# Snowflake CoWork, BigQuery or Looker: Which AI Analyst Fits Your Team?

Canonical URL: https://www.martechdevs.com/blog/ai-analyst-for-your-warehouse

By [Jafrin Akter Alo](https://www.martechdevs.com/authors/jafrin-akter-alo)

Data Engineer and Co-founder, martechdevs

Published: 2026-01-05

Last updated: 2026-09-30

The best AI analyst for your team is the one that can answer a real business question using definitions you trust, within the permissions and budget you can maintain. Start there. A convincing chat demo is a poor reason to move your warehouse or rebuild your reporting.

Snowflake CoWork, BigQuery conversational analytics and Looker conversational analytics all let people ask questions about data. They sit at different points in the stack, and the work required to make their answers useful differs.

Looker is a business intelligence and modelling layer. It can sit above a warehouse, including BigQuery. Choosing Looker does not necessarily mean choosing a different warehouse. A company can have two of these products and still need one agreed route for answering a particular question.

For a founder, the decision is whether the tool reduces the time between a question and a defensible action. For a data lead, it is whether the team can explain, reproduce and maintain that answer after the launch demo ends.

## Start with the reporting model you already trust

Take the last question that needed an analyst. Perhaps paid acquisition increased, but qualified pipeline did not. Write down the data and definitions needed to answer it: spend, lead identity, qualification rules, opportunity creation, time windows and attribution boundaries.

Then locate the maintained model that joins those things correctly.

- If it lives in Snowflake semantic views and the team works in Snowflake, evaluate CoWork first.
- If it lives in curated BigQuery tables or views, evaluate a BigQuery data agent against those models.
- If it lives in LookML and trusted Explores, evaluate Looker conversational analytics before duplicating the definitions elsewhere.
- If nobody can identify a trusted model, fix one reporting question before comparing chat interfaces.

That last case is common in small companies. Connecting an AI interface to raw CRM, billing and event tables creates more ways to produce plausible answers. It does not decide whether a customer means a person, workspace or paying legal entity.

Our [AI-ready data guide](https://www.martechdevs.com/ai-ready-data) covers the foundations. For this decision, you need one narrower deliverable: a question that an analyst can already answer correctly, with an explicit definition and a reproducible query.

![A business decision leads to its maintained Snowflake, BigQuery or LookML model, a corresponding analyst pilot, and tests of answers, access and total cost before release.](https://www.martechdevs.com/assets/blog/ai-analyst-for-your-warehouse.svg)

## Snowflake CoWork: a fit when analysis spans Snowflake data and tools

[Snowflake CoWork](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork) is a business-facing agent application. Its underlying Cortex Agents can use structured-data analysis, search and configured tools. That makes it relevant when a question needs both warehouse metrics and supporting material, such as account notes or product documentation.

The structured-data path still needs business meaning. [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) uses semantic definitions of entities, dimensions, facts, metrics and relationships. Semantic views are the recommended path for new implementations; existing staged semantic models remain supported.

For a growth team, useful preparation might mean a semantic view with account-level recurring revenue, acquisition source and activation date. It should specify currency treatment, account grain and which subscriptions count. A verified example query can show the intended calculation. It does not prove every variation of the question will be answered correctly.

Choose this path when the Snowflake model is already valuable and the team can own its definitions. Be more cautious if the proposed project begins with a migration, a new document index and several custom tools before answering one question.

There is also an important permission boundary. [Cortex Search runs with owner's rights](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview). Do not assume a user's access to a search service reproduces that user's access to every underlying source row. Test each tool with the intended audience and design the searchable material accordingly.

The [Snowflake AI guide](https://www.martechdevs.com/blog/snowflake-ai) explains the components in more detail. The buying decision is simpler: can the existing Snowflake estate support a useful, bounded analyst without creating a second set of business definitions?

## BigQuery conversational analytics: a fit for curated BigQuery reporting

[BigQuery conversational analytics](https://docs.cloud.google.com/bigquery/docs/conversational-analytics) supports questions over selected data sources. A maintained data agent can add instructions, business terminology and verified queries. That is a different proposition from opening a direct conversation over tables without an agreed reporting context.

This is a practical option when a small team already has reliable BigQuery reporting views but does not have a substantial LookML estate to preserve.

For example, give an acquisition agent an account-level pipeline view and a daily campaign-spend view. Do not give it a raw contact table and hope it discovers that three contacts can belong to the same opportunity. Model that relationship first.

The [agent configuration](https://docs.cloud.google.com/bigquery/docs/create-data-agents) supports verified queries and a maximum-bytes-billed setting. Matched verified queries can be executed directly; other questions can use them as context. The setting helps constrain query processing, but it is not a universal cap on every charge involved in an AI workflow.

Use business instructions to define interpretation: which timezone closes the week, what counts as qualified pipeline and when the data is complete. Use data permissions to control access. A sentence saying “do not show sensitive fields” is not a substitute for a restricted data source.

The [BigQuery AI guide](https://www.martechdevs.com/blog/bigquery-ai) separates code assistance, data agents and AI functions. You do not need to deploy all three to answer marketing questions well.

## Looker conversational analytics: a fit when LookML is already the source of truth

[Looker conversational analytics](https://docs.cloud.google.com/looker/docs/conversational-analytics-overview) uses the LookML model. In the standard Explore analysis path, the AI selects fields, filters and query options; Looker builds the query through its modelling layer. A data agent can draw on up to five Explores.

That matters when your finance and growth teams already rely on LookML measures, joins and access rules. Reusing them can reduce the work of keeping a separate AI reporting model aligned.

It does not make a poorly designed Explore trustworthy. A misleading measure name, an incorrect join or an undocumented filter remains a problem when the question arrives through chat.

Check [instance setup and user permissions](https://docs.cloud.google.com/looker/docs/conversational-analytics-looker-setup) before promising access. Gemini and conversational analytics must be enabled, and users need the relevant permissions and agent access. Sharing an agent does not itself grant access to the model data it uses.

Looker is harder to justify as a new purchase solely for an AI chat feature if a five-person company has no maintained semantic model and only two reporting questions. It becomes more compelling when the model already supports recurring decisions across teams, and conversational access makes that investment easier to use.

Evaluate the exact experience you intend to deploy. Explore agents, dashboard agents and optional advanced analysis are not interchangeable feature sets. A feature shown in one demo should not become a requirement assumed to exist in every Looker workflow.

## Compare total cost with the current billing terms

AI pricing changes quickly. These are the public terms checked on 30 September 2026, not a negotiated quote or a forecast of your bill.

### Snowflake: separate AI consumption from warehouse work

[Snowflake's pricing documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/pricing) meters CoWork and Cortex Agents through AI consumption, with additional services and warehouse execution contributing their own costs. Standalone Cortex Analyst has a different message-based billing path; do not use that unit to estimate an agent workflow.

Measure a representative conversation, including follow-up questions and tool calls. A credit price alone cannot tell you the cost of answering “why did expansion fall?”

[CoWork resource budgets](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork/cowork-resource-budgets) support monitoring and configured enforcement actions, but enforcement can take up to eight hours after a threshold is exceeded. The documented low-latency option reduces that interval to two hours. Both are delayed controls: leave headroom and review the underlying compute and tool costs as well.

### BigQuery: separate the trial from ongoing economics

[Google's Data Cloud Agent pricing](https://cloud.google.com/products/data-agents/pricing) currently announces a free trial through 31 December 2026, followed by $3 per million input data tokens and $20 per million output data tokens. Query processing and other billable services need their own estimate.

A trial is useful for measuring consumption. It should not make the ongoing cost line in your business case zero. Record tokens and query work during the pilot, then price the same workload under the announced post-trial terms.

### Looker: instance allowances and a different promotional period

[Looker's pricing page](https://cloud.google.com/looker/pricing) lists conversational analytics token allowances shared across an instance. For Standard, the published monthly allowance is 60 million input tokens and 1.2 million output tokens. These are separate pools, not an allowance per user.

The page currently describes promotional access without quota limits or overage fees, subject to fair usage, with at least 90 days' advance notice before billing and quota enforcement. Do not apply BigQuery's December trial deadline to Looker.

After those fees apply, the listed overage rates are $3 per million input and $20 per million output tokens. A hypothetical Standard instance using 70 million input and 1.4 million output tokens would have $34 of token overage: $30 for input and $4 for output. That excludes platform, user, query and other applicable costs. It is an illustration of the formula, not a current promotional-period charge.

## Run a pilot that can expose a confident wrong answer

Use the same business questions across candidates. If one tool receives a clean model and another receives raw tables, you are comparing preparation as much as software. Record that preparation time because it is part of the decision.

Build a small reference pack containing the metric definition, a fixed data snapshot, expected outputs, permitted roles and the query or calculation behind each answer. An analyst or metric owner should approve the pack before the AI test.

### Include a deliberately awkward revenue example

This fictional fixture is small enough to inspect without trusting another model:

```csv
account_id,status,billing_interval,contract_amount_usd
A,paid,monthly,120
B,paid,monthly,80
C,paid,annual,1200
D,trial,monthly,50
```

For this test only, define monthly recurring revenue as the monthly equivalent of these paid recurring contracts. Annual contracts are divided by 12; trial accounts are excluded. There are no taxes, credits, discounts or usage charges in the fixture.

The expected answer is $300: $120 + $80 + $100. There are three paid accounts. Adding the contract amounts directly gives $1,400, which mixes billing intervals. Including the trial produces $350 after normalisation, which breaks the population rule.

Now ask “how much revenue did we collect?” The correct response is that the fixture cannot answer it. There are no payment records. An analyst that returns $300 has confused a recurring-revenue definition with cash collection.

In a real pilot, use your own signed-off metric definitions. This miniature example tests whether the tool respects an explicit contract; it is not a universal accounting definition.

### Use three separate test groups

Start with 20 original test cases: 12 answerable questions, four questions requiring clarification or missing evidence, and four access-boundary checks. These counts are a manageable starting design, not a statistical benchmark.

The answerable group should include comparisons, segments, date boundaries and a known join trap. Ask the same question in different wording. Include a follow-up that changes the time period, because conversational context can carry an old filter into a new answer.

The clarification group should include undefined “revenue,” an incomplete reporting day, a request for causation from observational data and a question whose required source is absent.

The access group should use ordinary staff roles. Include a question that a privileged analyst can answer but the intended marketing user cannot. A refusal is the expected result there, not a failed feature demo.

Do not blend these groups into one flattering accuracy number. Eleven correct numerical answers out of twelve cannot compensate for exposing restricted customer information. A polished explanation cannot compensate for silently choosing an undefined metric.

### Score evidence and effort as well as the final number

For each case, record the first response, follow-ups required, final result, query or source evidence, time to acceptance and reason for rejection. Save the model and instruction versions used for the test.

Ask a person who did not configure the agent to review a sample. Can they tell which data supported the conclusion? Can they reproduce the total? Can they distinguish a documented fact from the agent's suggested explanation?

Choose acceptance rules before seeing the results. Permission failures should block release. A recurring numerical failure should narrow the scope until the underlying model or behaviour is fixed. A useful system can launch with a smaller question set; it should not launch with undisclosed uncertainty about its core metric.

## Make the founder's business case explicit

The benefit is not the number of questions asked. It is the time saved on accepted work and the decisions that become possible sooner.

Consider a fictional team handling 24 recurring questions a month. Manual preparation and review takes 30 minutes each. With an agent, accepted answers still need 10 minutes each, and maintaining the model and test pack takes four hours a month.

Manual work totals 12 hours. The new workflow takes four hours of answer review plus four hours of maintenance, leaving four hours saved. Counting only the faster answer would claim eight hours saved and ignore half the ongoing work.

That can still be worthwhile. It may not justify a platform migration. Add setup time, subscriptions, compute and the cost of investigating failures before choosing.

Also separate capacity from cash. Four hours of freed analyst time becomes a cash saving only if spending actually falls. Otherwise, describe what the team will do with that capacity: investigate churn, improve qualification or repair tracking.

## A release checklist for a small team

- [ ] One business owner approves the first question set and its definitions.
- [ ] The chosen tool reuses a maintained model with explicit entity grain and freshness.
- [ ] Reference answers include queries or calculations that can be reproduced.
- [ ] Missing evidence and ambiguous questions lead to clarification, not invented certainty.
- [ ] Ordinary-user tests cover every connected analysis or retrieval tool.
- [ ] Cost estimates include maintenance, query work and post-promotion terms.
- [ ] Someone owns failed answers, model changes and a repeatable regression pack.
- [ ] The initial release has a bounded audience and a clear route to a human analyst.

If the comparison keeps returning to inconsistent customer IDs, duplicate opportunity rows or competing revenue definitions, the next project is the reporting foundation. Our [data warehouse and CDP work](https://www.martechdevs.com/data-warehouse-cdp) focuses on making those models reliable enough for reporting, activation and AI. The best outcome is a smaller set of questions your team can act on with confidence.

## Frequently asked questions

### What is Snowflake CoWork?

It is Snowflake's business-facing agent application for working with structured and unstructured information through configured analysis, search and other tools. Useful answers still depend on the underlying definitions, source quality and tool permissions.

### Is Looker an alternative to BigQuery?

They serve different layers. BigQuery is a data platform; Looker provides a modelling and business intelligence layer that can query a warehouse. If you already use both, compare where the trusted metric is maintained before choosing a conversational interface.

### Which AI analyst should a company with fewer than 50 people choose?

Headcount alone is a weak filter. Start with the model your team can maintain, the questions it needs answered and the permissions it must enforce. A small company with mature LookML has different needs from one with curated BigQuery views and no BI modelling layer.

### Do verified queries make AI answers accurate?

They provide useful reference logic and, in supported matched-query paths, an approved query to execute. They do not validate every new question, join, filter or explanation. Keep a regression pack that includes variations and questions the available data cannot answer.

### Should we migrate warehouses to get a better AI analyst?

Usually, first test a bounded workflow on the stack you already operate. A migration needs a wider case involving data capabilities, operating cost and team needs. A stronger chat demo alone does not account for rebuilding models, permissions, pipelines and reporting trust.

## Sources

1. [Snowflake CoWork overview](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork)

2. [Snowflake Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst)

3. [Cortex Search overview and access model](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview)

4. [BigQuery conversational analytics](https://docs.cloud.google.com/bigquery/docs/conversational-analytics)

5. [Create BigQuery data agents](https://docs.cloud.google.com/bigquery/docs/create-data-agents)

6. [Looker conversational analytics overview](https://docs.cloud.google.com/looker/docs/conversational-analytics-overview)

7. [Set up conversational analytics in Looker](https://docs.cloud.google.com/looker/docs/conversational-analytics-looker-setup)

8. [Snowflake Cortex AI pricing](https://docs.snowflake.com/en/user-guide/snowflake-cortex/pricing)

9. [Snowflake CoWork resource budgets](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork/cowork-resource-budgets)

10. [Google Data Cloud Agent pricing](https://cloud.google.com/products/data-agents/pricing)

11. [Looker pricing](https://cloud.google.com/looker/pricing)