Understanding the true cost of Salesforce’s AgentForce platform is essential for any business considering AI agents. AgentForce is Salesforce’s autonomous AI agent platform that promises to automate tasks and augment your workforce with 24/7 “digital labor.” In this in-depth guide, we’ll break down AgentForce’s pricing models, explore practical cost scenarios, and examine alternatives in the rapidly evolving AI agent landscape of 2025-2026. Whether you’re looking at customer service chatbots or AI assistants for internal operations, this guide will help you grasp what AgentForce could cost you – and how it stacks up against the competition.
Contents
AgentForce and the Rise of AI Agents
AgentForce Pricing Models in 2026
Value, Use Cases, and Limitations of AgentForce
Alternatives to AgentForce: Platforms and Pricing
Future Outlook: AI Agents in 2026 and Beyond
1. AgentForce and the Rise of AI Agents
AI “agents” have quickly moved from sci-fi to business reality. Unlike basic chatbots that only answer questions, AI agents can take actions and complete multi-step tasks autonomously, acting like digital employees. These agents use large language models (LLMs) to understand goals, break them into tasks, and execute them across apps or data sources (kommunicate.io). For example, an AI agent might not only answer a customer question, but also update a record, send an email, or schedule an appointment – all in one interaction.
Salesforce’s AgentForce emerged as a response to this trend. Announced in the mid-2020s, AgentForce is built into the Salesforce ecosystem so it can leverage the data and workflows companies already have in their CRM. The idea is powerful: a support agent that can resolve a customer issue by looking up order history and creating a case, or a sales assistant that autonomously updates pipeline records and drafts follow-up emails. By late 2025, hundreds of new AI agent platforms and tools had launched in the market (kommunicate.io), but AgentForce stands out due to Salesforce’s enterprise footprint and integration with its Data Cloud and CRM services.
Why does pricing matter? Deploying AI agents at scale can become expensive, and the pricing models can be complex. Businesses need to know what they’ll pay per conversation or per action, and where hidden costs might lurk. AgentForce’s pricing has evolved rapidly – from a simple per-conversation fee to a more flexible consumption model – aiming to align cost with the value each agent provides. In the next section, we’ll break down these models so you can understand exactly what AgentForce could cost you in 2026.
2. AgentForce Pricing Models in 2026
Salesforce offers multiple pricing options for AgentForce, designed to fit different use cases and preferences. As of late 2025 going into 2026, you can choose between consumption-based Flex Credits, conversation-based pricing, or fixed per-user plans. Let’s examine each:
Flex Credits – Pay Per Action
The Flex Credits model is Salesforce’s new default for AgentForce. In simple terms, you pay per action that an AI agent performs. Actions are individual tasks the agent completes – for example, looking up a customer record, answering a question from knowledge base, or updating a case note. Each action costs a certain number of Flex Credits, and credits are bought in bulk. The current rate is $500 USD for 100,000 Flex Credits (salesforce.com). This works out to about $0.005 per credit. Since a standard AgentForce action is defined as 20 credits, each action effectively costs about $0.10 (ten cents) (linkedin.com). More complex or voice-based actions might use 30 credits (15 cents), but the principle is the same.
How does this look in practice? Imagine a customer support scenario: an agent handles a case by identifying the customer (one action), retrieving all their past cases (another action), and adding a resolution note (third action). That’s 3 actions totaling 60 credits, which costs roughly $0.30 for that case interaction (salesforce.com). If 100 support agents each handle 3 such cases per day, the monthly Flex Credit consumption would be about 360,000 credits – which costs about $1,800 per month in AgentForce fees (salesforce.com). The Flex Credit model scales with usage, so light usage costs very little, while heavy usage will increase your bill in proportion.
Salesforce provides tools like a Digital Wallet dashboard to track your AgentForce credit usage in real time, helping you monitor costs. You can buy credits as you go or pre-purchase a chunk upfront for a discount. There’s also a “pre-commit” option where you commit to a certain usage level for better rates, paying monthly and truing-up later (salesforce.com) (salesforce.com). The key benefit of Flex Credits is flexibility – you’re charged for actual work done by the AI. If an agent doesn’t do much, you pay little; if it works hard on many tasks, the cost reflects that value. This granularity appeals to many businesses because it aligns cost with delivered value and allows experimentation at low cost (linkedin.com). However, it also means you need to forecast your usage. Complex workflows can involve multiple actions, and these can add up. Even a “routine” interaction might involve 5+ actions, and a complex multi-step process could trigger 20 or more actions across systems (fin.ai). Companies must watch for cost overruns in high-volume scenarios.
Conversation-Based Pricing
Conversation-based pricing is a more traditional model (and was AgentForce’s original pricing method). In this model, you pay a flat fee for an entire “conversation” or session with an AI agent, regardless of how many steps or actions occur. The standard rate is $2 USD per conversation (salesforce.com) (with equivalent pricing in other currencies). This model is primarily intended for customer-facing agents – for example, a customer support chatbot handling an issue from start to finish would count as one conversation, billed at $2.
The advantage here is simplicity and predictability. If you expect a certain volume of customer chats or calls, you can budget $2 each without worrying about the exact number of actions. This was originally set to mirror call center costs (where a human agent handling a call has a known average cost per call) (linkedin.com). It can be convenient for customer service scenarios, especially if each conversation tends to involve many back-and-forth steps. For instance, if one complex conversation would have required 30+ actions (which under Flex would cost ~$1.50 or more), the flat $2 fee might still cover it and cap the expense.
However, as AI agents expanded to other domains (like marketing or internal processes), the per-conversation model proved too rigid. Not every use case fits neatly into “conversations,” and if your AI agent interactions are short (few actions), $2 could end up being expensive. In fact, industry observers have noted that $2 per conversation can be a steep price for simple Q&A bots – one review bluntly called it “atrocious” for high volumes of basic chatbot queries (kommunicate.io) (kommunicate.io). Salesforce recognized this and introduced Flex Credits to offer a cheaper option for quick tasks. Today, conversation pricing is optional and often used only in specific scenarios (typically external chatbots). It is available on a pre-purchased basis only (salesforce.com), meaning you buy a bundle of conversations in advance. You cannot mix conversation pricing and Flex credits in the same org or agent; you must choose one model or the other for a given deployment (salesforce.com) (salesforce.com).
In summary, choose conversation pricing if you desire cost certainty and your agents engage in lengthy, complex sessions with customers (where each session’s value is high). For anything else, the per-action model is usually more economical and flexible.
Per-User Licensing (Unlimited Use for Employees)
Salesforce also offers a licensing approach for internal, employee-facing AI agents. Instead of paying per action or conversation, you pay a fixed fee per user to allow unlimited agent usage by that user. This is essentially an add-on to your Salesforce user licenses.
For example, there is an AgentForce add-on for Sales or Service Cloud users priced at $125 per user per month (salesforce.com). If you attach this to, say, each support agent on your team, those employees can use the AI agent as much as they want without consuming Flex credits. Similarly, an “Industries” add-on (for industries cloud products) is available at $150 per user/month, which includes industry-specific AI capabilities (salesforce.com). Salesforce also introduced AgentForce 1 Editions – premium bundles starting around $550 per user/month that include the AgentForce add-on plus a large allotment of credits (e.g. 1 million Flex Credits per year) and Data Cloud storage credits (salesforce.com). These bundles target enterprises that want an all-in-one package for AI and data usage.
Finally, there is a basic AgentForce User License at $5 per user/month (salesforce.com). This low-cost license simply entitles each employee to access the AgentForce features (e.g. to chat with an internal AI assistant), but still requires Flex Credits for the actions they trigger (salesforce.com). Think of it as a door pass – it gives your staff permission to use the AI, and then you pay per action via the consumption model. In contrast, the $125 add-on is more like an “all-you-can-eat” plan – higher fixed cost, but no metered charges.
When does per-user make sense? These options are geared toward employee productivity agents (like an AI that helps your sales reps write emails or your service agents summarize cases). If your employees will be heavily using the AI assistant as part of their daily job, a fixed cost can prevent unpredictable bills. For example, a busy support agent might invoke the AI 50 times a day – that could be 1,000 actions daily, which on Flex Credits would add up fast. The $125 flat fee might be more cost-effective in such a scenario. On the other hand, if only a few employees use the AI occasionally, sticking with Flex pay-as-you-go (plus the basic $5 license) might be cheaper overall.
It’s worth noting that these licensing options require underlying Salesforce products. You’d typically only consider them if you already use Salesforce CRM (Sales Cloud, Service Cloud, Industry Clouds, etc.) and want to augment those users with AI. The add-on essentially bolts AgentForce onto your existing CRM subscription.
Additional Cost Factors and Hidden Costs
When budgeting for AgentForce, be mindful of some additional cost factors beyond the headline prices:
Data Cloud and Storage: AgentForce is often used in conjunction with Salesforce’s Data Cloud (formerly Customer 360 Audiences/CDP) for unified customer profiles and grounding the AI in up-to-date data. Usage of Data Cloud involves its own credits and fees. In fact, many real-world AgentForce deployments require Data Cloud, which can add significant cost that isn’t obvious from AgentForce’s list price (fin.ai). (The premium AgentForce 1 Edition attempts to bundle some Data Cloud credits for this reason.) If your AI agent needs to access large datasets or store a lot of conversation data, factor in data storage/query costs.
Sandbox and Testing: Salesforce charges for using AgentForce in sandboxes (test environments) at 80% of the normal rate (fin.ai). This means if you run extensive trials or QA testing with your AI agents, those actions still cost you credits (albeit slightly discounted). It’s wise to include some budget for the testing phase of your agent deployment.
Channel Fees: If your agent interacts via certain channels like SMS or WhatsApp, there may be pass-through fees (e.g. WhatsApp charges per message, or telephony costs for voice calls). AgentForce itself supports voice interactions (beta in some regions) and likely leverages Salesforce’s contact center infrastructure or telephony integration, which could incur additional usage fees. Always clarify whether channels like SMS, phone, or third-party integrations will add costs. The Flex Credit pricing covers the AI’s actions, but not necessarily carrier fees for a message sent on WhatsApp or similar (fin.ai).
Implementation and Integration: While not a fee to Salesforce, the cost of implementing AgentForce agents can be non-trivial. Building effective AI agents may require configuring prompts using Salesforce’s Prompt Builder, setting up Salesforce Flow automations, mapping data fields, and defining routing logic (fin.ai) (fin.ai). This often requires a Salesforce admin or developer’s time. Some organizations engage Salesforce consulting partners to help deploy AI agents, which can feel like a “hidden” cost of adoption. Additionally, if you need custom integrations (say your agent must connect to an external system via API), there might be development effort or middleware licensing involved.
Salesforce Platform Requirements: Remember that AgentForce doesn’t operate in a vacuum. You generally need to have the relevant Salesforce platform in place. For customer-facing use, you’d need Service Cloud or a Digital Experience portal to host the agent. For internal use, you need Salesforce licenses for your users. These have their own costs. In short, AgentForce is an add-on – you can’t just buy it standalone without the Salesforce ecosystem. Companies already invested in Salesforce will find it readily fits in, but those who aren’t must consider the cost of Salesforce CRM itself if they were to adopt AgentForce.
To sum up this section, AgentForce’s pricing in 2026 is flexible but multifaceted. You can pick pay-per-action for granular control or pay-per-conversation for simplicity, or even unlimited use licenses for a predictable fee. Each has trade-offs in cost vs. complexity. Beyond the list prices, wise buyers will account for surrounding costs like data usage and implementation. Next, we’ll explore how these costs translate into real business value – and where AgentForce delivers the most benefit (or encounters limitations) in practice.
3. Value, Use Cases, and Limitations of AgentForce
AgentForce opens up many possibilities for applying AI in business. But where does it shine, and where might it struggle or even fail to meet expectations? In this section, we’ll look at common use cases, success stories, and potential pitfalls – all of which factor into the true cost-effectiveness of the platform.
Proven Use Cases and Where AgentForce Succeeds
Salesforce has positioned AgentForce to be used across sales, service, marketing, and more, essentially anywhere you have repetitive tasks or inquiries that an AI can handle. Some of the areas where AgentForce has been most successful include:
Customer Service and Support: This is a natural fit. AgentForce can act as a virtual support agent, either customer-facing (answering common questions, helping troubleshoot issues) or as a co-pilot for human agents (summarizing cases, suggesting next steps). For example, in case management, an AgentForce bot might identify a customer from an email, pull up all their past cases, and draft a response or solution recommendation (salesforce.com) (salesforce.com). This speeds up resolution and reduces workload on human staff. Companies with large call centers or helpdesks see clear value here – you can handle routine queries at low per-action costs, reserving human agents for complex issues. AgentForce’s deep integration with Salesforce Service Cloud means it can log cases, update fields, or tap into the Knowledge Base seamlessly, which standalone chatbot solutions might not do as easily.
Field Service and Scheduling: In field service scenarios (like scheduling maintenance visits or dispatching technicians), AgentForce can automate those multi-step workflows. Salesforce gives an example where an agent finds an available time slot and books an appointment after checking various parameters – using a series of actions to coordinate calendars, work type, and customer availability (salesforce.com). The AI essentially acts as a smart dispatcher. This use case reduces the back-and-forth typically needed to schedule jobs, saving time and ensuring faster service for customers.
Sales and Lead Management: AI agents can help sales teams by automating follow-ups, data entry, or research. For instance, an AgentForce sales assistant could scan a prospect’s LinkedIn and company info (via Data Cloud integration), then suggest a tailored outreach email or update the CRM with new insights. AgentForce Sales (sometimes dubbed “AI Sales Agent”) is intended to automate and scale parts of the sales process – like qualifying leads or even coaching reps on next best actions (salesforce.com). While this is a newer application, some organizations have found success offloading mundane sales tasks (logging calls, scheduling meetings) to the AI, allowing their salespeople to focus on closing deals.
Marketing and FAQ Bots: With Salesforce’s Einstein GPT foundation, AgentForce can power conversational marketing bots on websites or social channels – answering product questions, collecting feedback, or guiding users through product selection. These are similar to traditional chatbots but with more “brains” behind them. They can pull in customer data to personalize responses (e.g., recognizing a returning customer vs a new lead) and even perform actions like creating a follow-up task for a sales team if a lead seems promising.
Internal Employee Support: Companies also use AgentForce internally for things like HR or IT helpdesk bots. An internal AI agent might answer employees’ FAQs (“How do I reset my VPN?”), help onboard new hires by walking them through setup steps, or retrieve internal knowledge base info on policies. With the $5 user license option, deploying a simple help bot to all staff is relatively low-cost. This can deflect tickets from human HR/IT teams. However, unlimited internal use might warrant the $125 user add-on if usage is heavy. The benefit seen here is improving employee productivity – quick answers and actions without waiting on support staff – which can justify the cost if it saves enough employee time.
In all these cases, the value proposition of AgentForce is saving human labor and speeding up processes. The pricing, while not cheap, is usually far lower than the cost of a human doing the same task. For example, a 10-cent action to update a record is trivial compared to an employee spending a few minutes on it. When scaled to thousands of actions, the ROI can be significant – Salesforce even provides an ROI calculator tool to estimate productivity gains versus cost.
Another success factor for AgentForce is integration and governance. Because it operates inside Salesforce, it respects your existing data permissions, audit trails, and compliance settings (fin.ai) (fin.ai). Regulated industries (finance, healthcare, etc.) appreciate that control. All interactions are logged in the CRM, and you can apply governance (approvals, human review steps) if needed. Competing agents outside your system might lack that level of enterprise oversight.
Where AgentForce May Struggle or Fail
Despite its strengths, AgentForce is not a magic bullet for every scenario. There are limitations and potential pitfalls:
Salesforce-Centric: AgentForce is designed for Salesforce environments. If your data and processes aren’t largely in Salesforce, the agent’s effectiveness drops. For instance, AgentForce works great with Salesforce CRM data, but it has limited reach into external tools unless you integrate them. Some competitor AI agents can connect natively to multiple CRMs or helpdesk systems, whereas AgentForce is mostly limited to Salesforce-native channels and surfaces (fin.ai). So, a company using Zendesk for support or HubSpot for sales might not benefit from AgentForce without migrating or doing custom integration. In short, it’s an ideal solution for Salesforce-centric organizations, but not a one-size-fits-all for those with diverse systems.
High Cost for Simple Needs: While AgentForce can handle complex tasks, using it for very basic chatbot needs could be overkill both in complexity and cost. There are lighter-weight AI Q&A bots that might be far cheaper. If you only need a simple FAQ chatbot on your website and you don’t need deep CRM integration, a $2 per conversation or multi-action AgentForce bot might not be cost-effective. Some have noted that competing solutions can achieve similar outcomes at a fraction of the cost (kommunicate.io) (kommunicate.io). Thus, if the tasks are simple, the premium you pay for AgentForce’s sophistication and integration might not be justified.
Complex Setup for Advanced Workflows: Salesforce touts AgentForce’s prompt-based agent builder as “no-code” – you can create an agent by describing its purpose in a prompt. That works for basic scenarios. But to really automate end-to-end business processes, often you need to involve Salesforce Flow (automation workflows), custom prompts, and metadata mapping (fin.ai) (fin.ai). Many users find that to achieve reliable performance, they must do significant configuration and iteration. This requires skilled admins or developers familiar with Salesforce’s ecosystem. If misconfigured, an AI agent might perform inconsistently or even fail to resolve queries. Some early adopters reported scalability and reliability issues at larger scales (fin.ai), which suggests that as the number of agent interactions grows, careful tuning is needed to maintain quality. In contrast, some alternative platforms focus on a more self-contained no-code experience for designing workflows. So, AgentForce can fail to deliver value if the team deploying it isn’t equipped to configure it properly. The risk is you invest in the platform, but due to complexity, your implementation underperforms or remains limited to narrow use cases.
AI Limitations and Accuracy: AgentForce uses underlying AI models (Salesforce’s Einstein GPT, possibly partnered LLMs) to power the agent’s understanding and generation. Like any AI, it can make mistakes – give a wrong answer, misinterpret a request, or take an unintended action if not sandboxed. Salesforce has built-in guardrails (e.g. an agent can only do what it’s been given permission to do in the CRM), but there’s always a chance of “AI hallucination” or error. For example, if an agent is supposed to draft an email to a customer, a mistake might mean a poorly phrased or incorrect email goes out. That can hurt customer experience. Thus, in critical applications, companies often keep a human-in-the-loop to review AI outputs initially, which can reduce the immediate efficiency gain. Over time as trust builds, more autonomy can be given. But it’s important to set the right expectations: AgentForce is advanced, but not infallible. If used in areas where absolute accuracy or empathy is needed (say medical advice or sensitive financial interactions), failures can be costly. Sometimes the “failure” is not catastrophic but simply that the agent couldn’t complete the task and had to escalate to a human – which is fine, but you’ve still paid for the attempted actions.
Volume and Performance Constraints: Since AgentForce works within Salesforce’s cloud, it might inherit some platform limits (e.g. API call limits, throughput limits) if usage is extremely high. Salesforce likely has accommodations for this within the AgentForce product (given the digital labor concept, it’s meant to scale), but it’s worth monitoring. If you plan to deploy dozens of AI agents handling millions of actions, engage Salesforce early to ensure the infrastructure (and pricing model) can handle it efficiently. In some cases, it might be more cost-effective to use a specialized AI solution for extremely high-volume, low-complexity interactions (like offloading to a simpler FAQ bot).
In summary, AgentForce delivers the most value when used for what it’s best at: leveraging Salesforce data and automation to handle moderately complex, high-impact tasks in sales, service, and operations. It can fail to impress if used outside that sweet spot – either because it’s too expensive for trivial tasks or because it’s too constrained to Salesforce when you need broader integrations. Companies should evaluate their use case carefully: do you truly need an AI agent embedded in CRM doing multi-step processes (justifying AgentForce), or would a simpler bot or different platform suffice? If you do go with AgentForce, invest in proper setup and training. The costs we discussed earlier will pay off only if the agent is configured to actually solve problems consistently. Next, we’ll broaden our view and look at alternatives to AgentForce – other major players and emerging solutions in the AI agent market, and how their pricing compares.
4. Alternatives to AgentForce: Platforms and Pricing
Salesforce may be a leader in CRM, but in the world of AI agents it’s just one of many players. By late 2025, a host of companies – from startups to tech giants – had introduced their own autonomous agent platforms. If you’re exploring AgentForce, it’s wise to also consider these alternatives, as they differ in capabilities, approach, and pricing. Here we highlight several noteworthy ones and how they compare:
Intercom Fin (Fin AI Agent): Customer messaging platform Intercom made a splash with Fin, an AI customer service agent launched in 2023. Fin is laser-focused on customer support automation and boasts impressive performance (Intercom claims high resolution rates). The pricing is straightforward and outcome-based: about $0.99 per resolved conversation (fin.ai). In other words, you pay roughly $1 when Fin successfully handles a customer query from start to finish. If Fin has to hand off to a human agent, you might not be charged for that conversation. This model is very transparent – it aligns cost to successful outcomes, making budgeting easy. Intercom also offers a 14-day free trial and requires a minimum commitment for larger deployments (fin.ai). If you use Intercom’s own helpdesk software, Fin can be integrated for an extra platform fee (e.g. Intercom’s plans might add $29 per support seat) (fin.ai). Fin’s big advantage is that it’s built into the support experience – it can handle tickets, live chat, email, even WhatsApp, working alongside human agents in the Intercom inbox. Unlike AgentForce, Fin is not limited to Salesforce; it can connect with other helpdesk systems like Zendesk or Freshdesk too (fin.ai). For companies whose primary need is customer support automation (and especially if they already use or don’t mind using Intercom), Fin offers a compelling alternative. Its per-conversation cost can be much lower than AgentForce’s $2 conversation fee, and there are no separate “credit” charges for knowledge base access or sandbox – it’s all included in that $0.99 resolution price (fin.ai). However, Fin doesn’t natively perform actions in your CRM like updating Salesforce records (outside of what’s needed to resolve the ticket), so its scope is narrower than AgentForce’s cross-CRM workflow automation. It excels in Q&A and issue resolution within support contexts.
Microsoft Copilot (and Power Virtual Agents): Microsoft has integrated generative AI “Copilot” features across its product suite, and it also offers AI agent capabilities, especially through Power Virtual Agents and the newer Copilot Studio. Enterprises already using Microsoft 365 or Dynamics might lean this way for tight integration and security. Microsoft’s approach often requires more configuration – one has to design the bot’s dialogs or flows (similar to the old school bot design, albeit augmented with GPT). The pricing for Microsoft’s AI conversational agents tends to be on the higher side as well. For instance, one reported figure is about $200 for 25,000 messages per month for Microsoft’s AI chat service (kommunicate.io). This was described as a “high entry” cost in comparisons, roughly translating to $0.008 per message – which might seem cheap per message, but it’s a bulk package and smaller usage might not have cheaper tiers. Microsoft’s selling point is enterprise-grade data protection and compliance (kommunicate.io). If you are in a highly regulated industry and already in the Microsoft ecosystem (using Azure OpenAI, etc.), Copilot or Power Virtual Agents might fit in well. They allow deployment across Teams, web chat, etc., and benefit from Azure’s AI infrastructure. However, many users found Microsoft’s AI bot offerings a bit rigid and technical; you might need developers to fully utilize the Copilot framework (kommunicate.io). In comparisons, some users felt alternative solutions had better UI and lower cost for similar functionality (kommunicate.io). So, while Microsoft is a credible alternative (especially for large enterprises), be prepared for higher upfront costs and a potentially steep learning curve in exchange for that Azure/M365 integration and security.
IBM watsonx Orchestrate: IBM has been a longtime player in AI for business, and their latest foray is watsonx Orchestrate, an AI agent platform aimed at automating work across emails, schedules, and business apps. IBM’s offering emphasizes a multi-agent orchestration (multiple AI agents collaborating) and comes with a library of pre-built skills. The pricing model here is more traditional SaaS: the Essential plan starts at around $500 per month for one Orchestrate instance (g2.com) (g2.com). That is a fixed subscription which includes the core AI capabilities and a catalog of integrations. Higher tiers (Standard plan) with more advanced automation features are “contact us” pricing – likely more expensive or enterprise-negotiated (g2.com). IBM often bundles its solutions with consulting services, and they tout that watsonx Orchestrate can be deployed on IBM Cloud, AWS, or on-premises for flexibility (ibm.com). Who should consider IBM? Likely, large enterprises that already trust IBM for systems integration and want a solution that can be heavily customized. IBM’s AI agents can handle things like coordinating meeting scheduling, generating reports, or other back-office tasks using both AI and RPA (robotic process automation) elements. The trade-off is that IBM’s AI models (like their Granite and Llama-based models) have not been seen as as powerful as the latest OpenAI or Anthropic models in some cases (kommunicate.io) (kommunicate.io), and the product has been described as code-heavy and not yet a smooth DIY tool for end-users (kommunicate.io) (kommunicate.io). Essentially, IBM Orchestrate can be very powerful with the right development team behind it, and it comes with IBM’s renowned support for governance (data stays in your control, etc.). But at a starting cost of about $6,000+ per year (for Essentials) and likely much more for enterprise deployments, you need to justify that investment. It may be overkill if your needs are simpler or if you’re not an “IBM shop.” On the other hand, for highly regulated industries that demand on-prem or private cloud solutions, IBM offers options there that Salesforce’s cloud-only service might not.
Sierra AI: A notable newcomer on the scene is Sierra AI, a startup co-founded by ex-Salesforce CEO Bret Taylor. Sierra positions itself as a platform to build customer-facing AI agents with a strong emphasis on being highly personalized to a business. They’ve developed something called “Agent OS” under the hood (nextword.substack.com), aiming to make it easier to deploy AI agents that represent your brand and handle customer interactions. Because Sierra is relatively new (founded in 2023) and likely in a rapid development phase, details on pricing are not very public. Early indications are that Sierra is likely outcome-focused in pricing (possibly charging per successful customer interaction or via a SaaS license) and working closely with pilot customers to refine its model. In one analysis, Sierra’s approach was noted as having “unclear pricing structure, outcome-based” (kommunicate.io). This suggests they might tailor pricing to each customer or base it on results achieved (similar in spirit to Intercom’s Fin). Why keep an eye on Sierra? For one, its leadership comes straight out of Salesforce, so they understand the domain deeply and are building with the latest AI tech from scratch. They might introduce innovative pricing or features that undercut bigger players. Sierra also touts a proprietary model for grounding the AI in a company’s data, which could reduce the need for something heavy like Salesforce Data Cloud. As of 2025, Sierra is an “up-and-coming” player – it may not have the breadth of features yet, but it could be more agile and potentially more cost-effective for specific customer experience use cases. If you are exploring AI agents specifically for customer support or engagement and are not tied to a big vendor, Sierra could be a modern, albeit less proven, alternative to consider. Just be prepared that as a startup solution, you’d be engaging in a bit of a partnership to shape the product, including how you’ll be billed for it.
O-Mega AI: Another emerging platform is O-Mega.ai, which markets itself as providing a “virtual workforce” of AI agents. O-Mega’s vision is to offer AI workers that can autonomously handle knowledge work tasks across browsing, data entry, communications, etc., almost like hiring digital employees. The platform emphasizes ease of deployment – no complex API setup or manual workflows, as the agents learn and operate like a human would through the interface. In terms of pricing, O-Mega uses a credit-based model as well: each action an AI worker takes (like clicking a browser button, sending an email, or calling an API) consumes 1 credit (o-mega.ai). While specific prices for credit bundles aren’t publicly listed, the approach is similar to AgentForce’s per-action concept, but applied broadly to any kind of task (not just CRM actions). O-Mega allows you to create unlimited agents in your account and connect them to various tools you use (Google Sheets, your CRM, social media, etc.), so you could have a whole team of specialized AIs – e.g., one for doing research, one for social media management, one for lead outreach. It’s a flexible, horizontal platform for automation. As an alternative, O-Mega is appealing for those who want quick automation without much technical setup. You could get started free, then pay for what you use. Compared to AgentForce, O-Mega is not Salesforce-specific; it’s aiming to integrate with anything via the web or APIs. This means it might solve problems AgentForce can’t, but it also might lack the deep CRM-specific optimizations. In a sense, if AgentForce is like hiring an AI specialist inside your Salesforce org, O-Mega is like hiring a general AI assistant that can work across many apps. Depending on your needs, one or the other could fit better. For pricing, if you foresee an agent doing, say, 1,000 actions a month and if credits are priced roughly in the same ballpark as Salesforce’s (pure speculation: perhaps $0.01 or less per action), it could be quite cost-effective. But as always, you’d need to verify current plans with the vendor.
Other Noteworthy Mentions: There are many others in the AI agent space. A few examples:
Ada CX – A well-known AI chatbot platform for customer support which has added generative AI capabilities. Ada typically charges on a subscription plus usage basis (historically per resolved conversation or MAU, not publicly cheap, as it’s enterprise-focused). It’s a competitor if you’re mainly looking at customer service automation, and you don’t need the agent to do CRM updates beyond what Ada’s integrations allow.
Kore.ai, Cognigy, and Dialogflow – These are conversational AI platforms that let you design virtual assistants. They often offer robust dialog management and integration options. Pricing varies (from pay-as-you-go cloud usage in Dialogflow’s case to enterprise licenses for Kore/Cognigy). They might be alternatives if you need multi-channel chatbot experiences with fine control. However, they may require more bot-building effort and might not reach into performing “agentic” tasks beyond conversations unless combined with RPA.
Open-Source Agent Frameworks – For the adventurous (and more technical), frameworks like LangChain, AutoGPT, and SuperAGI have emerged, allowing developers to build custom autonomous agents. These let you program an AI to use tools, browse the web, or execute code. The benefit here is flexibility and potentially lower cost – you’re only paying for the compute (e.g., OpenAI API calls or running open-source models) rather than a vendor’s platform fees. Some companies with strong engineering teams have experimented with building their own agents using these tools, especially for internal workflows. The downside is the engineering effort and maintenance required. Unlike a polished product like AgentForce or Fin, open-source solutions might need constant tuning and don’t come with support. But for non-mission-critical tasks or prototypes, this route can be the cheapest. For example, a company might rig up an AutoGPT agent to automate some internal research tasks, paying just for API usage (which might be a few cents per action) – essentially cutting out the middleman. It’s an alternative in the truest sense: build vs buy for AI agents.
In evaluating alternatives, consider what ecosystem you are most comfortable with and the nature of your use case. AgentForce excels for Salesforce-centric workflows and tight integration. Fin shines for customer support with minimal fuss. Microsoft and IBM appeal to those prioritizing enterprise integration with their stacks. Startups like Sierra and O-Mega promise cutting-edge approaches that might leapfrog on flexibility or cost, but with the risk of young products. Pricing among these varies widely: some charge per conversation, some per action, some per user/month, and some are custom quotes. Make sure to project your usage (how many interactions or actions you expect) and compare the effective cost. Also weigh the “total cost” including any needed manpower to set up and maintain the agent. A cheaper platform that requires heavy dev work could end up costing more in the long run than a pricier one that’s plug-and-play.
5. Future Outlook: AI Agents in 2026 and Beyond
As we head into 2026, AI agents like AgentForce are at the forefront of a transformative wave in business technology. It’s worth pondering the future trends – not only for how pricing might change, but how capabilities will evolve and what that means for ROI.
Continued Evolution of Pricing Models: We’ve already seen Salesforce revise AgentForce’s pricing model within a short span (from $2 per conversation to a $0.10-per-action model, plus new unlimited-use licenses) (linkedin.com) (linkedin.com). This reflects a broader trend: vendors are experimenting to find pricing that customers perceive as fair and aligned with value. In the future, we may see more outcome-based pricing – charging only when the AI agent successfully completes a task or saves a certain amount of time/money. This could be facilitated by analytics that measure resolution rates or customer satisfaction. We might also see competitive pressure driving costs down. If a new entrant offers a similar service for half the cost per action, Salesforce and others may respond by adjusting prices or bundling more value (for example, including more free credits with existing CRM subscriptions).
For now, in late 2025, AgentForce’s Flex Credit model at ~$0.10/action is touted as simpler and often lower than many competitors’ equivalent costs (linkedin.com). But by 2026, as more players (including open-source models) proliferate, we could see a “race to the bottom” in pure per-action pricing. Vendors might differentiate instead on quality, ease-of-use, and included features rather than price alone. One might bundle unlimited knowledge base usage, another might include a free sandbox environment, etc., to add value.
AI Agents Getting Smarter and More Specialized: The capabilities of AI agents are set to improve significantly. With advances in LLMs (GPT-4, GPT-5, and beyond) and more fine-tuning on domain-specific data, we can expect agents that are more accurate, faster, and able to handle more complex requests. For AgentForce, Salesforce’s roadmap (as seen with announcements like AgentForce 3) indicates deeper integration with their entire Customer 360 platform and possibly more industry-specific agent templates (apexhours.com). For example, we might see pre-built agents for healthcare patient inquiries or retail e-commerce help, which companies can deploy with minimal training. As agents get smarter, the value side of the value/cost equation improves – you might get resolutions in areas that previously weren’t feasible to automate.
One exciting development is agents being able to chain together tasks across systems intelligently. The early versions can do steps, but often in a fixed, pre-defined flow. Future agents may dynamically decide to invoke other AI services or tools as needed (for instance, automatically call a translation service if a customer writes in Spanish, then proceed to solve the issue). This kind of adaptability will make AI agents more robust. It could, however, introduce new pricing considerations – e.g., if your AgentForce bot starts leveraging an external AI API via Salesforce’s platform, that might incur additional charges or partner fees.
Integration of Multiple AI Agents (Agent Teams): We may see scenarios where multiple specialized AI agents collaborate. Salesforce’s platform might allow one agent to escalate to another agent that has a different specialty (kind of like how human teams work). This concept is present in IBM’s multi-agent orchestration and is likely to spread. If that happens, pricing might shift to package deals (for example, a base fee covers a “team” of 5 AI agents working together). It’s not hard to imagine Salesforce or others offering an “AI workforce” bundle: a set of agents for, say, $X per month that cover different roles (one for support, one for sales, one for IT helpdesk, etc.).
AI Agents Changing Job Roles and Workflows: On the business side, as AI agents become commonplace, companies will refine how they use human employees in tandem. Rather than replacing people, many are finding the best results with human-AI collaboration. For example, an AI agent triages and handles 70% of routine cases, and the humans handle the tricky 30%. Or an AI sales assistant pre-qualifies leads so that human sales reps only spend time on the most promising ones. This trend will affect how businesses justify the cost of AI agents – it’ll be about augmentation. In a way, calculating the cost of AgentForce might start to resemble calculating the cost of a new hire, except this “hire” is digital. Businesses might start allocating budget for “digital workers” as a category, separate from software or headcount, which is a mindset shift.
Competition and Industry Landscape: By 2026, we’ll likely see some consolidation. The dozens of startups offering AI agents will shake out – some will be acquired by bigger players (perhaps a CRM competitor will buy a startup to embed their own AgentForce rival), others might pivot. Big Tech will continue to embed AI capabilities into their existing platforms too. For instance, we could see Google amplifying its Contact Center AI with more autonomous features, or even an AI agent built into Google Workspace to handle tasks across Gmail/Calendar (Google hasn’t fully productized that yet, but their Duet AI hints at it). Amazon might integrate agents into AWS offerings for customer service or IT operations. This means AgentForce will continuously face pressure to improve and innovate, which is a good thing for customers. More options usually lead to better pricing and features.
Regulation and Trust: A factor that might influence AI agent deployment is regulation. By 2026, there could be new laws about AI transparency, especially in customer-facing roles. Companies might be required to disclose to users when they’re chatting with an AI agent, or to maintain audit logs of automated decisions. Salesforce’s enterprise approach likely already covers audit trails (AgentForce logs actions in the CRM), which is a plus. But compliance needs could add overhead – for example, storing conversation logs securely for a certain period (which might circle back to Data Cloud costs). If certain AI uses are restricted (say in finance, giving advice might require the AI to be certified or heavily monitored), some companies might hold back on full autonomy. However, overall trust in AI for routine tasks is growing as success stories accumulate.
The future of AI agents is bright and dynamic. For someone evaluating AgentForce now, it’s important to stay agile. Revisit the pricing and features periodically – what was true in late 2025 could change by mid-2026. Perhaps Salesforce will include a chunk of free AI usage with every CRM license, or perhaps usage costs will plummet as AI becomes commoditized. Conversely, if your usage skyrockets, consider negotiating enterprise deals or switching models (Salesforce does allow switching from conversation to flex if needed (linkedin.com)). Keep an eye on emerging alternatives like the ones we discussed; today’s newcomer could be tomorrow’s leader with a more efficient solution.