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Sierra vs Salesforce Einstein / Agentforce
A plain-English comparison to help you choose between them.
Two enterprise answers to the same question: can an AI agent resolve customer conversations end to end? Agentforce says yes inside the Salesforce estate, grounded in the CRM's data, permissions and trust layer. Sierra, founded by Bret Taylor and Clay Bavor, says yes as a dedicated platform: deployed across chat, voice, SMS, WhatsApp, email and a ChatGPT channel, and priced by outcome, so you pay for resolutions delivered rather than seats. Salesforce shops evaluate Agentforce first; everyone else should see Sierra.
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Side by side
- Summary
Sierra is an enterprise AI agent platform for customer service, founded by Bret Taylor and Clay Bavor. One agent resolves conversations end to end rather than deflecting them, deployed across chat, voice, SMS, WhatsApp, email and a ChatGPT channel, with governance and guardrails built in. Ghostwriter turns procedures and transcripts into production-ready agents, Agent Studio manages them, Insights watches conversation quality, and Experiments runs multivariate tests of agent behaviour.
The commercial shape is strictly enterprise: pricing is outcome-based, so you pay for the resolutions the platform delivers rather than for seats, and there is no public rate card. It raised a major funding round in 2026, and it suits large support operations ready to hand real conversation volume to AI agents under supervision.
- Best for
- Resolving customer conversations end to end across chat and voice
- One governed agent deployed across chat, SMS, WhatsApp, email, voice and ChatGPT
- Building production agents from plain-English descriptions with Ghostwriter
- Enterprise support operations with serious conversation volume
- Paying for resolutions delivered rather than seats
- Cost
- Enterprise
- Ease
- Openness
- Hosted service
- Data
- Customer conversations and connected customer records flow through a closed platform. The Agent Data Platform holds agent memory and integrations into your customer systems, so procurement should treat it as a core data processor. Deployments reportedly handle regulated interactions such as insurance claims and financial services conversations, where AI agent outputs need human oversight and audit trails, automated customer decisions may trigger consumer-protection and disclosure obligations, and the platform does not substitute for compliance review of agent behaviour.
- Summary
Salesforce's AI, now fronted by the Agentforce brand, is embedded intelligence and autonomous agents across the CRM estate: drafting, summarising and predicting inside Sales and Service Cloud, and agents that resolve service cases and work sales tasks against the customer data the platform already governs.
The architecture is the argument: agents grounded in your Salesforce data, permissions and workflows, with a trust layer between models and customer records. The practical dependency runs deep, with unified customer data effectively required for the strongest results.
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It is bought as part of a Salesforce strategy, not as a tool: value and cost both scale with the estate.
- Best for
- Autonomous service agents resolving cases on governed data
- Sales assistance grounded in the CRM record
- Embedded drafting, summaries and predictions in the flow of work
- Enterprises standardised on the Salesforce estate
- Agent deployments under platform trust controls
- Cost
- Enterprise
- Ease
- Openness
- Hosted service
- Data
- AI runs on your Salesforce data under its enterprise trust layer; Data Cloud is effectively required for the full picture.
Pricing
- Sierra
- No published price
- Salesforce Einstein / Agentforce
Free·$500 once$5/user·$125/user·$150/user·from $550/user
Prices as of August 2026.
- Salesforce Foundations
- Free
- Flex Credits
- $500one-off
- Agentforce User License
- $5per user, per month
- Agentforce add-ons
- $125per user, per month
- Agentforce Industries add-ons
- $150per user, per month
- Agentforce 1 Editions
- from $550per user, per month
By area
Where each one pulls ahead, area by area.
| Area | Sierra | Salesforce Einstein / Agentforce |
|---|---|---|
| By job | ||
| Customer support | Sierra's deployments reportedly handle regulated interactions such as insurance claims, so human oversight and audit trails are part of the design rather than an afterthought | Agentforce inherits your knowledge and data quality: thin articles and messy records become confident wrong answers at customer speed, so escalation design and human review come before automation |
| Operations | Sierra is the specialist whose entire platform exists to run resolution-owning customer agents, with building, guardrails, testing and analytics shaped around that one job | Agentforce automates around Salesforce data and flows rather than the business's every system, and data quality stays the ceiling on everything the agent does |
| By task | ||
| Customer support & chatbots | Sierra is sales-led with no public rate card, so a mid-market team wanting AI switched on inside an existing helpdesk, or a deployment that skips procurement, should look elsewhere | Agentforce is the wrong shape for anyone not already running Salesforce: buying the platform to get the agent inverts the decision entirely |
Common questions
What does outcome-based pricing actually mean here?
Sierra charges for the resolutions its agents deliver, not for seats or licences: the economics track results rather than deployment size, though there is no public rate card and the purchase is strictly enterprise. Salesforce's model is the opposite shape: AI bought as part of a Salesforce strategy, with value and cost scaling with the estate. Model the expected resolution volume honestly; it is the number that decides which shape is cheaper for you.
Which handles more channels?
Sierra's channel breadth is a headline feature: one agent deployed across chat, voice, SMS, WhatsApp, email and a ChatGPT channel, with governance and guardrails built in. Agentforce's strength runs the other direction: depth of grounding in the customer data, permissions and workflows Salesforce already governs. If voice and messaging channels carry your support volume, weigh Sierra's breadth directly; if the answer's quality depends on CRM context, weigh Agentforce's grounding.
How do we keep quality honest on either?
Sierra builds the discipline in as product: Ghostwriter turns procedures and transcripts into production-ready agents, Insights watches conversation quality, and Experiments runs multivariate tests of agent behaviour. An operating loop, not just an agent. On the Salesforce side, quality management runs through the platform's own governance machinery. Either way, hand over real volume only under supervision, and treat the agent like a new hire on probation.
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Tool facts last checked August 2026