Over the past few years, data platforms have moved from “nice to have” to core infrastructure for how enterprises compete in the AI age. More than 90% of enterprises now use some form of data warehousing, and cloud-based deployments already account for the majority of those environments.
However, choosing the “right” data platform is becoming increasingly complex. Snowflake, BigQuery, and Databricks all market themselves as end-to-end data and AI platforms and offer comparable capabilities (compute separation, SQL modelling, streaming, and GenAI tooling).
Despite the overlap, the choice matters. The wrong platform can inflate costs and slow down AI adoption.
For SmarterX, migrating from Snowflake to BigQuery cut data warehousing costs by 50% and helped accelerate model building and simplify their AI-enabled data platform.
Other enterprises have seen six-figure annual savings from moving workloads between BigQuery and Snowflake or consolidating onto Databricks when their use cases demanded tighter data–ML integration.
This guide compares Snowflake, BigQuery, and Databricks on the dimensions that matter most at scale:
- Fit with your existing cloud ecosystem
- SQL and data modelling capabilities
- AI/ML toolchains
- Performance and scalability considerations
- Total cost of ownership
Snowflake: Multi-cloud AI data warehouse for governed, self-service analytics
Snowflake is an AI data cloud platform that runs natively across AWS, Azure, and Google Cloud.
It provides elastic storage with compute separation, governed data sharing, lakehouse-style analytics, and built-in AI services like Cortex, vector search, and Native Apps to help data engineering teams ship data products and AI applications without managing the infrastructure underneath.
At the time of writing, Snowflake enables real-time personalization, financial risk and fraud analytics, operational reporting, and AI/LLM workloads for over 12,000 customers, with over 680 organizations generating more than $1M in annual revenue.
Notable enterprise use cases
- Capital One runs real-time analytics for thousands of analysts on Snowflake
- Adobe uses the platform as part of a composable CDP for large-scale customer experience activation
- S&P Global deploys Snowflake to unify vast financial and alternative datasets in a governed cloud environment for real-time analytics and data products for institutional customers.
BigQuery: Serverless GCP-native warehouse for petabyte-scale analytics and AI
BigQuery is Google Cloud’s fully managed, serverless data and AI warehouse that now acts as an autonomous “data-to-AI” platform.
Because BigQuery is tightly integrated with the broader Google Cloud ecosystem, including Vertex AI, Looker, Dataflow, and Pub/Sub, it is widely used for streaming analytics, ML feature pipelines, marketing and advertising analytics, and predictive modeling.
BigQuery’s storage layer supports structured, semi-structured, and unstructured data through BigLake, allowing enterprises to unify warehouse and lake workloads with a single governance model.
Notable enterprise use cases
- For HSBC, BigQuery is a governed analytics backbone for financial crime, risk, and AML monitoring across high-volume multi-jurisdictional datasets.
- Spotify runs global product and listener analytics on BigQuery to contextualize engagement, optimize recommendations, and support data-informed product decisions at streaming scale.
- The Home Depot uses BigQuery as its enterprise retail data warehouse to power inventory and supply-chain optimisation, operational dashboards, and customer experience analytics.
Databricks: Lakehouse platform unifying data engineering, BI, and ML/GenAI
Databricks is a cloud-native Data Intelligence Platform built on a lakehouse architecture that unifies data engineering, real-time streaming, BI, and machine learning/GenAI on open formats such as Delta Lake.
Its capabilities span high-performance ETL/ELT pipelines, real-time analytics, collaborative notebooks in SQL/Python/R/Scala, and centralized governance through Unity Catalog.
Enterprise organizations rely on Databricks to modernize legacy warehouses, build full-funnel marketing attribution, and operationalize LLM and agent-based applications on top of their unified data estate.
Notable enterprise use cases
- JPMorgan Chase uses Databricks to standardize and govern massive trading, risk, and payments datasets as a unified AI foundation for hundreds of production use cases.
- General Motors runs a Databricks-based “data factory” and lakehouse to process fleet telemetry and enterprise data for predictive maintenance, safety analytics, and GenAI-powered operational insights.
- Comcast builds on Databricks to power security and advertising analytics, from DataBee’s security data fabric and SEC-aligned cyber reporting to predictive ad-optimization tools in Comcast Advertising.
Comparing data platforms is not straightforward because performance and TCO depend on how well the data platform fits into your existing infrastructure, how experienced data engineers are with each tool, and the type of queries you are processing.
This selection guide will cover key considerations that can drive latency, costs, or time to market for each solution, but we recommend running a more targeted assessment once you clearly define the use case and talent available.
Cloud ecosystem integration
Snowflake
Snowflake deploys natively on AWS. It stores data in S3, uses KMS for encryption and IAM for auth, and integrates tightly with Lambda, SageMaker, Amazon PrivateLink, and other managed services.
Teams building on Amazon’s infrastructure will be able to use Snowflake out of the box for low-latency data apps and machine learning. However, to avoid security gaps and surprise data-transfer costs, engineers should carefully examine bucket policies, IAM role chaining, and VPC peering.
On Microsoft Azure, Snowflake runs on top of Azure Blob Storage/ADLS Gen2 and Entra ID, integrates with Power BI and Azure ML. For secure traffic isolation, the platform taps into Private Link and VNets.
Despite otherwise frictionless implementations, engineers have to be careful when role-mapping between Entra and Snowflake roles. To avoid access and compliance vulnerabilities, teams should have a regular process for translating Azure Entra ID users and groups into Snowflake and keeping mappings in sync.
On Google Cloud, Snowflake is supported by GCS, Cloud KMS, and Cloud IAM, exposes secure connectivity through Private Service Connect, and plugs into Looker, BigQuery (via external tables/connectors), and Vertex AI.
While there are no functional limitations to running Snowflake on Google Cloud, due to considerable feature overlap between Snowflake and BigQuery, teams need to create policies for dual-governance between the two and watch for egress charges when moving data between Snowflake and other GCP services across regions or projects.
BigQuery
BigQuery is fundamentally a GCP-native data and AI warehouse.
For engineering teams already committed to GCP, there’s no tighter fit. With BigQuery, data engineers who already host their infrastructure with Google get first-class integrations with Vertex AI directly on BigQuery tables, Gemini for SQL generation and optimization, unified observability, billing, and a single IAM/governance model that reduces glue code and custom plumbing.
On the other hand, for multi-cloud architectures, engineering overhead gets asymmetrical.
Teams that keep substantial workloads in AWS or Azure have to accept added complexity around networking, data movement, and egress, or rely on Omni and federated access patterns that don’t have feature parity or cost characteristics identical to running BigQuery natively in GCP.
If you are on AWS, Snowflake is comparable in price to BigQuery and has lots of the same features. You will not like the cloud egress/ingress of cross-cloud. Plus, you can share between clouds in Snowflake. I’m a huge advocate of BigQuery in GCP, but cross cloud will be more expensive.
A Reddit user on the challenges of using BigQuery on AWS
Databricks
Databricks has well-fleshed out integrations with all key cloud vendors.
On AWS, it runs on top of S3, EC2, and EKS with tight integrations into IAM, KMS, PrivateLink, Glue, and services like Kinesis, Redshift, and SageMaker.
On Azure, Databricks is delivered as a first-party service (Azure Databricks) that sits on ADLS Gen2, Azure Kubernetes Service, and Entra ID and enables RBAC, native integration with Synapse/Power BI/Event Hubs, and managed VNet injection.
Keep in mind that, unlike other data platforms, Databricks runs VN-injected workplaces inside the client’s private network, which puts the cloud team under pressure to “carve out” enough private address space for all the Databricks clusters the company will ever need.
If data engineers underestimate that capacity, new clusters won’t start, and they may have to rebuild the entire network.
On Google Cloud, Databricks uses GCS, GCE/GKE, Cloud IAM, and VPC Service Controls. The platform integrates with all GCP-managed services – Pub/Sub, BigQuery, and Vertex AI, and others, so teams can run Spark/Delta workloads alongside GCP-native analytics and LLMs.
Like Snowflake, the primary friction point for deploying Databricks on GCP is the way it clashes with BigQuery. Teams that store core data as Delta tables on GCS will see excellent performance on Databricks, but considerably higher latency for GCS tools that need access to the table due to the need for third-party connectors that stitch two systems together.
Also keep in mind that Databricks on GCP might not have feature parity with most AWS/Azure regions, as it’s quite a new product.
It also costs more as it has GKE running under the hood all the time instead of ephemeral VMs like Azure.
Reddit comments on the pain points of implementing Databricks on the Google Cloud platform
SQL and data modeling
All three data platforms support SQL, complex joins, window functions, common table expressions (CTEs), and semi-structured data, but their SQL layers are optimized for different types of applications.
Snowflake
Out of the three vendors, Snowflake’s data modeling capabilities are the easiest to navigate for non-technical teams.
The platform allows most of the important logic for metrics and reports to live in clear, reusable queries.
Analysts can define core concepts like “active customer,” “net revenue,” or “churned account” directly in SQL models and reuse those definitions across dashboards and teams to make sure that sales, finance, and operations teams see consistent numbers.
Besides, time travel and zero-copy cloning allow data engineering teams to safely change models, compare “before vs after,” and quickly roll a model back without breaking the dashboards it supports.
BigQuery
BigQuery’s SQL and data modelling are designed for “big data first” scenarios where engineering teams have billions of rows to examine under minimal latency.
In these scenarios, BigQuery’s Standard SQL allows teams to explore clickstreams, events, and logs in large columnar datasets without forcing them into a rigid warehouse schema.
Then, with partitioning, clustering, and materialized views capabilities, data engineers can shape large tables into dashboards that respond quickly to common business questions, such as identifying the most active app users over a set period of time.
On top of that, built-in ML and geospatial functions help express advanced data analytics use cases like propensity scoring, location analysis, or anomaly detection directly in SQL instead of spinning up separate ML infrastructure.
Databricks
Databricks’ data modeling capabilities deliver the most value when analytics is combined with heavy data engineering and ML.
The platform lets teams build one set of curated tables that feeds dashboards, experiments, and models at the same time. Engineers can shape raw feeds into bronze/silver/gold layers once, then reuse these customer, transaction, or sensor models both in BI and in ML features for churn prediction, pricing, or predictive maintenance.
Besides, since Databricks is built to handle streaming and batch processing in the same model, operations and product teams can move use cases from monthly reports to near-real-time alerts without redesigning the model from scratch.
However, this universality comes with added maintenance overhead since engineering teams have to autonomously maintain clusters, jobs, and storage.
All of those, if mismanaged, drive TCO and create a higher risk of pipeline changes causing ripple effects on downstream dashboards and ML models.
| - Very polished, warehouse-centric SQL - Easy for BI teams to adopt with minimal engineering support. | Classic layered warehouse mostly expressed in SQL, with semi-structured data handled via VARIANT. | - Great for building a single, stable source of truth - Metric definitions live in shared SQL models - Time travel and cloning make changes and QA low-risk; fits well with dbt and similar tools. | - Less “native” for streaming and real-time use cases - Complex ML/feature engineering usually pushed to external tools - Can feel opinionated if you want highly custom dataflow logic outside SQL. | |
| Powerful, expressive SQL tuned for very large analytical queries (arrays, nested data, advanced analytics functions). | - Large, often wide tables with partitioning, clustering, and materialized views - Mixes warehouse-style models with exploratory, schema-on-read patterns | - Excellent for big data analytics (product, marketing, risk) - Event/log data can be queried without heavy pre-modelling - Built-in ML and analytics in SQL shorten the path from idea to insight. | - Easy to accumulate many ad-hoc datasets and “competing truths” if the modelling discipline is weak - Some semantic modelling shifts into the Looker/BI layer - External users may need guidance to avoid overly complex or costly queries. | |
| - Solid ANSI SQL on top of Delta - Improving UX for analysts, but historically more engineering-centric than warehouse-centric. | - Medallion (bronze/silver/gold) layers in Delta tables shared between BI, data engineering, and ML - Logic is often split between SQL and notebooks/pipelines. | - Best fit when you want one set of curated tables powering both dashboards and ML/AI - Strong for mixing batch and streaming; business logic can flow consistently from reports into model features and real-time decisions | - Requires more engineering maturity to keep models governed and comprehensible to pure BI users - Metrics logic can be fragmented between SQL and Spark code - Pure “SQL-only” teams may perceive more friction than in Snowflake/BigQuery. |
AI and ML: How each platform supports the full ML lifecycle
Snowflake
Snowflake is an excellent fit for engineering teams that want to keep models “close to the data” and add AI features to existing analytics products rather than build a heavyweight ML platform from scratch.
With Snowflake Cortex, teams can call curated foundation models (text, search, embeddings, and some task-specific models) directly on governed data, use vector search to power retrieval-augmented generation, and expose data through SQL.
This setup helps deploy chat-style assistants, semantic search, and summarisation on top of trusted tables without moving data out of the platform.
Snowpark and Native Apps let experienced ML engineers package custom logic, orchestrate GenAI workflows, or integrate external models while still benefiting from Snowflake’s security and data-sharing.
However, for highly customised GenAI pilots that require large-scale fine-tuning, complex multi-agent systems, or latency-sensitive inference, the platform is limited to the data backbone. Model training, orchestration, and serving are not advanced enough to build a full-spectrum GenAI platform, and engineering teams have to use third-party platforms to support these capabilities.
BigQuery
BigQuery is a reliable choice if an engineering team already has a large dataset in GCP and wants to layer intelligence on top with minimal friction.
With Gemini in BigQuery, analysts and analytics engineers can generate and optimise SQL, document pipelines, and even prototype simple agents directly in the BigQuery UI.
Combined with BigQuery ML and tight integration into Vertex AI (for custom models, fine-tuning, and online prediction) plus native vector search capabilities, the platform creates a direct path from warehouse tables to RAG systems, scoring APIs, and an AI-enhanced dashboard within the same security and governance perimeter.
It’s worth noting that BigQuery itself is not a full GenAI runtime. Sophisticated multi-agent systems, low-latency serving, or very customised fine-tuning are typically implemented in Vertex AI or other GCP services, with BigQuery as the analytics foundation and feature store.
Databricks
Among the three vendors, Databricks has the most complete AI and machine learning toolset and allows teams to fully manage data prep, model training, and LLM or agent orchestration in a single ecosystem.
The platform comes with a powerful roster of ML-facing services.
- MLflow for native experiment tracking, logging runs, comparing models, and keeping a clear model lineage.
- Delta Lake, a transactional lakehouse storage that turns raw data into curated, feature-ready tables (bronze/silver/gold) shared across BI, ML, and GenAI.
- Databricks AutoML, an automated training service that generates baseline models and starter notebooks for tabular problems, speeds up proof-of-concept design.
- Feature Store, a central service for defining, versioning, and reusing ML features across different models and teams
- Vector Search, a built-in vector index and retrieval service that stores embeddings alongside Delta data to power RAG, semantic search, and domain copilots.
Databricks’ native support for vector search, retrieval pipelines, and tools for building agents gives data and ML teams the flexibility to design complex workflows that span batch, streaming, and real-time decisions.
On the other hand, non-technical teams might find the learning curve of the platform too steep and will require dedicated engineering assistants to manage lightweight genAI projects like an internal RAG-augmented chatbot.
Performance and scalability
Snowflake
Snowflake’s scalability model for enterprises is anchored in the multi-cluster virtual warehouses and services layer.
On the platform, compute is provisioned in straightforward “sizes” that can scale up or down without downtime, and are easily segmented by domain or workload.
This helps enterprise companies make sure that domain-specific workloads, like a month-end close in finance, are not competing with data science experiments or heavy ELT.
Automatic micro-partitioning, query optimization, and extensive result/data caching support BI and transformation workloads with no need for continual tuning. Auto-suspend/auto-resume and resource monitors also provide pragmatic controls over spend as adoption grows.
For teams with mission-critical data pipelines, however, Snowflake might not be the best option.
Although the platform supports streaming via Snowpipe and related services, real-time computing is not its core strength, so it may be better to limit adoption to high-throughput batch processing and interactive analytics.
BigQuery
BigQuery deploys a serverless, storage–compute–decoupled architecture, optimized for high-concurrency analytics over very large datasets.
The platform storage sits in a durable, shared layer while a large pool of managed compute is dynamically allocated per query, allowing thousands of users to run complex analytics on shared data without teams having to provision, scale, or maintain dedicated clusters.
Therefore, enterprise teams can shift their focus away from query sizing towards table design and query shape.
The flexibility in choosing how to partition tables, cluster data by filter keys, and expose pre-aggregated materialized views helps engineers ensure that business queries only scan a small, targeted portion of the dataset for a faster, more predictable performance.
At the same time, the platform’s scalability model introduces its risks and necessary mitigation strategies.
Because pricing and performance are both driven by bytes scanned, poorly modelled wide tables or unbounded ad-hoc queries can become both simultaneously slow and expensive to maintain. To prevent this, central data teams have to impose strict schema design, query patterns, and guardrails.
Databricks
Out of the three vendors, Databricks offers the most flexibility in performance and latency fine-tuning.
Teams can tweak the performance of everything from small interactive clusters to massive autoscaling jobs and Photon-powered SQL warehouses.
The flipside of this granularity is the increase in operational responsibility.
The engineering team’s level of experience in maintaining cluster configs, storage layout, and job design will have a bigger impact on performance. Poorly governed workspaces can run into noisy-neighbour effects or under-/over-provisioned clusters more easily than the more opinionated Snowflake/BigQuery models.
Total cost of ownership
Snowflake
Snowflake’s pricing model is built around three components: storage, compute (virtual warehouses), and cloud services.
Storage
Snowflake storage is billed at a flat rate per TB per month, with costs varying by plan and region. The platform has a calculator that engineering teams can use to budget their storage expenses precisely. Based on this data, we approximated Snowflake storage pricing across key regions.
| AWS US East (N. Virginia) | - On-demand - Capacity / pre-purchase | $40 / TB $23/TB |
| AWS Canada Central | On-demand | $25 / TB |
| AWS EU (e.g., Zurich / London) | On-demand | $26.95–$45 / TB |
| Capacity EU (general) | Capacity | $24.5 / TB |
| APAC / Middle East | On-demand | $25–$30 / TB |
Compute
Compute is priced per second in credits and is only charged while a virtual warehouse is running. The number of credits a warehouse consumes depends on its size, how long it runs, and the Snowflake edition the team chooses.
Because idle warehouses incur no cost, teams often leverage auto-suspend and fast resume to avoid paying for unused capacity by spinning up larger warehouses for heavy jobs and shutting them down as soon as those jobs complete.
| Standard | $2.00 / credit | Frequently cited as the baseline on-demand price in AWS US East and similar regions. |
| Enterprise | $3.00 / credit | Typical on-demand rate for accounts needing multi-cluster and stronger governance features |
| Business Critical | $4.00 / credit | Higher tier aimed at regulated workloads (HIPAA/PCI, tri-secret encryption, etc.). |
| All editions (capacity) | $1.50–$2.50 / credit effective | Typical discounted range reported for customers on annual capacity commitments rather than pure on-demand. |
Cloud costs
Cloud services introduce a third dimension to pricing, but with a built-in buffer.
Metadata management, query parsing, authentication, and other control-plane operations are counted as cloud services usage, which is included up to 10% of the daily compute consumption at no extra cost.
If cloud services exceed the 10% threshold, additional credits are billed, and Snowflake automatically applies a daily 10% credit adjustment to account for the included portion.
Realistically, typical workloads never see a separate cloud-services line item. Still, metadata- or governance-heavy patterns (lots of short queries, frequent DDL, or heavy catalog activity) can push teams above the threshold and should be monitored.
BigQuery
BigQuery’s compute and query pricing revolves around two main models: on-demand and capacity-based (slots via BigQuery Editions).
On-demand model (default)
Under this model, teams pay per number of logical bytes processed (e.g., scanning table data, materialized views, or external data), so the key levers are how much data each query reads and how often queries are run.
Google’s budgeting tools, like query validator and dry runs, help estimate bytes processed before execution. BigQuery also has the maximum bytes billed setting that allows teams to hard-cap costs for individual queries.
Capacity-based planning
With capacity-based pricing, engineering teams can reserve a fixed number of slots (virtual compute units) via BigQuery Editions and pay per slot-hour for the allocated capacity.
The advantage of that model is that, as long as workloads stay within your reserved and autoscaled slot pool, teams do not pay incremental per-query fees, and performance is governed by how many slots are available for concurrent queries.
This approach improves cost predictability for large, steady workloads but requires more active capacity planning and reservation management.
Under-provisioning will cause heavy or over-concurrent workloads to queue and run more slowly, while over-provisioning will have teams paying for idle slots.
Databricks
Databricks also offers engineering teams separate pay-as-you-go and provisioned capacity models to better adapt to a wide range of data jobs.
The pay-as-you-go model
In the pay-as-you-go model, Databricks charges based on DBUs burned for running clusters, SQL warehouses, or GenAI/ML endpoint consumes DBUs per hour.
Since there is no upfront commitment, engineers can freely scale workflows, explore services, or handle seasonal spikes without contract changes. However, month-to-month pay-as-you-go spend is unpredictable, which means teams need good tagging, monitoring, and auto-stop policies to avoid infrastructure cost spikes.
Committed-use discounts
Under this model, teams agree to a minimum Databricks spend (or DBU volume) over a fixed term, typically within the range of 1–3 years, and Databricks reduces the per-DBU price across the workloads covered by that commitment.
It’s a reasonable model for organizations that already run steady data engineering, SQL warehousing, or GenAI workloads and can forecast their baseline compute needs. If teams exceed the committed level, extra usage is billed at standard (or slightly discounted) rates and, if they fall short, they still pay for the committed minimum.
Caveats for comparing the total cost of ownership
Although all three vendors share price lists that break down compute and storage costs, this data alone cannot predict how much using a specific data platform will cost for the following reasons.
Reason #1. Each vendor’s “unit of compute” is different.
Vendor price lists are not directly comparable as Snowflake sells “credits,” Databricks bills in “DBUs,” and BigQuery charges in “slot-seconds” or bytes scanned. Each of these units represents different mixes of CPU, memory, and time.
- Snowflake credit buys time on a virtual warehouse you size yourself
- Databricks DBUs back clusters or SQL serverless tiers
- BigQuery’s slot-based/bytes-scanned model runs queries on a massive multi-tenant pool.
The way capacity scales, shares, and idles across these platforms is not the same, so two “similar-looking” price points can behave very differently when applied to real queries and concurrency on them.
Hence, “$2 per credit” vs “$2 per DBU” vs “$X per slot” doesn’t offer a clear estimate of which system will actually be cheaper for your workload.
Reason #2. Query runtimes don’t scale the same way as data grows
When ClickHouse assessed how data platforms behave under growing loads, it turned out that, as teams move from 1B to 10B to 100B rows, some systems drift into “slow and high-cost” much faster than others.
While the cost-per-unit from the price list stays constant, the amount of compute each query burns grows at different rates per engine, so a vendor that appears cost-effective at a small scale can become unsustainably expensive at enterprise scale.
Reason #3. Price lists don’t factor in the difference in required developer experience
A further caveat is that list prices ignore the cost of the people needed to run each platform well, and this impact is not uniform across vendors.
Databricks, in particular, tends to require more experienced data and platform engineers to design cluster strategies, optimize jobs, manage storage layout, and keep multi-tenant workspaces healthy. Under-investing in that expertise results in wasted compute and unstable pipelines, and hiring for it creates a higher payroll compared to a leaner “warehouse-first” stack.
I haven’t used Snowflake, but for just querying data, BigQuery is amazing, and I loathe Databricks. If the finance department accounted for all the wasted engineering time babysitting Databricks, I don’t know if it’s actually cheaper or worth it.
A Reddit comment calls out added engineering strain for Databricks users.
By contrast, Snowflake, although it has a higher price list, requires less day-to-day performance tuning from specialized engineers, so, to some teams, it may be cheaper long-term than Databricks.
Choosing the best data platform for your use case
Before choosing a data platform, use this decision-making cheatsheet to clearly identify your infrastructure, team, budget, and performance requirements.
If you don’t have a clear understanding of your use case yet, here are broad-stroke considerations that can help engineering teams break the tie between three popular data platforms in the enterprise.
| Is GCP already your primary cloud (and likely to stay that way)? | ||
| Do you want a BI-first, single source of truth with minimal platform babysitting? | ||
| Do you need one platform for data engineering + ML + GenAI on the same curated tables? | ||
| Are you dealing with huge event / log / clickstream datasets and lots of ad-hoc analytics? | ||
| Are you planning to stay multi-cloud (significant workloads on more than one hyperscaler)? | ||
| Is your team light on senior platform and infra engineers and heavier on analysts or dbt-style data engineers? | ||
| Are your core systems and identity strongly tied to Azure and the Microsoft stack (Entra, Power BI, Fabric)? | Azure Databricks is better if you want a lakehouse and ML tightly integrated with Azure tools. | |
| Do you prioritize governed self-service SQL for many business users over advanced ML? | ||
| Do you have a strong ML/AI engineering team that wants to own complex pipelines and agents in-house? | ||
| Is cost predictability and minimising engineering time more important than squeezing every last % of performance? |
Snowflake: teams with a straightforward multi-cloud analytics stack
If your organization is looking for a straightforward, multi-cloud analytics and AI backbone where most logic lives in SQL and business users expect one consistent source of truth, Snowflake will be the right call.
It fits well if you are on AWS or Azure, need governed data sharing across teams or partners, and care about adding GenAI features (via Cortex, vector search, Native Apps) directly on top of existing analytics without building a full ML platform.
Teams that value predictable BI and ELT performance, simpler day-to-day operations typically get a lot of value out of Snowflake with minimal maintenance cost and overhead.
BigQuery is best for teams whose infrastructure lives on GCP
Companies building with Google Cloud will see no friction when connecting BigQuery to large volumes of event, log, and behavioural data.
The platform supports complex, ad hoc analytics at streaming scale and offers a bridge from warehouse tables to ML and GenAI via BigQuery ML, Vertex AI, and Gemini.
Databricks is best for teams that want a ‘Swiss knife’ data platform
It allows data engineers to unify data pipelines, streaming, BI, and ML/GenAI, even though the learning curve is steep and requires strong engineering expertise.
Databricks delivers the most value when you’re ready to invest in cluster and job governance, accept more operational responsibility in exchange for flexibility, and want your analytics, ML models, and AI agents all to share the same data backbone rather than being split across separate, warehouse-only stacks.
Choosing between Snowflake, BigQuery, and Databricks is a crucial strategic decision that impacts the productivity of the engineering team, added costs, and the ability to deliver data products at scale.
An informed choice aligned with your company’s infrastructure, team capabilities, and business requirements will prevent costly migrations, technical debt, and productivity bottlenecks down the road.
