Snowflake vs Databricks vs BigQuery for AI

The first time I had to pick a “forever” data platform, I did what everyone does: I opened a spreadsheet, wrote Snowflake, Databricks, and BigQuery across the top, and promised myself I’d be rational. Then the first bill arrived, my dashboard users multiplied overnight, and my ML teammate asked, “Can we do vector search here?” That’s when I learned the comparison isn’t abstract—it’s a set of small, operational moments: cold starts before a CEO demo, a runaway credit burn at 2 a.m., and a notebook that makes prototyping feel like cheating. In this post I’m comparing Snowflake vs. Databricks vs. BigQuery through an AI lens, but with the unglamorous details included: query execution patterns, cost control habits, and how each platform feels on a random Tuesday when you just want the pipeline to stop failing.

Performance Characteristics: what I notice at 9 a.m.

My first question: does it feel fast before I optimize anything?

At 9 a.m., I don’t start by tuning clusters or rewriting SQL. I start with a simple test: can I run a few common queries and get answers fast enough to keep moving? For AI work, “fast” is not just about one big benchmark. It’s about how quickly I can explore data, refresh BI, and build features without babysitting the platform.

Real-world performance: BI vs ad-hoc SQL vs ML feature jobs

In practice, I see three different speed needs:

• BI dashboards: lots of repeated queries, predictable patterns, and many users.
• Ad-hoc SQL: messy joins, filters, and “what if” questions that change every minute.
• ML feature jobs: heavy scans, wide tables, and scheduled pipelines that must finish on time.

Snowflake often feels smooth for BI and repeatable analytics because compute can be separated and scaled. BigQuery feels quick for ad-hoc exploration when my data is already in place and I’m scanning large columns. Databricks shines when feature jobs are tied to Spark pipelines and I want the same engine for ETL plus AI.

Query execution patterns in plain English

• BigQuery (Dremel): breaks a query into many pieces and runs them in parallel across Google’s infrastructure.
• Snowflake (micro-partitions): stores data in small chunks and tries to skip chunks that don’t match my filters.
• Databricks (Photon): speeds up Spark SQL with a faster execution engine, especially for common SQL patterns.

Cold start times: the awkward wait

The first query of the day can be weird. Snowflake may need a warehouse to resume. Databricks may need a cluster to spin up. BigQuery usually feels “already on,” but I still notice delays from caching, slot availability, or large metadata planning.

Concurrent query handling: when everyone hits refresh

When the whole company refreshes dashboards at once, I watch for queueing. Snowflake’s separate warehouses help isolate teams. BigQuery handles bursts well, but heavy usage can change timings. Databricks depends on cluster sizing and job concurrency settings, so I feel it most when many notebooks and SQL endpoints compete.

Cost Analysis: the bill that made me change habits

Pricing Model Breakdown: why each platform’s meter changes behavior

When I compared Snowflake vs Databricks vs BigQuery for AI, the biggest surprise was how pricing shapes day-to-day choices. Each platform measures “work” differently, so teams often optimize what the meter tracks, not what the business needs.

• BigQuery: mostly pay per data processed (on-demand) or flat-rate slots. This pushes me to reduce scanned bytes and partition tables.
• Snowflake: credits for compute (warehouses) plus storage. This encourages right-sizing warehouses and controlling concurrency.
• Databricks: DBUs + cloud compute. This nudges me to tune clusters, job types, and runtime choices.

Consumption-based pricing vs credits vs DBUs: how teams optimize the wrong thing

I’ve seen AI projects chase the cheapest “unit” while ignoring the real driver. In BigQuery, people obsess over bytes scanned and forget that repeated experiments can still add up. In Snowflake, teams focus on credit burn but leave too many warehouses available “just in case.” In Databricks, it’s easy to tune DBUs while the underlying cloud VM cost quietly dominates.

Platform	Common accidental optimization	Better focus
BigQuery	Only reducing scan size	Reuse results, materialize features
Snowflake	Picking smallest warehouse	Auto-suspend + workload isolation
Databricks	Chasing lower DBUs	Cluster uptime + job scheduling

Analyst-Centric Cost Control: small settings that save big money

The best savings came from boring controls: query limits, separate dev schemas, and default timeouts. I also started tagging workloads (team, project, environment) so I could explain AI spend without guessing.

My “oops” moment: leaving compute running

I once left a Snowflake warehouse running overnight after an AI feature test. The bill was my teacher.

After that, I set Auto-suspend aggressively (like 60–120 seconds) and required manual resume for large warehouses.

A budgeting rule of thumb

I now separate exploration from production compute: cheap, short-lived resources for experiments, and stable, monitored resources for pipelines and AI training jobs.

SQL Development Experience: how it feels to build daily

ANSI SQL support: where things feel standard—and where they don’t

When I’m building AI-ready datasets, I want SQL to feel boring—in a good way. Snowflake is closest to “standard warehouse SQL” day to day, so joins, window functions, and CTE-heavy modeling feel predictable. BigQuery is also friendly, but I still notice its Google-flavored edges (like how it treats arrays/structs and some function names). Databricks (Spark SQL) works well for most analytics SQL, yet I sometimes hit small differences in function behavior or type casting that slow me down when I’m porting code between systems.

BI Tool Connectivity: the ‘can my dashboard tool just work?’ checklist

For BI, I care about drivers, permissions, and whether my semantic layer stays stable. In practice, all three connect to common tools, but the “it just works” feeling varies by setup and governance.

• Snowflake: strong ODBC/JDBC support and common BI defaults.
• BigQuery: smooth with Google ecosystem; service accounts are powerful but easy to misconfigure.
• Databricks: great when you standardize on SQL Warehouses, but clusters vs warehouses can confuse new teams.

Spark SQL integration vs warehouse-native SQL: my debugging tempo changes

In Databricks, I feel the Spark engine under my feet. That’s great for AI pipelines that mix SQL with notebooks, Python, and ML, but debugging can involve partitions, shuffles, and job stages. In Snowflake and BigQuery, the experience is more warehouse-native: I’m mostly thinking about query shape, pruning, and cost, not distributed execution details.

Query cache mechanism and the illusion of “my query is optimized”

All three can make repeated queries look fast thanks to caching. I’ve learned to sanity-check by changing a filter, adding a comment, or testing on fresh data. Otherwise, I might celebrate an “optimized” AI feature query that was simply served from cache.

A small tangent: the day a UDF saved my weekend

I once had messy event strings that broke my training labels. A quick UDF turned chaos into clean columns:

CREATE FUNCTION norm(x STRING) AS (LOWER(TRIM(x)));

That tiny function kept my SQL readable and my weekend intact.

Data Transformation Capabilities: ELT, lakehouse, and the messy middle

When I compare Snowflake vs Databricks vs BigQuery for AI, I start with a simple question: where does each platform want my transformations to live? That choice shapes how fast I can clean, join, and prepare features for AI models when requirements change mid-week.

Where transformations “belong” (and why it matters)

• Snowflake pushes me toward SQL-first ELT inside the warehouse, with strong governance and predictable pipelines.
• Databricks wants transformations closer to the lakehouse, mixing SQL and notebooks so I can move from raw files to feature tables in one place.
• BigQuery keeps transformations in the managed warehouse, and I often pair it with dbt-style workflows for repeatable AI-ready models.

Decoupled storage-compute: the “data doubled overnight” test

All three separate storage from compute in practice, which matters when AI logs or event data suddenly spikes. I can scale compute for heavy transforms without copying data. In Snowflake, I spin up a bigger warehouse; in Databricks, I resize clusters or use job clusters; in BigQuery, I lean on on-demand or reserved slots.

Delta Lake optimizations: the unsexy performance secret

In Databricks, Delta Lake is where I feel the “messy middle” get cleaner. File layout work like OPTIMIZE and Z-ordering isn’t glamorous, but it keeps joins and time-range reads fast when I’m building AI training sets from huge tables.

Micro-partitioning: why I sometimes stop thinking about indexes

Snowflake’s micro-partitioning often means I spend less time designing indexes and more time shipping features. BigQuery’s columnar storage and partitioning/clustering give a similar “just query it” feel, though I still plan partitions for cost control.

Friday ingestion scenario

“We just acquired a company—can we ingest their data by Friday?”

If their data lands as files, Databricks lakehouse ingestion can be quickest. If it’s clean SaaS exports, Snowflake or BigQuery ELT can be faster to standardize with SQL. Either way, I prioritize a thin landing model first, then iterate transformations once the AI team can query something real.

Machine Learning Capabilities: from SQL to models (and back)

What I can do without leaving the platform

When I compare Snowflake vs Databricks vs BigQuery for AI, I start with one question: how far can I get without switching tools? All three let me prep data, train models, and operationalize results close to where the data lives. The difference is how “SQL-first” or “notebook-first” the experience feels, and how quickly I can move from an idea to a working model.

BigQuery ML: when “ML in SQL” is the fastest path

BigQuery ML is the cleanest example of AI work staying inside SQL. I can create a model, evaluate it, and run predictions using familiar statements, which is great for teams that already live in queries.

CREATE MODEL my_model OPTIONS(model_type='logistic_reg') AS SELECT * FROM my_table;

Snowpark models: when Snowflake stopped being “just a warehouse”

Snowflake changed for me once I started using Snowpark. I can write Python, use common libraries, and keep governance and access controls consistent with the rest of the platform. For feature engineering and batch scoring, it feels like I’m extending SQL workflows rather than replacing them.

Databricks: notebook-native ML and tight iteration loops

Databricks is where I go when experimentation speed matters most. The notebook flow, distributed training, and built-in tracking (often with MLflow patterns) make it easy to iterate on features, algorithms, and evaluation in one place, especially for larger or more custom model work.

Vector search: the AI feature everyone asks about now

For retrieval-augmented generation and semantic search, I look for vector storage + indexing + fast similarity queries. Each platform now has a story here, but the practical question is: can I generate embeddings, store them, and query them with low friction? If that loop is smooth, building AI search becomes much simpler.

A practical workflow: build, validate, iterate, deploy

• Build training data: BigQuery and Snowflake shine with SQL transforms; Databricks shines with notebooks + Spark.
• Validate: BigQuery ML gives quick metrics in SQL; Databricks excels for deeper analysis.
• Iterate: Databricks is fastest for rapid experiments; Snowpark is strong when data stays in Snowflake.
• Deploy: BigQuery ML and Snowflake support in-platform scoring; Databricks fits flexible pipelines and custom serving.

Scalability Comparison: the ‘everyone’s using it now’ moment

What changes when your “pilot” becomes mission-critical

In an AI pilot, I can live with slow jobs, manual retries, and a few “don’t run this at 9am” rules. The moment the model feeds a product feature or a daily decision, scalability becomes less about raw speed and more about predictability: stable runtimes, stable costs, and fewer surprises when more teams pile in.

Data volume response: who stays steady as datasets and users grow

At scale, I watch two things: concurrency (many users) and throughput (big workloads). Snowflake stays steady by separating storage and compute and letting me add more virtual warehouses for more users. BigQuery handles huge scans well and shines when I want many people querying the same data with minimal ops. Databricks is strong when AI pipelines are heavy on Spark, streaming, and feature engineering, but I usually feel scaling more directly because clusters are a real thing I manage.

Auto-scale modes: invisible vs a knob

• BigQuery: scaling is mostly invisible. I focus on SQL and cost controls, not cluster sizing.
• Snowflake: scaling is semi-invisible. I pick warehouse sizes and can enable multi-cluster for bursts.
• Databricks: scaling is a knob. Autoscaling helps, but I still think in terms of cluster policies, node types, and job tuning.

Snowflake vs BigQuery: a quick, blunt decision tree I’ve used

1. If my team wants least ops for AI analytics at scale → BigQuery.
2. If I need strong workload isolation and predictable concurrency for many teams → Snowflake.
3. If the “AI” part is mostly Spark/ML pipelines and lakehouse patterns → I step back and consider Databricks first.

My gut-check: pick the platform your team will actually operate well when everyone is using it now.

Conclusion: the platform is a habit, not a trophy

After comparing Snowflake vs Databricks vs BigQuery for AI, I keep coming back to one idea: performance, cost, workflow, and ML are not separate checkboxes. They compound. A faster query changes how often people explore. A clearer workflow reduces rework. A predictable cost model changes how bold your team feels. And better ML integration changes what you even try to build. So the “best” platform is usually the one your team can repeat daily without friction.

If your team is BI-first, you probably win by choosing the platform that makes dashboards and ad-hoc SQL simple, stable, and easy to govern. If you’re ML-first, you’ll care more about notebooks, feature work, experiment tracking, and how quickly you can move from data prep to training. If you’re mixed, the real test is whether analysts and engineers can share the same definitions, the same tables, and the same trust—without building parallel systems that drift apart.

Here’s my wild card question: imagine your future audit. Not a scary one—just the normal “why did this model make this decision?” review. Who can explain the data lineage fastest? Who can show where the data came from, what changed, and who approved it? In AI projects, that answer matters as much as raw speed.

In practice, the platform that helps you explain your data is often the platform that helps you scale your AI.

My personal bias (confession): I value boring reliability over cleverness—most days. I like tools that behave the same on Monday morning as they did on Friday night, with fewer surprises in permissions, pipelines, and costs.

My call to action is simple: run a one-week bake-off using the same workload and the same rules. Use identical datasets, identical queries, one training job, and one governance task (like lineage or access review). Track runtime, spend, developer time, and how confident you feel in the results. The winner won’t be a trophy—it’ll be the habit your team can keep.

TL;DR: If you want serverless, high-concurrency analytics with minimal tuning, BigQuery often feels frictionless (Dremel + automatic scaling slots). If you want BI-friendly warehousing with strong ANSI SQL, micro-partitions, and predictable “virtual warehouse sizing,” Snowflake is hard to beat. If you’re building an AI/ML-heavy lakehouse with notebooks, streaming, and Spark SQL integration, Databricks (Photon + Delta Lake optimizations) usually wins—just budget time for cluster behavior and DBUs.