.
Pick neither when your support volume is low enough that a five- or six-figure floor can’t pay back — a lighter, seat-priced deflector on your existing help desk wins (Zendesk or Intercom Fin) — or when you would rather consolidate agents inside the CRM you already run (Salesforce Agentforce) than add a high-touch specialist on top.