Data 360 or Snowflake? Differences and cost

The question is not "do we need Data 360?". It is: which architectural layer do we need, for what objective and at what cost.

Snowflake is an enterprise data platform: it collects, stores, transforms, governs and makes data available to analytics, BI, data science teams and applications. The cost is driven mainly by the compute, storage and services used (Snowflake, pricing).

Data 360 - the Salesforce data layer, called Data Cloud until recently - connects Salesforce and external data and makes it usable for segmentation, CRM, automation, analytics and AI inside the Salesforce ecosystem. It can reach Snowflake data through zero-copy, avoiding physical duplication and traditional ETL flows in several scenarios.

They are not alternatives of the same kind, and in enterprise companies they often coexist. The choice does not depend on the product being pitched to you, but on the question you have to answer:

Do we have to build an enterprise data platform, or do we have to use company data to make CRM and AI more useful?

The essential difference

Aspect	Data 360	Snowflake
Primary role	Making data usable inside Salesforce for CRM, segmentation, automation and AI	Enterprise data platform for storage, transformation, analysis and distribution
Primary users	CRM, marketing, sales and service teams, Salesforce admins	Data engineers, analysts, BI, data scientists, applications
Strength	Native activation of data inside Salesforce processes	Scalable management and analysis of company-wide data
Salesforce data	Native	Can be integrated and analysed, but it is not the CRM's operational layer
Data from ERP, IoT, e-commerce	Connects it and makes it usable in the Salesforce context	Collects, models and governs it as part of the data platform
AI and agents	Useful to agents that have to use Salesforce data and actions	Useful for analytics, feature engineering, models and AI applications
Cost model	Credits, profiles or dynamic consumption, depending on the contract	Compute, storage and services on consumption

Snowflake organises and governs the data estate. Data 360 makes it usable inside Salesforce flows.

When the answer is Snowflake

When the problem is company-wide and not limited to the CRM. Investing first, or mainly, in Snowflake makes sense if you have to:

consolidate data from ERP, CRM, e-commerce, supply chain, plants, logistics and finance;
create a common data model for the whole company;
build reporting, analysis and data science on large volumes;
separate storage, transformation and consumption of data from individual applications;
serve several business functions, not only sales, service or marketing;
expose governed data to AI tools, internal applications or external platforms.

The model is consumption-based: you mainly pay for compute and storage, with costs varying by edition, cloud provider, region and actual usage. Storage is calculated on the monthly average of compressed data. That does not mean Snowflake costs less than Data 360: it means the cost is tied more directly to data workloads.

When the answer is Data 360

When the expected value sits close to Salesforce processes. For example when you have to:

unify customers, contacts, accounts and interactions coming from several sources;
create segments and audiences usable in marketing, sales or service;
make external data reachable inside CRM processes;
feed automation, personalisation and customer journeys;
give an AI agent information that does not live only in the CRM;
activate Snowflake data in the Salesforce operational context without building a custom integration for every use case.

Data 360 has commercial models based on credits, profiles and dynamic consumption. Before signing, ask which model you are being offered, which activities consume capacity and what is not included in the first quote (Salesforce, pricing).

Data 360 does not replace Snowflake. If you already have a solid data platform, it can become the CRM activation layer. If you do not, it can solve specific Salesforce-related needs well, but it is not the obvious choice as the data foundation for the whole enterprise.

When you need neither

Not every AI use case requires a new data platform. If the first use case works on data already present and reliable inside Salesforce, with a defined process and clear permissions, you can start without adding either Data 360 or Snowflake. For example:

an assistant for internal users based on Salesforce knowledge articles;
support for completing a CRM record;
finding information already available on accounts, cases or opportunities;
classifying or summarising requests handled entirely in Salesforce.

In those cases adding a data platform straight away increases cost, time and complexity without improving the first result. The right question remains: which data is indispensable to the first use case, and where does it live today?

The first use case test

Before choosing a platform, fill in a table like this one.

Data needed	Where it lives today	Quality and ownership	Needed in real time?	Decision
Status of a case	Salesforce	High, owned by Service	Yes	Use Salesforce
Open orders	ERP	To be verified, owned by Operations	Yes	Integration or federated access
Purchase history	Snowflake	High, owned by the Data team	No	Consider zero-copy
Technical documentation	SharePoint	Variable, owned by Product	No	Point connection and access control
Customer margin	ERP / Finance	Sensitive, owned by Finance	Depends	Review access and whether it is genuinely needed

Three different answers come out of that table:

if all the data you need is in Salesforce, Data 360 is not a prerequisite for the first use case;
if a small amount of data sits in one clearly identified external system, a point integration or API access is simpler;
if data and customer identity are spread across CRM, ERP, e-commerce, portals and a data platform, then Data 360, or Snowflake plus Data 360, become options worth taking seriously.

The real point: identity and activation

Data 360 creates value when seeing data from different sources is not enough, and you need to know that it belongs to the same person or the same commercial relationship. In the CRM you have a contact, in the ERP an administrative customer, in the portal a registered user, in Snowflake the purchase history, in ticketing the open requests.

If those are not reliably connected, an AI agent, a salesperson or a service agent works from an incomplete picture - and none of them has any way of noticing.

Zero-copy federation allows access to Snowflake data without extracting, transforming and duplicating it: Salesforce describes it as providing secure access to Snowflake data while eliminating the need to extract, transform, and load data (Salesforce Developers). Bi-directional sharing between the two platforms has been generally available since April 2024.

The two platforms together

In larger organisations the better choice is often a clear division of responsibility.

Layer	Predominant platform	Responsibility
Enterprise data collection and modelling	Snowflake	Data from company systems, transformation, data quality, BI, data products
Customer identity and activation	Data 360	Identity, audiences, data actionable in Salesforce, CRM automation
Operational processes	Salesforce	Accounts, opportunities, cases, workflow, users, roles, actions
AI interface	Agentforce, ChatGPT, Claude, Slack, Teams	Experience, conversation, search, automation
Integration and control	APIs, MCP, middleware	Access, traceability, orchestration, data protection

This division avoids the two most frequent mistakes: using Data 360 as though it should replace the entire enterprise data platform, and keeping everything in Snowflake without making it usable where people take decisions.

Cost: what to actually compare

Do not put a Data 360 price list next to a Snowflake price list: that is an incomplete comparison. Compare the total cost over 24 months.

What drives Snowflake cost

compute capacity for queries, transformations and workloads;
storage, calculated on the monthly average of compressed data;
additional data services, sharing and transfers;
data engineering and pipeline maintenance;
governance, security and observability;
the cost of the teams that model and manage the data.

What drives Data 360 cost

the commercial model chosen: credits, profiles or dynamic consumption;
connections to external data sources;
ingestion, harmonisation, identity resolution and activation;
configuration of the data model, segments and Salesforce processes;
security, governance and access management;
Agentforce costs, if used, which are separate from the data layer.

The cost nobody quotes for

It is not the licence. It is the organisational work needed to decide which is the system of record for each piece of data, who is accountable for it, which data users and agents may use, how duplicates and conflicts are handled, which data is sensitive and who steps in when a value is wrong.

If those things are not clear, neither Data 360 nor Snowflake solves the problem. They make it visible.

The questions to put to your partner

Which problem does Data 360 solve that Snowflake, Salesforce or a point integration do not solve in our first use case?
Which data has to go into Data 360, and why?
Which data can stay in Snowflake or in the source systems?
Do we have to copy the data, or can we use zero-copy and data sharing?
Which identity or relationship between records do we have to unify?
Which Salesforce processes will actually use this data?
How much of the value arrives in the first release and how much only later?
Which consumption model is being proposed, and on what volumes?
How does the cost change if data, profiles, queries or AI usage double?
Who maintains the data model, matching, quality and permissions after go-live?
Which simpler solution did you consider?
What happens if we defer Data 360 by six months?

"It is the foundation for AI" is not an answer. A competent partner shows why it is needed in your use case, which data it enables and which operational result it produces. The other questions to ask before signing are in this guide.

A reasonable road

If you are starting from scratch, avoid buying a broad data platform simply because you are evaluating an AI agent. Start from a use case with a real, repetitive process, data already available, a business owner, verified access, an outcome metric and a bounded scope.

If it creates value, you will have concrete grounds to decide whether to extend the architecture with Data 360, Snowflake or both. If it does not, you will have learned the same thing for far less money, and without having turned an experiment into an enterprise platform.

Frequently asked

Do you need Data 360 for Agentforce to work?

Not always. If the agent works on data already present and reliable inside Salesforce, Data 360 may not be necessary for the first use case. It becomes relevant when the agent has to use external data, customer identities spread across several systems, or information that has to be made available inside CRM processes in a governed way.

Can we use Snowflake instead of Data 360?

Yes, if the main need is an enterprise data platform: centralising, transforming, analysing and governing data from several systems. No, if you need to activate that data quickly in segments, automation, customer journeys or Salesforce processes. In that case Snowflake is the data foundation and Data 360 is the activation layer.

Can we avoid duplicating the data?

In several scenarios, yes. Bi-directional sharing between Snowflake and the Salesforce data layer has been generally available since April 2024, and zero-copy federation allows access to Snowflake data without extracting, transforming and loading it. What still has to be defined is the data model, permissions, latency, supported regions and the actual use cases.

Does Data 360 cost less than Snowflake?

There is no general answer, and anyone giving you one is simplifying. Snowflake costs according to the compute, storage and services used; Data 360 has credit, profile or dynamic consumption models. The correct comparison is not between two price lists: it is between the total cost of achieving the same result, including integrations, governance, maintenance and people.

Data 360 or Snowflake? Differences, cost and when you actually need them