Sierra vs Decagon: Which AI Agent Platform Wins?

Sierra vs Decagon comes down to what your team needs to control most: Sierra leads for multilingual voice agents, while Decagon leads for customer-owned agent logic written as Agent Operating Procedures and version-controlled in Git. For enterprise support and AI team decision-makers choosing an AI customer service platform, that difference shapes how well you can launch voice automation across 34 languages, keep engineering ownership of agent behavior, and meet QA, deployment, and compliance requirements at scale.

OnClarity (www.onclarity.com) is a third option for teams with stricter QA and deployment needs. It fits regulated organizations that want every conversation scored against a rubric plus deployment in their own cloud or fully on-premise. This Sierra vs Decagon comparison covers agent building, channel support, QA methods, deployment models, pricing structures, compliance certifications, and who owns the agent logic across all three platforms.

Sierra: Best for multilingual voice deployments. Voice agents are tested end-to-end across 34 languages, including Arabic. Decagon: Best for engineering-owned agent logic. Agent Operating Procedures read like plain-English SOPs and version-control in Git. OnClarity: Best for regulated teams needing QA plus deployment control. It scores 100% of conversations against your rubric and deploys in your own cloud or fully on-premise.

Sierra vs Decagon: what is the difference?

The Sierra vs Decagon comparison comes down to agent authorship and channel strength for teams choosing between two enterprise-grade conversational AI platforms built to automate customer experience. Sierra’s Agent SDK and Agent Studio let engineering and CX teams define goals and guardrails. The format is declarative, code-based or no-code. Simulation testing gates every release. Sierra also publishes voice agents supporting 34 languages. Each one is tested for accuracy, latency, rhythm and tone, including Arabic (Sierra, multilingual voice agents).

Decagon vs Sierra is a different bet, and the real difference is who can change agent behavior fastest in practice. Decagon’s Agent Operating Procedures (AOPs) are written in natural language by customer teams, with an emphasis on operator control over agent behavior. They are version-controlled in Git. That gives engineering teams direct ownership of the underlying logic (Decagon, AOP product page).

Claims on this page come from each vendor’s own published material, read on the vendor’s own site rather than taken from aggregator write-ups. Any gap prints as “Not published” rather than an estimate. No list price appears on this page unless the vendor itself published one.

Sierra vs Decagon: which should you pick?

The Sierra vs Decagon decision starts with architecture, not a feature checklist. Sierra is built as a standalone agent layer. It runs alongside a company’s existing helpdesk rather than inside it. The ticketing system of record stays where it is, and Sierra sits in front of or beside it. Decagon takes the opposite approach. Agent logic is defined in natural language procedures through Agent Operating Procedures, using natural language instructions that compile into structured workflows and version-control in Git, which lets the CX team manage workflow logic directly. The logic itself becomes an artifact in the customer’s own repo (Decagon, AOP product page). One vendor layers on top of what you have. The other asks engineering to own and ship the agent’s procedures like code.

Fixed field set

Sierra

• Pricing model: Outcome-based, pay per resolution, with blended consumption pricing where outcomes do not fit

• Free plan: No free trial or self-serve option

• Best for: Large enterprises with a dedicated program budget

• Standout feature: Voice agents in 34 languages, tested end-to-end for accuracy, latency, rhythm and tone (Sierra, multilingual voice agents)

• Deployment: Standalone agent layer alongside existing helpdesk, not inside it

• Compliance: SOC 2, HIPAA, GDPR, PCI, FedRAMP High, CCPA, CSA STAR, ISO 27001 and ISO 42001 (Sierra Trust Center)

• Rating: Omitted*

Decagon

• Pricing model: Per-conversation or per-resolution

• Free plan: No self-serve signup

• Best for: Enterprise organizations handling high-volume support across multiple products, regions, or channels (Decagon, best AI customer service software)

• Standout feature: Agent Operating Procedures written in plain English, version-controlled in Git

• Deployment: Standard SaaS, single-tenant, or customer VPC, plus on-premises datacenter options for specialized requirements

• Compliance: SOC 2 Type II, ISO 27001, GDPR, HIPAA, PCI, CCPA

• Rating: Omitted*

OnClarity

• Pricing model: Custom, usage-based, scoped to conversation volume

• Free plan: No

• Best for: Regulated enterprises needing full QA coverage and deployment control in their own cloud or on-premise

• Standout feature: AI Agent QA scores 100% of conversations, AI and human, against the customer’s existing rubric

• Deployment: SaaS, in-region cloud, or fully on-premise; LLM-agnostic

• Compliance: SOC 2 Type II; GDPR and HIPAA ready; Saudi PDPL aligned

• Rating: Omitted*

• Evaluation: Starts with a demo scoped to your conversation volume. Published governance adds NIST and ISO/IEC 42001 alignment, audit logs for every AI and human action, and a 99.9% uptime SLA.

Aggregator ratings are omitted on this page because review samples differ across vendors and are not independently verifiable.

Seven-dimension comparison

Decagon Assist, Decagon’s copilot for human representatives, works inside Salesforce, Zendesk, and Front, with a dedicated analytics hub. OnClarity’s Agent Assist sits beside your people with a drafted answer and the source it came from.

Who should pick which

Pick Sierra if the buyer is a large enterprise with a dedicated program budget. Heavy multilingual voice volume favors Sierra, which publishes voice agents tested end-to-end across 34 languages and iterates on each agent during the initial weeks of deployment. It suits high-volume enterprise brands that want more finished support flows, with Sierra continuing to optimize the agent after launch and Agent Studio covering customer-side changes and ongoing maintenance.

Pick Decagon if engineering capacity is available in-house and the team wants agent logic sitting in its own repo under Git. That fits high-volume support across multiple products, regions, or channels, which is how Decagon positions itself (Decagon, best AI customer service software). Customer teams can directly edit agent behavior, which fits workflows that change more often than a small team would usually need.

Pick OnClarity if the requirement includes QA on every conversation, AI and human, rather than a sample, Voice of Customer analysis on that same data, and deployment control beyond cloud-only hosting. That includes on-premise for security-sensitive environments (OnClarity, AI Agents; OnClarity, Agent QA; OnClarity, Deployment).

Why do US support teams add OnClarity to a Sierra vs Decagon shortlist?

Teams add a third vendor to this evaluation for three reasons. The billing model needs to be scoped to the problem rather than a published list price. The deployment boundary needs to include on-premise rather than cloud-only. The compliance posture needs to be checked line by line rather than taken on faith. Below is the mechanism behind each, not a sales pitch for any one shape.

Pricing model: three billing shapes, three failure modes

A per-conversation meter bills every handled conversation at the same rate, resolved or not. The bill tracks volume, not outcome. It does not shrink as the agent gets better at resolving issues on the first pass. Decagon offers both per-conversation and per-resolution pricing, and says most enterprises choose per-conversation for predictable costs. For enterprises, per-conversation pricing can make costs more predictable than resolution-based billing, depending on volume and how success is defined.

An outcome or per-resolution meter only bills when the agent resolves the issue end-to-end. That sounds cleaner until you ask what counts as a resolution. Some vendors count resolution by customer inactivity after a timeout window. A silent abandonment can get billed as a successful outcome. The same problem can also return later, which raises whether the first interaction should have counted as resolved at all. That makes the resolution definition a line item to negotiate in the contract, not a detail to assume. Sierra’s published pricing model is outcome-based, pay per resolution. Sierra says it agrees clear outcome criteria with each customer upfront, and that escalated cases are in most cases not charged, so the definition is set per contract rather than published.

A usage-based model scoped to conversation volume decouples the bill from seat count entirely. That matters once an agent automates away the human seats a per-seat tool used to charge for. OnClarity’s pricing is usage-based and scales with conversation volume rather than per agent seat. It is custom and scoped to the specific problem, not a published list price. Evaluation starts with a demo, not a signup form.

Neither Sierra nor Decagon publishes a list price on its own pricing page. Every annual figure circulating for either vendor comes from third-party write-ups, not vendor documentation. This page does not reprint any of them. To judge total cost, you also have to account for implementation, integrations, ongoing tuning, governance, and the details of per resolution pricing.

Does Sierra support on-premise deployment?

No. Sierra does not publish an on-premise deployment option. Its published architecture is a standalone agent layer that runs alongside a company’s existing helpdesk, not inside it and not inside the customer’s own infrastructure. Decagon publishes standard SaaS, single-tenant, and customer VPC deployment, where its full stack runs inside the customer’s own cloud environment. It also says it supports on-premises datacenter deployments for more specialized requirements.

OnClarity offers SaaS, in-region cloud, or fully on-premise, and is LLM-agnostic. The inference layer is not locked to a single model vendor. That third option answers the security review that will not accept a third-party cloud as the only control boundary. That is the exact objection buyers raise when a prior vendor could not deploy on-premise.

On-premise is not free. The customer inherits the upgrade cadence, GPU or inference capacity planning, and observability plumbing that a SaaS tenant gets handled by the vendor. Choosing on-premise means owning that operational surface in exchange for keeping data and control inside your own perimeter.

Compliance: attestation report versus certification, read the wording

Sierra lists SOC 2, HIPAA, GDPR, PCI, FedRAMP High, CCPA, CSA STAR, ISO 27001, and ISO 42001 (Sierra Trust Center), plus PCI DSS Level 1 certification for payments. Ask which items are certifications and which are attestation reports or programs, because the wording differs by framework. Decagon publishes SOC 2 Type II, HIPAA, GDPR, PCI, CCPA, and ISO 27001. OnClarity holds SOC 2 Type II certification, is GDPR and HIPAA ready and Saudi PDPL aligned, and publishes NIST and ISO/IEC 42001 alignment.

Two OnClarity specifics, stated plainly: audit logs record every AI and human action and keep it reviewable, and refunds follow approval workflows. OnClarity’s pricing is custom and usage-based on conversation volume, so evaluation starts with a scoped conversation about your actual volume rather than a signup.

Who owns the agent logic once it is live is worth asking every vendor by name, not assuming. For enterprise buyers, that includes whether oversight, live intervention, and implementation support are built in before deployment or assembled after go-live. With OnClarity, configuration of agent behavior, knowledge grounding, and approval workflows for sensitive actions sits with the customer’s own team. OnClarity supports that work rather than gating it. Sierra, by contrast, emphasizes governance and version control in its deployment approach. For a fuller framing of this decision, see OnClarity’s build vs buy post.

How does each vendor measure agent quality and agent operating procedures after launch?

This guide does not reproduce G2, Capterra, or TrustRadius star ratings in this section, and says so directly. Review samples are not independently verifiable by this publisher. What follows instead is the mechanism each vendor publishes for measuring agent quality after go-live, because that mechanism is what a QA lead or compliance reviewer actually needs to evaluate.

The sampling problem, mechanically

Most support organizations manually QA only a small sample of conversations. At that coverage rate, a single reviewer sampling 25 calls out of a 1,000-call week for one intent, on one channel, has almost no statistical power to detect a regression confined to that intent and that week. A new prompt change can break refund-policy answers on WhatsApp but not on chat. It can run for weeks before a human reviewer happens to pull a sample that contains it. The sample isn’t biased. It’s just too small to carry the signal. A rubric-based automated scorer changes the denominator, not the rubric. It applies the same scoring criteria a human QA team already uses, but to every conversation rather than a sliver of them. That is the only way to catch a regression in the week it happens rather than the quarter someone notices the complaint volume.

Scoring AI-handled and human-handled conversations on the identical rubric matters for a second reason. It’s the only way to compare the two populations honestly. If AI conversations are scored against one standard and human conversations against another, a QA lead cannot answer the basic question of whether automation is raising or lowering service quality. An audit trail usable by a compliance team needs, at minimum, the full conversation transcript, the rubric criteria applied, the score per criterion, and a timestamp tying the score to the agent version that produced the response. A reviewer can then reconstruct what happened and why it was scored that way months later.

Published positions

Sierra publishes that its voice agents are tested end-to-end for accuracy, latency, rhythm, and tone across 34 languages (Sierra, multilingual voice agents). That is a pre-release and release-gating claim about voice agent behavior. After launch, Sierra Insights adds always-on monitoring that flags conversations needing attention, plus CSAT and resolution reporting. Decagon publishes Agent Operating Procedures as version-controlled, plain-English logic, which makes a behavior change reviewable as a diff before it ships. Decagon also says Watchtower, its always-on monitoring and QA, scores 100% of conversations in real time against your team’s criteria. Ask each vendor whether that coverage extends to human-handled conversations, resolution rates tracking, and regression testing. That is a question to raise in diligence, not a judgment about either platform.

OnClarity’s AI Agent QA scores 100% of conversations, AI-handled and human-handled, against the customer’s existing evaluation rubric, across voice, chat, email, and WhatsApp. OnClarity’s Voice of Customer runs on that same conversation data. A product team reading root-cause themes and a QA team reading agent scores read one corpus, not two separate pipelines. AI safety guardrails and approval workflows apply to sensitive actions before they execute, which is a different control than scoring a conversation after the fact.

Questions to ask both vendors

1. Who authors and owns agent logic after go-live? A weak answer names only the vendor’s implementation team with no customer-side editing path.

2. How is a behavior change reviewed and rolled back? A weak answer has no version history or no stated rollback mechanism.

3. What percentage of conversations are scored post-launch, and against whose rubric? A weak answer cites pre-release simulation testing as if it answers a live-traffic QA question.

4. Are AI and human conversations scored on the same rubric? A weak answer scores only the AI side.

5. Where is conversation data stored and processed, and which regions? A weak answer says “cloud” with no named region.

6. What is the billing unit, and how exactly is “resolution” defined? A weak answer can’t say whether a timeout counts as a resolution.

7. What is the realistic contract-to-production timeline, and does it match the ranges each vendor publishes? If the vendor emphasizes managed deployment, ask how that affects timeline, what post-launch changes your team can make without additional engineering resources, and which parts of its deployment models still require vendor support. A weak answer has no number or buries it in a sales call.

8. Which channels are natively built versus integrated through a partner? A weak answer can’t distinguish the two.

Which AI agent or customer support platform should you shortlist?

This Sierra vs Decagon decision follows the table, not a preference. If the deciding constraint is multilingual voice at enterprise scale, Sierra is the stronger starting point for brand-aligned conversations and complex workflows, especially across voice as part of a broader AI customer experience push. Voice agents tested end-to-end across 34 languages (Sierra, multilingual voice agents) carry the most weight when voice coverage is the binding requirement. If the deciding constraint is engineering ownership of agent logic under version control, Decagon is the answer, and its AOPs let CX teams manage workflows directly. Agent Operating Procedures read like plain-English SOPs and version-control in Git, giving engineering a reviewable diff for every behavior change. If the deciding constraints are QA coverage across every conversation, AI and human, a Voice of Customer loop running on that same data, and deployment inside the buyer’s own cloud or on-premise, OnClarity publishes all three. That combination is what enterprise AI support automation and on-premise AI agent deployment buyers most often ask for together.

Restated plainly: OnClarity prices on conversation volume, not seats, and publishes ISO/IEC 42001 and NIST alignment alongside SOC 2 Type II certification. Those are published positions, not negotiating positions. They belong in the same evaluation as everything else on this page.

The next step for any of the three is a scoped conversation, not a signup form. Book a demo to see AI Agent QA and Voice of Customer running against your own rubric and your own deployment constraints. Full OnClarity deployment typically takes six to ten weeks, and scored conversations start appearing within minutes of call end once your rubric is loaded.

Sierra vs Decagon: frequently asked questions

Is Sierra or Decagon cheaper?

Neither publishes a list price in this Sierra vs Decagon comparison. Sierra bills on outcomes, pay per resolution. Decagon bills per conversation or per resolution, and says most enterprises choose per conversation. Any specific annual figure circulating online for either vendor is a third-party estimate, not a number either company has published itself. The honest answer on total cost depends on your volume, how each contract defines a resolution, and what implementation, integrations, and ongoing tuning add on top.

Does Decagon support on-premise deployment?

Yes, for specialized requirements. Decagon publishes standard SaaS, single-tenant, and customer VPC deployment, and says it also supports on-premises datacenter deployments. A buyer who requires on-premise AI agent deployment as a hard constraint should ask Decagon directly in writing which features, models, and channels run inside that environment. OnClarity lists fully on-premise alongside SaaS and in-region cloud, with PII removed at ingestion.

Which handles voice better, Sierra or Decagon?

Sierra publishes voice agents tested end-to-end across 34 languages for accuracy, latency, rhythm, and tone, including Arabic (Sierra, multilingual voice agents). Decagon publishes 70+ languages across chat, voice, email, SMS, and WhatsApp, with outbound voice and Simulations that test agent behavior across channels. Language support is still something to verify by workflow and channel rather than by a single headline figure, and neither claim is a head-to-head benchmark. OnClarity’s AI agents work across chat and voice, and AI Agent QA scores every voice conversation against your own rubric.

Who owns the agent logic after launch?

Decagon publishes Agent Operating Procedures version-controlled in Git, putting logic in the customer’s own repository. That gives customer teams self-serve configuration over how the agent behaves, with workflow instructions they can directly edit and Git-based versioning tracking each update. Sierra publishes Agent Studio for no-code changes and an Agent SDK that lets developers build agents as code within their existing software development lifecycle, with simulations gating each release. OnClarity agents are configured by the customer’s own team, with approval workflows gating sensitive actions before they execute.

What is the best Sierra or Decagon alternative for regulated industries?

For a Sierra AI alternative or Decagon AI alternative in a regulated setting, OnClarity is built on SOC 2 Type II certification, GDPR and HIPAA readiness, and Saudi PDPL alignment. Deployment options are SaaS, in-region cloud, or fully on-premise. That combination of compliance posture and deployment control is the reason regulated buyers add it to a Decagon vs Sierra shortlist.