Agentforce vs. Google Gemini Enterprise

The shortlist for “enterprise agent platform” used to be a Salesforce-versus-Microsoft conversation. It isn’t anymore. Google folded Agentspace and its Vertex AI agent tooling into a single product — Gemini Enterprise — and now pitches it as the front door to agents for the whole company, not just for teams that live in the CRM. So the question landing in inboxes has changed shape: not “should we build or buy,” which we’ve answered elsewhere, but “we already run Salesforce and Google Workspace — which platform should own our agents?”

It’s a fair question with an unsatisfying honest answer: for most enterprises the winner isn’t one or the other, because in April 2026 Salesforce and Google Cloud agreed to make their agents interoperate rather than compete for the same seat. But “use both” is a cop-out unless you know which agent belongs where. That’s a real architectural decision with cost, latency, and governance consequences, and getting it wrong means either an agent that can’t see the data it needs or a consumption bill that scales with your mistakes. This post is the comparison at the level that actually decides it: what each platform grounds on, how each one charges, and the rule for assigning a workload to one side or the other.

The core difference: what each agent is grounded in

Strip away the marketing and an agent platform is three things bolted together — a reasoning model, a grounding layer that feeds it trustworthy context, and an action layer that lets it do something. The models are increasingly a shared commodity; both platforms will happily run a frontier model, and Agentforce itself already lets you pick Google’s Gemini models alongside OpenAI and Anthropic. The reasoning layer is not where these two diverge. The grounding layer is.

Agentforce grounds in the CRM and Data Cloud. Its entire premise is that the agent sits inside the system of record — it can read the account, the open cases, the entitlements, the order history, and the Knowledge base without an integration project, because that data already lives on the platform the agent runs on. When it needs data from outside, Data 360 zero-copy federation and MuleSoft pull it in. The grounding is transactional: the agent knows this customer, this record, this moment.

Gemini Enterprise grounds in the Google workspace and the data lake. Its center of gravity is unstructured enterprise knowledge — the documents in Drive, the threads in Gmail, the tables in BigQuery, plus connectors to third-party apps — surfaced through Vertex AI Search. It’s exceptional at “find and reason over everything the company has written down,” and BigQuery makes it strong on analytical, warehouse-scale questions. What it does not have natively is a governed customer record with a sharing model, field-level security, and a transactional action layer — that’s the thing Salesforce has spent twenty-five years building.

The platform to pick is the one already holding the data your agent has to be right about. If the agent’s job depends on the customer record, the entitlement, the order — that gravity pulls to Agentforce. If it depends on documents, analytics, and knowledge scattered across the company — that gravity pulls to Gemini Enterprise.

This is the same principle we apply inside Salesforce when we decide whether an agent even needs Data Cloud: grounding quality is a data-location question before it’s a model question. Across vendors, it’s the whole ballgame.

How each one charges — and why the pricing shapes are not comparable

The pricing models are where teams get genuinely burned, because the two vendors don’t just charge different amounts — they charge along different axes, so a spreadsheet that compares them line-for-line is comparing nothing.

Agentforce is moving to outcome- and conversation-based pricing. The headline model is per-conversation (Salesforce has publicly anchored a Service Agent conversation around the low-single-dollar range), and the flexible path is Flex Credits — a consumable balance drawn down as agents take actions. The mental model is “I pay when an agent does a discrete unit of customer-facing work.” The cost driver you manage is the number of conversations and the number of billable actions per conversation — which is exactly why we wrote a whole piece on Agentforce pricing and Flex Credits.

Gemini Enterprise is consumption-metered like cloud infrastructure. There’s a per-seat element for the assistant experience, but the agent runtime bills the way GCP bills everything else: the Agent Engine charges for compute by the vCPU-hour and GB-hour, Vertex AI Search charges per thousand queries at a rate that varies by tier, session and memory storage charges per thousand events, and the foundation-model tokens are billed separately on top. The mental model is “I pay for the infrastructure my agents consume, continuously.” The cost driver you manage is runtime, query volume, and token throughput.

The practical consequence: Agentforce cost is legible per interaction, Gemini Enterprise cost is legible per resource. A high-volume, short-transaction workload — tier-1 case deflection, order status, password-style resets — is easy to forecast on a per-conversation model and can get expensive on a metered-runtime model if it’s chatty. A low-volume, heavy-reasoning workload — an analyst asking sprawling questions across a data lake — is the opposite: cheap on metered consumption, potentially awkward to price as “conversations.” Model your actual workload shape against each axis. (Treat every rate here as illustrative and confirm current numbers against each vendor’s pricing page — both have repriced their agent products more than once, and the specific figures move.)

The build experience: clicks, code, and where each is stronger

Both platforms now offer a natural-language-first authoring path and a code-first escape hatch, and they’ve converged more than either would admit.

On the Salesforce side, you build topics and actions in Agent Builder, drop to Apex or Flow for custom actions, and — for regulated flows where “probably” isn’t good enough — pin behavior down with Agent Script’s deterministic control. The strength is that actions are first-class Salesforce metadata: they inherit the org’s security model, they’re testable, and they deploy through the same pipeline as the rest of your org.

On the Google side, Agent Studio is the visual, low-code builder — you describe the agent and it scaffolds the configuration (“vibe coding” agents, in Google’s phrasing) — and the Agent Development Kit (ADK) is the code-first Python framework for custom logic and multi-agent orchestration. The strength is raw model and data-science flexibility: if your team lives in Python and BigQuery, ADK feels native in a way a declarative CRM builder never will.

The tell is your team, not the tooling. A Salesforce admin-and-architect team ships faster on Agentforce because the actions are the platform primitives they already know. A data-science-and-platform-engineering team ships faster on Gemini Enterprise because it’s a cloud-native toolkit that assumes code. Neither is “easier” in the abstract; each is easier for a different org.

Governance: the same words, different trust boundaries

Both vendors say “grounded, governed, secure.” The substance is different because the trust boundary sits in a different place.

Agentforce runs inside the Einstein Trust Layer — prompts and responses pass through data masking, toxicity scoring, and an audit trail, and crucially the agent’s data access is bounded by the user’s Salesforce sharing rules and field-level security. The permission model is the one your org already enforces; the agent doesn’t get a wider view of the data than the running user would. That’s a genuine advantage for regulated, record-level workloads, and it’s the same discipline we describe in governing an agent fleet.

Gemini Enterprise inherits Google Cloud’s IAM, VPC controls, data-residency options, and the access controls on the underlying Workspace and BigQuery sources. It’s a mature, well-instrumented cloud security posture — but it’s a cloud-resource posture, not a CRM-record-sharing posture. If your governance requirement is “this agent must never surface a field this user can’t see on the record,” that maps cleanly onto Agentforce’s sharing model and requires deliberate design on the Google side. If your requirement is “keep everything in our VPC service perimeter in this region,” Google’s controls are built for exactly that.

The plot twist: they interoperate, so it’s not either/or

Here’s what changes the whole framing. In April 2026, Salesforce and Google Cloud announced an expanded partnership specifically to let their agents act across both platforms — an Agentforce agent and a Gemini-powered agent invoking each other and sharing context, rather than a customer being forced to standardize on one. This isn’t a vague “we’re friends” press release; it rides on the open agent-interoperability standard we’ve covered in the Agent2Agent (A2A) protocol, which is precisely the plumbing that lets an agent on one vendor’s runtime delegate to an agent on another’s.

That turns the decision from “pick a platform” into “assign each workload to the right runtime and let them talk.” Concretely:

The customer-facing service agent — grounded in the case, the entitlement, and the order — lives on Agentforce, because the data it must be right about is in the CRM.
The internal research and analytics agent — grounded in documents, wikis, and the BigQuery warehouse — lives on Gemini Enterprise, because that’s where its grounding data is.
When the service agent needs a deep analytical answer (“what’s the churn-risk pattern for accounts like this one?”), it delegates to the Gemini agent over A2A, gets a grounded answer, and continues the customer conversation. Neither agent had to replicate the other’s data.

This is the multi-vendor version of the same pattern we design inside Salesforce for multi-agent orchestration: a small number of well-scoped agents, each grounded in the data it owns, handing off across a clean protocol instead of one bloated agent pretending to know everything.

The decision rule

Stop trying to crown a universal winner and answer three questions per workload:

Where does the data this agent must be right about actually live? CRM records, entitlements, transactional state → Agentforce. Documents, knowledge, warehouse-scale analytics → Gemini Enterprise. This one question resolves most cases on its own.
What shape is the workload — high-volume short transactions, or low-volume heavy reasoning? The first prices cleanly per conversation (Agentforce); the second prices cleanly per consumption (Gemini Enterprise). Model your real volumes against both axes before you sign anything.
What is the binding governance constraint? Record-level sharing and field-level security that must exactly mirror the user’s CRM access → Agentforce. VPC/service-perimeter and data-residency controls over cloud resources → Google Cloud.

For a Salesforce-anchored enterprise, the center of gravity for customer-facing agents is almost always Agentforce, because the grounding data and the action layer are already there and the security model is the one you already run. Gemini Enterprise earns the internal-knowledge and analytics agents, where Google’s document search and BigQuery reasoning are genuinely ahead. And because the two now interoperate over open protocols, the sophisticated answer is a portfolio, not a monogamy — each agent on the runtime that owns its data, delegating across the seam. The teams that treat this as a religious platform war will overpay to force one platform to do a job the other was built for. The teams that treat it as a routing problem will spend less and ship agents that are actually grounded.

Understanding the basics

What is the difference between Agentforce and Google Gemini Enterprise?

Agentforce is Salesforce’s agent platform, grounded in the CRM and Data Cloud and priced per conversation or via Flex Credits — it’s strongest for customer-facing, record-grounded, transactional work where the agent must respect the org’s sharing and field-level security. Gemini Enterprise is Google’s consolidated agent platform (bringing together Agentspace and Vertex AI agent tooling), grounded in Google Workspace, third-party connectors, and BigQuery, and metered like cloud infrastructure — it’s strongest for internal knowledge search and warehouse-scale analytics. The deciding factor is usually where the data the agent must be right about lives.

Can Agentforce and Gemini Enterprise agents work together?

Yes. In April 2026 Salesforce and Google Cloud expanded their partnership to let agents act across both platforms — an Agentforce agent can delegate to a Gemini-powered agent and share context, and vice versa. This interoperability builds on the open Agent2Agent (A2A) protocol, so a customer-facing Agentforce agent can hand a deep analytical question to a Gemini Enterprise agent grounded in BigQuery and continue the conversation without either platform replicating the other’s data.

Which is cheaper, Agentforce or Gemini Enterprise?

Neither is universally cheaper because they charge along different axes. Agentforce bills per conversation or per action (Flex Credits), so cost is legible per customer interaction and forecasts well for high-volume, short-transaction workloads. Gemini Enterprise bills for consumption — compute by vCPU-hour and GB-hour, search per thousand queries, plus model tokens — so cost is legible per resource and forecasts well for low-volume, heavy-reasoning workloads. Model your actual workload shape against each pricing axis rather than comparing headline rates, and confirm current numbers on each vendor’s pricing page.

Trying to decide which agents belong on Agentforce, which belong elsewhere, and how to wire them together without paying twice for the same data? Talk to us — mapping workloads to the right runtime, grounded in the right data, is exactly the work we do.

Agentforce vs. Google Gemini Enterprise: two bets on the agentic enterprise, and when each one wins