The top 7 Sierra AI alternatives in 2027

If you want autonomous AI agents for customer service that a vendor designs and launches with you, you'll likely evaluate Sierra.

Teams that choose Sierra tend to value its white-glove deployment, which gets agents live without heavy internal engineering. Its Agent Studio also lets your team take on more of the build later.

That said, Sierra doesn't support a dedicated quality management or human-agent coaching product. Their pricing is also outcome-based, so long-term costs are hard to model.

Given all of Sierra's pros and cons, we'll help you decide whether it's the right fit by comparing it to its best alternatives.

Note: This guide was updated on 10/16/2026. The information on this page can change in the future.

How we evaluated Sierra AI alternatives

This comparison draws on third-party review sites like G2 and Garter® Peer Insights, vendor documentation, and conversations with prospects and customers who've used and/or evaluated these platforms.

Each Sierra alternative was scored against five criteria:

• Ownership after launch: managed builds launch fast, but if every change waits on a vendor team, iteration slows as volume grows
• Conversation intelligence before automation: knowing which topics drive volume and complexity shows where automation pays off and where it'll fail
• Quality management for AI and human agents: AI agents answer inconsistently, so without shared scoring you can't catch errors or compare AI with humans
• Human handoff and continuity: escalated conversations carry the most risk, so guidance that stops at the handoff leaves agents alone at the hardest moment
• Deployment fit: your cloud, CCaaS, and data residency requirements decide which platforms you can run at all

Here are the top Sierra AI alternatives scored against the criteria above.

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The best Sierra AI alternatives

Here's a closer look at each Sierra AI alternative.

1. Cresta

Cresta is the AI operating system for CX. It offers a unified platform for human and AI agents that analyzes, automates, and augments customer conversations.

Highlights

• One conversation record and models fine-tuned on your own conversations power all of its products, so every insight shapes automation, guidance, and coaching

• Cresta Deputy Agent, launched in October 2026, extends the platform past the conversation. It's a long-running agent that keeps goals and context across systems and teams, so operational work across CRM, case management, and payments systems gets finished, with people approving key steps

• Forrester named Cresta a Leader in The Forrester Wave™: Conversation Intelligence Solutions For Contact Centers, Q2 2025. The report gave Cresta the highest score in the Current Offering category

• Cresta's customer stories span travel, financial services, telecom, and home services, and most include significant ROI metrics. For example, Xanterra Travel Collection, the largest operator of lodges, restaurants, and concessions in U.S. national parks, reached a 74% containment rate with Cresta AI Agent

Why choose Cresta over Sierra

Choose Cresta when AI and human agents need to improve from the same customer conversations.

Unlike Sierra’s primarily AI-agent approach, Cresta connects AI and human agents on one platform, with guidance and quality management continuing across handoffs.

Related: How to compare Cresta to Sierra

2. Decagon

Decagon builds autonomous AI agents for customer support, with agent behavior written in natural language.

Highlights

• Agent Operating Procedures (AOPs) let CX teams describe workflows in plain English, so behavior changes don't always need code
• Agents connect to existing knowledge bases and CX tools, so they can act on requests like refunds and troubleshooting
• Their users (including the G2 reviewer below) consistently praise Decagon's responsive and helpful support team

Why choose Decagon over Sierra

When you want your own CX team writing agent behavior in plain language instead of waiting on a vendor build.

Related: The leading Decagon competitors

3. Google (CCAI and Gemini Enterprise for CX)

Google Contact Center AI (CCAI) provides conversational AI within Google Cloud, including Dialogflow virtual agents, agent assist, and conversation analytics.

Highlights

• In January 2026, Google Cloud launched Gemini Enterprise for Customer Experience, with a visual drag-and-drop canvas for building and launching support workflows
• Virtual agents, agent assist, and conversation analytics are separate components, so teams pick only what they need
• Named a Leader in the 2026 Gartner® Magic Quadrant™ for Conversational AI Platforms

Why choose Google over Sierra

When you're already invested in Google Cloud and have technical teams to assemble components into a custom build.

4. Kore.ai

Kore.ai provides an enterprise AI agent platform with pre-built industry templates for industries like banking, healthcare, and retail.

Highlights

• Offers flexible deployment across cloud, hybrid, and on-premises environments, which helps teams with data residency rules
• Named a Leader in the 2026 Gartner® Magic Quadrant™ for Conversational AI Platforms and in The Forrester Wave™: Conversational AI Platforms For Customer Service, Q2 2026
• Users often cite their integrations as a strength. The G2 review below is a good example

Why choose Kore.ai over Sierra

When you want your own team building agents from industry templates, with deep integrations into enterprise systems.

Related: Explore Kore.ai alternatives

5. NiCE Cognigy

NiCE Cognigy provides a low-code platform for voice and chat AI agents, now sold as part of NiCE.

Highlights

• A low-code visual builder lets non-technical teams design and change flows without much IT help
• Deterministic flows and agentic AI run side by side, so teams choose how much autonomy each use case gets
• Named a Leader in The Forrester Wave™: Conversational AI Platforms For Customer Service, Q2 2026, and a Visionary in the 2026 Gartner® Magic Quadrant™ for Conversational AI Platforms
• Users value the platform's flexibility with AI. For example, here's a snapshot of what a user shared in Gartner® Peer Insights:

Why choose NiCE Cognigy over Sierra

When you run your contact center on NICE CXone and want AI agents built inside the same ecosystem.

Related: Compare the top Cognigy alternatives

6. PolyAI

PolyAI builds voice-first AI agents for enterprise contact centers, designed for natural, non-linear phone conversations.

Highlights

• Agents run on PolyAI's own models that are trained on real customer support interactions, so they're tuned for service calls rather than generic chat
• Agents handle voice, chat, and SMS, though the platform is built around the phone channel
• Included in the 2026 Gartner® Magic Quadrant™ for Conversational AI Platforms
• Many users credit PolyAI with cutting wait times. The snapshot of the review below is just one example

Why choose PolyAI over Sierra

When you handle most of your volume over the phone and want a platform built for voice first.

Related: The best PolyAI alternatives

7. Rasa

Rasa provides an open-source-based framework for building conversational AI agents that teams can host on their own infrastructure.

Highlights

• Full on-premises deployment, so teams with strict data sovereignty rules keep conversations in their own environment
• LLM capabilities layered on deterministic business logic, so critical steps follow defined rules
• Named a Strong Performer in The Forrester Wave™: Conversational AI Platforms For Customer Service, Q2 2026

Why choose Rasa over Sierra

When you need to self-host for regulatory reasons and your developers want to own how agents are built, tested, and released.

Final thoughts

Sierra gets autonomous agents live quickly with white-glove help, runs across chat and voice, and gives teams Agent Studio to take on more of the build over time. That said, check how Live Assist compares with more mature agent assist products from other vendors. And confirm how outcome-based pricing will scale with your volume.

Start evaluating Sierra AI alternatives when human agents still handle your most complex conversations and you need to coach and score them alongside AI. The same applies when your team wants to own agent changes, or when data residency rules rule out a managed platform.