Decagon and Sierra are both credible enterprise AI customer service platforms, and for a regulated buyer neither one is the default answer. Decagon fits teams that want a deeply configurable agent built alongside vendor engineers. Sierra fits teams that want spend tied strictly to fully resolved cases. The decision splits by situation rather than by scoreboard.
Decagon and Sierra are two of the most frequently shortlisted enterprise AI customer service platforms in 2026. Both resolve tickets autonomously across chat, email and voice, both sell to large companies including financial services brands, and both negotiate custom contracts rather than publishing rates. For a regulated buyer in fintech, banking, insurance, healthtech or gaming, four questions decide the purchase: what the platform costs against what you spend today, how tightly it controls hallucination on high-stakes answers, what compliance evidence it can hand your risk team, and where the data physically sits. This page works through those four, then splits the verdict by buyer situation.
Decagon publicly describes customer-selectable per-conversation and per-resolution billing, alongside white-glove deployment with embedded engineering at launch. It does not publish rates as of August 2026.
Sierra publicly describes outcome-based billing, where the customer pays on a fully resolved case and escalations are free. It reached $100M ARR in 21 months according to TechCrunch. It does not publish rates as of August 2026.
Neither vendor publishes a price list, a validated accuracy benchmark or a regional hosting map as of August 2026. Treat every "does not publish" as an unanswered question for the demo rather than evidence of a defect.
Decagon is the better pick when configurability and vendor-supplied engineering capacity are the binding constraints. Sierra is the better pick when finance is the gating stakeholder and your ticket base skews toward cases that fully resolve.
Data residency stalls more of these evaluations than any other constraint for Australian and European buyers. Ask both vendors for a written, region-named commitment before the security review starts.
Last updated: August 2026
Most head-to-head pages in this category try to declare a winner. That framing fails here. Decagon and Sierra were built for overlapping buyers and diverge mainly on billing philosophy, deployment style and how much of the configuration surface your own team holds after launch. The useful output is a routing rule plus the evidence you have to extract before either clears a compliance review.
A disclosure, because it should change how you read this page. It is published by Lorikeet, which sells an AI support platform for complex and regulated businesses and competes with both vendors. Lorikeet is kept out of the comparison itself and appears in one clearly marked section near the end. Every claim about Decagon and Sierra is drawn from public material and hedged accordingly, and where a vendor does not publish something this page says so rather than treating silence as a failing.
Decagon and Sierra at a Glance
Decagon is a high-end enterprise AI agent platform with named customers across consumer and financial services. Its public positioning centres on configurability paired with white-glove implementation: the platform is shaped to a specific business rather than deployed out of the box, with embedded engineering during launch. It runs chat, email and voice, supports multi-step action-taking across integrated systems, and publicly describes a choice between per-conversation and per-resolution billing options.
Sierra is the enterprise AI agent company founded by Bret Taylor and Clay Bavor. It scaled quickly, reaching $100M ARR in 21 months according to TechCrunch. Its hallmark is outcome-based billing: the customer pays when the AI fully resolves a case, and escalations are not charged. Sierra publicly describes a branded agent approach where the agent carries the customer's own persona and tone, deploys with embedded staff at launch, and publicly documents simulation-based testing of agent behaviour before release.
The honest summary: on raw capability they are close. Both resolve multi-step tickets, take actions in connected systems, cover the three core channels and use a high-touch launch model. The differences that decide a purchase are billing structure, who holds the configuration surface after the launch team leaves, and how much regulated-specific evidence each produces on request. For the wider field, see our roundup of the best enterprise AI support platforms in 2026.
What a Regulated Buyer Should Test, in Order
Generic CX buying guides start with deflection rate, response time and CSAT. In a regulated business those are downstream of correctness, provability and cost control. The seven criteria below are ordered by how often each ends an evaluation, and they are the questions to take into both demos. Ask for demonstrations rather than descriptions.
1. Cost against current spend. Model both against twelve months of your own tickets, split into full resolutions, partial resolutions and escalations, and ask each to price all three buckets.
2. Accuracy and hallucination control. Bring fifty hard historical tickets with known outcomes, have each vendor run them in a sandbox, and score against your own QA rubric.
3. Security and compliance evidence. Request current attestation letters under NDA and read the scope section, including which trust services criteria are covered.
4. Data residency. Ask for a written commitment naming the region, and ask about storage and inference separately, because they frequently sit in different places.
5. Configuration ownership after launch. Ask whether a compliance analyst who is not an engineer can change a disclosure or a threshold and re-test it without a vendor ticket.
6. Auditability. Ask to replay a real decision the agent made ninety days ago, with every tool call, prompt and reasoning step in order and timestamped, and ask what a compliance team would export for an examiner.
7. Failure behaviour. Ask what the agent does when a core system returns a server error mid-chain: retry, escalate or roll back. Ask to see the behaviour rather than the policy.
Cost Against Current Spend
In enterprise AI support evaluations the objection that ends deals earliest is rarely capability. It is the delta against the support cost line the business already carries. Both vendors negotiate case by case, so the comparison runs on structure rather than list price.
How Decagon fits: Decagon publicly describes a choice between per-conversation and per-resolution billing, selected by the customer. Per-conversation billing prices engagement rather than outcome, which suits a ticket base with many long, multi-turn contacts that legitimately end in a human handoff. The trade-off is symmetrical, because you also pay on contacts that never close. Decagon does not publish rates as of August 2026.
How Sierra fits: Sierra publicly describes outcome-based billing, where a fully resolved case is the billable unit and an escalation costs nothing. For a CFO this is the cleanest story in the category, because spend rises only with delivered value. The consequence worth modelling is that any outcome-only model creates a commercial gradient toward cases that resolve cleanly, and in a regulated business the hard cases carry the risk. That is a property of the model rather than a criticism of the product. Sierra also does not publish rates as of August 2026.
What to ask either vendor: price full resolutions, partial resolutions and escalations separately, then ask what happens to the effective rate if your resolution rate lands ten points below the modelled figure. For a fintech ticket base, our guide to Decagon alternatives for fintech works through the same modelling across a wider vendor set.
Accuracy and Hallucination Control
In a regulated business a wrong answer can be a missed disclosure, a consumer-protection complaint, an incorrect claims determination or a conduct finding. A platform that resolves ninety percent of tickets and cannot show how it behaved on the other ten is a liability to a risk team.
How Decagon fits: Decagon publicly documents grounding in the customer's own knowledge sources and multi-step action-taking across connected systems, and operates production deployments at large interaction volumes. It does not publish a validated accuracy or hallucination benchmark as of August 2026, which is the category norm rather than an outlier.
How Sierra fits: Sierra publicly documents simulation-based testing of agent behaviour before release, which is genuinely useful to a regulated buyer, because it is the mechanism by which bad paths get exercised before a customer meets them. Sierra also does not publish a validated accuracy or hallucination benchmark as of August 2026.
What to ask either vendor: ask for a published, validated number and treat the absence of one as an unanswered question rather than an admission. Then run the test yourself: fifty of your own hardest historical tickets, scored blind by your own QA team. Ask what the agent does when a connected system returns a server error mid-chain, whether it retries, escalates or rolls back. That answer is usually more predictive than any accuracy percentage. For how resolution is defined and measured across the category, see our analysis of AI platforms that deliver end-to-end resolution in regulated industries.
Security and Compliance
Security and compliance rarely determine which vendor a team wants. They determine whether the deal completes. A regulated procurement file needs attestations with the right scope, a subprocessor list, model-provider terms that exclude training on your data, role-based access control and a defensible PII handling position. Getting that evidence late slips the go-live date.
How Decagon fits: Decagon publicly documents SOC 2 Type II. It does not publicly document ISO 27001 certification or the availability of a HIPAA business associate agreement as of August 2026. For a healthtech buyer that is a question for the first call rather than the security review, because a BAA requirement discovered late ends evaluations that have already consumed a quarter. Teams working through that constraint may find our list of Decagon alternatives for healthtech useful as a parallel shortlist.
How Sierra fits: Sierra publicly documents SOC 2. It does not foreground a HIPAA posture or an ISO 27001 certification in its public material as of August 2026. As with Decagon, the honest instruction is to ask rather than to infer, because compliance posture moves faster than public web copy.
What to ask either vendor: request the current attestation letter under NDA and read the scope section rather than the cover page. Ask for the subprocessor list and the model providers behind the agent, and get the no-train position in writing. Ask how PII is redacted before it reaches a model, and who inside the vendor can read a production transcript. Scope and dates drift, so verify with the vendor rather than any comparison page, including this one.
Data Residency
Data residency is the most repeated hard constraint among Australian and European regulated buyers, and the one that most often turns a promising evaluation into a dead end at the last stage. It is also the dimension this category publishes least about.
How Decagon fits: Decagon does not publish a regional hosting map or a public list of supported data residency regions as of August 2026. Buyers outside the United States should raise residency on the first technical call.
How Sierra fits: Sierra does not publish a regional hosting map or a public list of supported data residency regions as of August 2026. The same instruction applies.
What to ask either vendor: ask four separate questions. Where is data stored at rest. Where does model inference physically run, which is frequently a different answer. Where do transcripts, logs and embeddings live. What happens to all of the above during a regional failover. Then get the answer into the contract, because a residency commitment that lives only in a sales thread will not survive an audit.
Who Owns the Workflows After Launch
Both vendors deploy with embedded staff, and for an enterprise without a large internal AI team that hand-holding is genuinely valuable. The question that matters twelve months later is who changes the agent once those people have moved on.
How each fits: Decagon's white-glove model means its engineers typically do much of the initial configuration, including guardrails and workflow logic, which is the point of the offer for a team with limited internal capacity. Sierra similarly deploys with embedded staff and a persona-led configuration approach, and publicly describes a developer-oriented agent building surface. The same question applies to both: after launch, can a non-engineer on your side make a policy change and prove it holds.
What to ask either vendor: ask for a live demonstration in which someone from your team, not the vendor, edits a guardrail and re-runs the test, and ask for the median turnaround on a vendor-side change request. Post-launch self-sufficiency compounds into a larger number than launch-period speed.
Audit Trails and What You Can Reconstruct Later
An audit trail in regulated support is a timestamped, replayable record of every tool call, prompt and reasoning step the agent took on a given ticket. A conversation transcript records what was said; an audit trail records what was decided and why.
How each fits: Decagon operates enterprise deployments at scale and maintains logging appropriate to that market. Sierra's enterprise posture includes the logging large customers expect, and its public emphasis on pre-release simulation sits adjacent to the same need. Neither publishes reasoning-step audit depth as of August 2026, so that has to be established in the demo.
What to ask either vendor: pick a real ticket from ninety days ago and ask to replay it end to end in the product, with every tool call and the reasoning between them visible in order. Ask what a compliance team would export for an examiner, then ask to see that export, and ask whether retention is configurable to your regulator's requirement. Our guide to AI support transparency and guardrails sets out the standard in more detail and applies to either vendor.
Decagon vs Sierra: The Comparison Table
The table covers the dimensions that move a regulated shortlist: how each vendor bills, how it deploys, what it publishes, and what you have to extract yourself. Treat "does not publish" as a prompt for a question rather than a negative finding, and verify every row directly, since public positioning changes quickly.
Decagon | Sierra | What to verify in the demo | |
|---|---|---|---|
Billing model | Per-conversation or per-resolution, customer-selectable | Outcome-based; escalations not charged | Price resolved, partial and escalated separately |
Published rates | Does not publish | Does not publish | Quote against 12 months of your own volume |
Channels | Chat, email, voice | Chat, email, voice | Whether voice shares the chat workflow engine |
Deployment model | White-glove, embedded engineering at launch | High-touch, embedded staff, branded persona | Time to first production ticket, in writing |
Post-launch ownership | Vendor-led; confirm self-service depth | Vendor-led; confirm self-service depth | Your compliance analyst edits a guardrail live |
Pre-launch testing | Does not publish a testing framework | Publicly documents simulation before release | Run adversarial cases; read a pass or fail report |
Published certifications | SOC 2 Type II | SOC 2 | Attestation scope and criteria, under NDA |
HIPAA and BAA posture | Does not publish | Does not publish | Ask on call one if you touch health data |
Data residency regions | Does not publish | Does not publish | Storage, inference and failover, in contract |
Best fit | Configurability plus vendor engineering capacity | Spend tied to outcomes; resolvable ticket mix | Match to your own gating stakeholder |
When Decagon Is the Better Pick
Your ticket base contains many long contacts that legitimately end in a handoff. Per-conversation billing prices that reality honestly, because the unit reflects work done rather than only the outcome reached.
You have little internal AI engineering capacity and need the vendor to supply it. Embedded engineering at launch is the core of Decagon's offer, converting a build project into a delivered configuration.
Configurability is the binding requirement. If your resolution paths span several backend systems with real branching logic and jurisdiction-specific behaviour, a platform built to be shaped rather than switched on is the right kind of vendor.
You want to choose the billing structure rather than accept one. Selecting between per-conversation and per-resolution matters when your ticket mix is unusual enough that one model prices badly.
Honest limitation: a deployment model built around embedded vendor engineers concentrates the deepest knowledge of your configuration outside your own team during the period when it is being created. That is a trade rather than a flaw, managed with a written handover plan and a demonstrated self-service path before signature. Decagon also does not publish rates, so the buyer set is defined by whatever the quote returns.
When Sierra Is the Better Pick
Finance is the gating stakeholder. Outcome-based billing is the cleanest procurement narrative in this category. When the person approving the line item wants spend to rise only with delivered resolutions, Sierra's model answers that directly.
Your ticket base skews toward cases that fully resolve. If most volume is genuinely automatable and escalations are the exception, an outcome-only unit is the cheapest structure available.
Brand voice and tone governance are first-order requirements. Sierra's branded agent approach is built around the agent carrying your persona consistently across channels, which matters more for a large consumer brand than feature comparisons acknowledge.
You want documented pre-release testing as part of the platform. Sierra publicly documents simulation-based testing before release. A vendor that treats pre-launch behavioural testing as a product surface starts from a better position than one treating it as a services engagement.
Honest limitation: outcome-only billing is a structural incentive as well as a pricing model, and it points the vendor's commercial interest toward cases that close cleanly. In a business where the hardest tickets are also the regulated ones, that gradient is worth pricing into the contract. Sierra also does not publish rates, which makes early budget planning harder than with a published-price vendor.
When Neither Is a Clean Fit
Three situations exist where the honest answer is that this comparison does not resolve. The first is a named data region as a contractual precondition. Neither vendor publishes a residency map, so if a written commitment to an Australian, European or United Kingdom region is a gate rather than a preference, that has to be settled before either platform can be shortlisted at all.
The second is a compliance lead with veto power who needs behaviour proven before go-live rather than reviewed after. Sierra publishes more about pre-release simulation than Decagon does, and neither publishes reasoning-step audit depth, so a buyer for whom that is the deciding criterion is running an evidence-gathering exercise rather than a feature comparison.
The third is volume or budget below the enterprise range both vendors serve, where a helpdesk-native AI layer is usually the better use of the money. Our roundup of Sierra alternatives for 2026 covers that range.
Where a Specialist Third Option Fits
This section exists because the page is published by Lorikeet, and leaving it out would be less honest than marking it clearly. Decagon and Sierra are general-purpose enterprise platforms with genuine strengths, and a regulated team can succeed with either by pushing hard on the criteria above. A narrower specialist earns a shortlist slot only when a compliance lead with veto power needs behaviour proven before go-live, and a replayable tool-call-level audit trail is a gate rather than a preference.
Lorikeet is an AI customer support platform built for complex and regulated businesses across fintech, financial services, healthcare, insurance and gaming. It holds SOC 2 Type II, ISO 27001, HIPAA and GDPR, is hosted on Google Cloud, and runs voice in the United States, United Kingdom and Australia at roughly 1.3 seconds p50 latency alongside chat and email. Its two relevant design commitments are pre-launch guardrail testing and simulation, so behaviour can be exercised before a customer meets it, and replayable audit trails at the tool-call level. It runs on top of an existing helpdesk such as Zendesk, Salesforce or Intercom as a resolution layer rather than replacing the system of record. Published pricing is $2,100 per month billed annually on Start and $5,100 per month billed annually on Scale, with a custom Signature tier. There are no per-seat charges and only successfully resolved tickets are charged.
Honest limitation: Lorikeet is deliberately narrow. It does not hold PCI-DSS, it is not a ticketing system and will not replace one, there is no on-premises option, and it is built for support operations at meaningful volume rather than small teams. If your tickets are mostly simple retrieval and you face no examiner, the guardrail and simulation depth is overhead. A three-way version of this comparison is set out in Sierra vs Decagon vs Lorikeet for regulated industries.
Key Takeaways
Decagon and Sierra are close on raw capability, diverging on billing structure, deployment style and how much of the configuration surface your team holds after launch.
Decagon is the better pick when configurability and vendor-supplied engineering capacity are the binding constraints, and when a meaningful share of volume ends in a legitimate handoff.
Sierra is the better pick when finance is the gating stakeholder, your ticket base skews toward cases that fully resolve, and brand voice governance matters.
Neither publishes rates, a validated accuracy benchmark, a HIPAA posture or a data residency map as of August 2026. Those four gaps are the agenda for both demos.
Data residency and reasoning-step auditability end more regulated evaluations late than anything else, so raise both on the first technical call.
Close this decision on evidence rather than feature lists. Shortlist both, take the seven criteria above into both demos, bring fifty of your own hardest tickets rather than the ones that look good on stage, and insist on demonstrations rather than descriptions. For the wider field before you narrow the list, our analysis of end-to-end resolution in regulated industries covers the category around both vendors.
