How much does Sierra AI cost? We don’t know, since the pricing is not publicly available on their website, and they don’t even have a pricing page. From what we hear, It’s completely custom and depends on how many AI Agents you need and what you need them for. A number of sources online show that pricing starts at around $150,000 per year, which is one of the most common reasons why users look at Sierra AI competitors.
If you’re considering Sierra AI to improve your customer experiences with agentic AI, pricing is a major concern. Today, we’ll show you everything we could find on the Sierra AI pricing model and how it works.
Looking for a Sierra AI alternative with clearer pricing and no hidden costs? Book a free demo with Quiq today.
PS. We also have a full Sierra AI review that goes into more detail on the ease of use, AI agent capabilities, agent hand-off and more.
Sierra AI has outcome-based pricing
Outcome-based pricing means that Sierra only charges you for outcomes, such as “a resolved support conversation, a saved cancellation, an upsell, a cross-sell, or any number of valuable outcomes”.
This is opposed to usage-based pricing models, where you are charged a smaller amount for every conversation.
This outcome-based pricing model means that you pay for measurable business outcomes, rather than paying per seat or usage, unlike consumption-based pricing.
This may seem like a good thing, but it is very complicated to predict upfront.
What is a good outcome?
If the customer doesn’t escalate from the AI Agent to a human agent, is that a good outcome? Or maybe they had a bad experience and are done with your brand? Determining ‘good outcomes’ is likely very complicated and can create hard conversations with your vendor.
And, more importantly, it is very difficult to predict what your pricing will be, even after you’ve bought the product. You know how many conversations you’ve had from your current tools, but how many outcomes have you had?
While it may be annoying that you don’t get the exact Sierra AI pricing from us (or Sierra themselves), there’s a good reason for this.
Why Sierra AI Agent pricing is not publicly available
Sierra is going after an enterprise market, where pricing is typically not disclosed publicly, and it’s for a very good reason.
To calculate pricing, Sierra AI needs many data points:
- How many channels, markets, languages and use cases you need
- How complex the tasks that agents handle are
- How much customization is needed for those AI Agents to achieve tangible business impacts for your business
- How many human agents you have, and how AI Agents fit into your workflow
- Contact volume and how many (potential) customers you talk to on a monthly/annual basis
This outcome-based pricing varies based on these factors, but there are also implementation fees for each account, starting at a reported sum of $50,000.
So, you really have to get in touch with Sierra to find out your fixed and ongoing costs.
How Sierra AI compares against major competitors
If you’re looking to improve your customer experience with AI Agents, Sierra is far from the only choice out there. Below, we list the (un)known costs of some of their major competitors with their pricing models.
| Pricing model | Reported starting cost | Setup fees | Predictability | Notes | |
| Quiq | Usage-based tiers by conversations, channels, and automation level. Addons available (AI agents/assistants, channels, translations, etc.) | Mid five figures per year is common for enterprise programs | Yes, usually lower than peers | High | Best balance of enterprise features and forecastable spend. Faster deployment reduces total contract cost. |
| Kore.ai | Session-based billing plus an enterprise license | Reported enterprise deals often start around $300,000 per year | Yes, often significant | Low to medium | Billing sessions and platform licensing add complexity. Costs can spike with long or repeated conversations. |
| Decagon | Capacity or per-conversation pricing | Roughly $95,000 to $590,000 per year based on usage | Yes | Medium | Less opaque than outcome pricing, but still a large enterprise commitment. Easier to budget than Sierra-style models. |
| Poly.ai | Per-minute voice usage | Around $0.90 to $1.00 per call minute | Yes | Medium | Voice only focus. Costs scale quickly with call volume, forecasting depends heavily on traffic stability. |
| Cognigy | Custom enterprise contracts. A mix of a number of messages and time passed | Commonly six-figure annual deals | Yes | Low | Powerful platform, but pricing details stay opaque until late in sales discussions. |
| Replicant | Usage-based voice automation | Six-figure annual contracts are typical | Yes | Medium to low | Outcome and usage-based billing tied to voice traffic. Can become costly at scale. |
Quiq
Quiq does not publicly list exact prices, but pricing is structured around usage tiers that scale with conversation volume, channels supported, and depth of AI automation.
Compared with the others below, Quiq typically lands at lower total enterprise costs while offering transparent, scalable pricing that teams can forecast and justify to finance leaders. With a robust platform, its AI agents can support multi-channel coverage (SMS, WhatsApp, web chat, social, voice, and email) without requiring extensive engineering to launch.
This reduces project costs and shortens time to value as reported by their enterprise customer,s such as Spirit Airlines, Roku, Panasonic, and Urban Outfitters, who’ve all launched AI Agents to handle complex scenarios and are seeing efficiency gains such as a 40% automatic resolution rate.
“We’ve able to launch a cohesive self-service experience that spans voice, chat, and all of our messaging touchpoints. The results have been remarkable. Over 40% of our requests resolve automatically without needing a live agent and we’ve seen a 16% reduction in our conversation time.”
This mix of predictable usage-based billing and faster deployment makes Quiq a strong option for teams that want enterprise-grade AI without opaque outcome pricing or massive upfront commitments.
The price is barely the tip of the iceberg. Quiq stands out with an excellent return on investment that goes beyond the value in money. You can’t put a price on improved customer experience and increased customer service KPIs across the board.
Kore.ai
Kore.ai does not publish standard pricing and primarily sells custom enterprise deals. Based on third-party research, lower-tier reported plans for small teams have appeared in the $50 to $180 per month range on annual billing, but these are unofficial and inconsistent.
For true enterprise deployments, most deals start around $300,000 per year and require a heavy implementation effort before realizing value. Billing is complicated because it uses “billing sessions” based on 15-minute blocks of conversation, which can make costs unpredictable month to month.
Decagon
Decagon’s pricing is also custom and enterprise-focused. It typically bills based on usage, either on a per-conversation basis (a fixed fee for each conversation) or on a per-resolution model (a higher fee for fully resolved interactions).
Public estimates suggest that annual contracts with Decagon can land anywhere from roughly $95,000 up to $590,900 or more, depending on volume, complexity, and integration needs, though these figures are based on external reviews rather than vendor price lists.
This makes Decagon cheaper than opaque outcome models like Sierra at the high end, but still an enterprise-level commitment that can outstrip platforms with simpler subscription tiers.
Make sure to read our full comparison of Sierra AI vs Decagon, too.
Poly.ai
Poly.ai sells voice agents with pricing based on usage, typically on a per-minute basis for calls, rather than fixed subscription tiers. Enterprise buyers have reported pricing around $0.95 per minute of voice interaction, which means costs can scale quickly with volume.
There is no flat rate or published tier sheet, and custom quotes are required. Compared with Quiq’s usage tiers or predictable billing, Poly.ai’s per-minute model can make forecasting harder for companies with high inbound voice volumes.
NICE Cognigy
Cognigy’s platform also does not list public pricing tiers. Like the big enterprise competitors, buyers must reach out for a custom quote that reflects channel volume, integrations, and deployment scope.
Organizations that choose Cognigy generally commit to enterprise contracts, though exact numbers are not published. It sits in the same general category as Kore.ai and Decagon in terms of pricing unpredictability, but exact figures are hard to find without vendor engagement.
Replicant
Replicant similarly requires custom quotes for pricing. It focuses on intelligent voice automation with outcome-driven billing, and enterprise deals can run into six figures, particularly when teams choose fully managed voice solutions with high call volumes. Like Poly.ai, Replicant’s costs are tied to usage metrics rather than simple seat or subscription models, and there are no published entry-level prices for smaller teams.
The true cost of Sierra AI
Like any AI Agent pricing model, Sierra’s pricing isn’t expensive or secretive. It merely depends on a variety of different factors, and it’s difficult to give a ballpark number on a pricing page. The outcome-based model that focuses on providing revenue gains from customer interactions is fair, but it’s difficult to predict and that’s not all the cost involved.
Long onboarding cycles, deep customization, and ongoing vendor involvement often add time and internal effort that are not obvious at the start. Over time, these factors can push the total cost well beyond the original estimate and make changes or exits expensive.
That is why many teams look beyond headline pricing and focus on predictability and speed to value, which is where Quiq shines.
Quiq uses clearer usage-based pricing, launches faster, and avoids outcome-driven ambiguity, which usually leads to a lower total cost of ownership and fewer surprises as teams scale.
Book a free demo to find out more about Quiq’s pricing model and how we can help.
