Snowflake vs Databricks: Which to Choose in 2026
Snowflake and Databricks spent 2025 and 2026 converging on each other's territory: each now sells a lakehouse, a managed Postgres (Databricks Lakebase went GA on February 3, 2026; Snowflake Postgres followed on February 24, 2026), Apache Iceberg support, and a full agent platform (Agent Bricks vs Cortex AI with CoWork and CoCo). So the decision is no longer a feature checklist; it is about workload center of gravity and team skills. Choose Snowflake when SQL analytics, BI concurrency, and governed data sharing dominate and you want the lowest operational overhead a small data team can run. Choose Databricks when data engineering, ML training, and agentic AI products dominate and your team is fluent in Python and Spark. Large enterprises increasingly run both, with Iceberg as the neutral storage layer between them.
Side-by-side comparison
| Dimension | Snowflake | Databricks |
|---|---|---|
| Core architecture | Multi-cluster virtual warehouses over Snowflake-managed storage | Open-format lakehouse (Delta, Iceberg) on your own object storage |
| Table formats and lock-in | Native tables plus Iceberg; Iceberg v3 support announced Summit 2026; Polaris catalog open-sourced | Open by default: Delta Lake and Iceberg; Unity Catalog open-sourced |
| SQL analytics and BI | Best-in-class UX and concurrency; Gen2 warehouses (GA May 2025) raised baseline speed | Strong and improving via Photon; administration heavier for analyst-only orgs |
| Large-scale ETL and data engineering | Capable, but warehouse-rate economics penalize long-running jobs | The benchmark: native Spark, classic Jobs Compute rates from about $0.15/DBU, spot instances |
| Machine learning | Snowpark ML plus Cortex Training (managed-GPU fine-tuning, announced June 2026); younger stack | Deepest in market: MLflow, feature engineering, GPU training, model serving |
| Agent / GenAI platform | Cortex AI: CoWork (business-user agent, ex Snowflake Intelligence), CoCo coding agent, Cortex Sense semantic runtime (all June 2026) | Agent Bricks: 100,000+ agents built, managed memory on Lakebase, sandboxed execution, Genie Ontology (DAIS 2026) |
| Built-in model access | Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek via SQL functions; Grok added June 2026 | OpenAI, Anthropic, Gemini, Qwen, Kimi; Grok via SpaceX partnership (June 2026) |
| Transactional (OLTP) database | Snowflake Postgres, GA February 24, 2026 (from the Crunchy Data acquisition) | Lakebase serverless Postgres, GA February 3, 2026 (from the Neon acquisition) |
| Streaming ingestion | High-performance Snowpipe Streaming GA September 2025; vendor-quoted up to 10 GB/s, sub-10s latency | Spark Structured Streaming and Delta Live Tables; strongest for complex streaming transformations |
| Pricing model | Credits billed per second; rate varies by edition and region; separate AI Credits pool for AI usage | DBUs varying by workload and tier (about $0.15 to $0.70 US list range); classic compute adds separate cloud VM costs |
| Cost center of gravity | Usually cheaper for spiky, high-concurrency BI (auto-suspend, per-second billing) | Usually cheaper for large scheduled ETL and ML training (jobs rates plus spot VMs) |
| Governance | Horizon, plus per-agent identity, audit trails, and AI security posture management announced Summit 2026 | Unity Catalog across data, models, and agents; Unity AI Gateway announced DAIS 2026 |
| Data sharing and marketplace | Industry-leading: secure shares and Marketplace without copying data | Delta Sharing; functional but a weaker commercial ecosystem |
| Team skills that unlock it | SQL; analysts are productive on day one | Python and Spark; data engineers and ML engineers get the most out of it |
| 2026 momentum | Q4 FY2026 product revenue $1.23B, up 30% year over year (quarter ended January 2026) | $6.9B annualized revenue as of June 2026, growing over 80% year over year |
Snowflake
The AI Data Cloud: best-in-class SQL analytics with near-zero administration, now with Postgres and agents.
Snowflake is a multi-cloud data platform (AWS, Azure, GCP) built around per-second billed virtual warehouses over centrally managed storage. Its core strengths have not moved: best-in-class SQL UX, high-concurrency BI, near-zero administration, and the strongest cross-account data sharing and marketplace story in the industry. What changed through 2025 and 2026 is the perimeter. Gen2 warehouses went GA in May 2025 with faster analytical performance and no config changes. High-performance Snowpipe Streaming went GA in September 2025, vendor-quoted at up to 10 GB per second with sub-10-second latency. Snowflake Postgres, built from the reported 250 million dollar Crunchy Data acquisition, reached GA on February 24, 2026, giving Snowflake a transactional database for the first time. At Summit 2026 (June 2026) the AI stack was rebranded and expanded: Snowflake Intelligence became CoWork, a governed natural-language agent for business users that connects to tools like Google Drive, Salesforce, and Slack; Cortex Code became CoCo, a coding agent; Cortex Sense assembles semantic context for agents at query time; Cortex Training adds managed-GPU fine-tuning of open-weight models; and AI Credits split AI billing from warehouse compute. Iceberg v3 support and the open-sourced Polaris catalog blunt the old lock-in objection.
Pros
- Best-in-class SQL and BI experience with near-zero administration: auto-suspend, auto-resume, per-second billing
- Gen2 warehouses (GA May 2025) improved analytical query performance with no tuning work
- Strongest data sharing story in the market: secure shares and Marketplace across accounts, clouds, and regions
- Cortex AI exposes models from Anthropic, OpenAI, Google, Meta, Mistral, and DeepSeek (Grok added June 2026) as SQL functions analysts can call directly
- CoWork plus Cortex Sense (Summit 2026) give business users a governed natural-language agent over enterprise data
- Snowflake Postgres (GA February 24, 2026) adds managed transactional Postgres inside the platform
- High-performance Snowpipe Streaming (GA September 2025) covers real-time ingestion at up to 10 GB per second, per Snowflake's figures
- Iceberg v3 support and the open-sourced Polaris catalog reduce storage lock-in
Cons
- Long-running data engineering and ML compute is billed at warehouse rates; large scheduled ETL is usually cheaper on Databricks
- ML tooling (Snowpark ML, Cortex Training) is younger and narrower than Databricks' MLflow-centered stack
- No native Spark: existing PySpark pipelines must be rewritten to Snowpark or kept on a second platform
- AI usage now meters through a separate AI Credits pool (announced June 2026), a new budget line to govern
- Deepest platform features still assume Snowflake-native tables; Iceberg parity is recent and worth validating per feature
Best for
- → Analytics-first organizations where analysts outnumber data engineers and SQL is the working language
- → Regulated enterprises that need governed data sharing with partners, auditors, or customers without building pipelines
- → Small data teams that need a serious warehouse with the least operational surface possible
Worst for
- → ML-heavy organizations training and serving many models; the tooling gap to Databricks is still real
- → Teams with a large existing PySpark codebase they are unwilling to rewrite
- → Petabyte-scale scheduled ETL where warehouse-rate compute economics work against you
Per-second compute billed in credits; public FinOps guides put per-credit rates at roughly $1.50 to $4+ depending on edition, cloud, and region, plus about $23/TB/month for on-demand storage. AI usage meters separately via AI Credits (announced June 2026).
Hours to days for a first governed analytical workload.
Databricks
Data intelligence platform from the Spark creators: open-format lakehouse, deepest ML stack, and the leading agent platform.
Databricks is the data intelligence platform from the creators of Apache Spark: open-format lakehouse storage (Delta Lake and Iceberg on your own object store) with governed compute for data engineering, warehousing, ML, and now transactional and agentic workloads. Its 2025 to 2026 run was aggressive. It acquired Neon (announced May 2025, reported around 1 billion dollars) and shipped Lakebase, a serverless Postgres that went GA on AWS on February 3, 2026, unifying OLTP with the lakehouse. Agent Bricks, its managed agent-building platform, passed 100,000 agents built and over a quadrillion agent tokens processed per year as of Data + AI Summit 2026 (June 2026), where Databricks added managed agent memory backed by Lakebase, Document Intelligence SQL functions (GA), a sandbox for isolated agent execution, and native model access spanning OpenAI, Anthropic, Gemini, Qwen, Kimi, and Grok (via a SpaceX partnership). Unity Catalog, which Databricks open-sourced, remains the governance spine across data, models, and agents. Company momentum is unmistakable: annualized revenue crossed 6.9 billion dollars in mid-2026, growing over 80 percent year over year. The tradeoff is unchanged: more capability and a more open architecture in exchange for more decisions, more knobs, and a platform that rewards Python and Spark fluency.
Pros
- Deepest ML stack in the market: MLflow, feature engineering, model serving, and managed GPU training in one governed platform
- Agent Bricks is the most complete managed agent platform: 100,000+ agents built, sandboxed execution, managed memory, and model choice across OpenAI, Anthropic, Gemini, Qwen, Kimi, and Grok (DAIS 2026)
- Open formats by default (Delta Lake, Iceberg) on your own object storage; Unity Catalog is open source
- Lakebase (GA February 3, 2026) puts serverless Postgres OLTP on the same governed platform as analytics and ML
- Classic Jobs Compute plus spot instances make it the cost benchmark for large-scale scheduled ETL
- Photon has made Databricks SQL a credible warehouse for most BI workloads
- Free Edition gives teams a zero-cost on-ramp for evaluation and training
Cons
- More operational surface than Snowflake: workspaces, cluster policies, classic vs serverless choices; small teams feel it
- Pricing is harder to model: DBU rates vary by workload type and tier, and classic compute bills cloud VMs separately
- SQL and BI administration still trails Snowflake for pure analyst organizations
- The platform rewards Python and Spark fluency; a SQL-only team leaves much of its value unused
- Pace of change (Lakebase, Agent Bricks, new gateways and catalogs) is itself a tax: teams must keep up with the platform
Best for
- → ML-heavy organizations running many production models that want data, training, serving, and monitoring on one platform
- → Teams building agentic AI products that need OLTP state, vector search, model serving, and governance together
- → Engineering-led organizations standardizing on open table formats to preserve exit options
Worst for
- → Analyst-led organizations whose workload is dashboards, ad hoc SQL, and scheduled reports
- → Small teams without dedicated data engineering capacity to own platform decisions
- → Organizations whose main requirement is polished cross-company data sharing, where Snowflake remains ahead
DBU-based and workload-dependent: published list rates run from about $0.15/DBU for classic Jobs Compute to about $0.70/DBU for Serverless SQL in US regions (higher in some regions); classic compute bills cloud VMs separately, serverless bundles them. Commit-plan discounts are standard at scale.
Days to weeks for a first production pipeline; the Free Edition makes evaluation immediate.
Decision scenarios
Series B SaaS standing up its first real warehouse; analysts outnumber data engineers
Snowflake. Per-second billing with auto-suspend fits spiky analyst usage, and there is almost nothing to operate. Cortex functions cover the early AI features without new infrastructure. Revisit only if ML becomes the product.
Organization with 20+ production ML models and a PySpark-fluent engineering team
Databricks. MLflow-centered lifecycle, GPU training, and model serving on the same governed platform beat stitching external ML infrastructure onto a warehouse. The Spark skills transfer directly.
Regulated bank: heavy BI concurrency plus governed data sharing with partners and auditors
Snowflake. Secure shares move governed data to counterparties without pipelines or copies, and warehouse isolation handles thousands of concurrent BI users with minimal tuning. Run AI initiatives through Cortex under the same governance.
Building a customer-facing agentic AI product that needs OLTP state, vector search, and model serving
Databricks. Agent Bricks plus Lakebase (GA February 2026) covers agent logic, memory, transactional state, and serving in one platform, with sandboxed execution and per-agent governance added at DAIS 2026. Snowflake's equivalent stack is newer and aimed more at internal users.
Internal analytics copilot so business users can ask questions of governed warehouse data
Snowflake. CoWork with Cortex Sense (June 2026) is purpose-built for exactly this: natural-language access for non-technical users over data already governed in Snowflake, connected to Drive, Salesforce, and Slack. No agent engineering required.
Large enterprise with both a heavy BI estate and a serious ML organization
Run both; most large enterprises now do. Snowflake serves BI and sharing, Databricks serves engineering and ML, and Apache Iceberg support on both sides means one copy of data can serve two engines instead of duplicating storage.
Cost-driven migration of petabyte-scale scheduled ETL off a legacy warehouse
Databricks. Classic Jobs Compute at roughly $0.15/DBU list plus spot instance discounts on the underlying VMs is the strongest cost profile for long-running batch work. Warehouse-rate pricing on Snowflake works against exactly this shape of workload.
Common questions
Neither is categorically cheaper; workload shape decides. Spiky, high-concurrency BI is usually cheaper on Snowflake because per-second billing and auto-suspend mean you pay only while queries run. Large scheduled ETL and ML training are usually cheaper on Databricks, where classic Jobs Compute lists at roughly $0.15/DBU and spot VMs cut infrastructure cost further. Published rates: Snowflake credits run roughly $1.50 to $4+ each by edition and region plus about $23/TB/month on-demand storage; Databricks DBU list rates run from about $0.15 for classic Jobs Compute to about $0.70 for Serverless SQL in US regions, with cloud VMs billed separately on classic compute. Model your top ten workloads under both before trusting any blanket claim.
No. Both are growing and both keep winning new workloads. Snowflake reported $1.23 billion in Q4 FY2026 product revenue, up 30 percent year over year (quarter ended January 2026). Databricks announced 6.9 billion dollars in annualized revenue in June 2026, up more than 80 percent. Databricks is growing faster, but Snowflake's base of SQL and BI workloads is sticky and its sharing ecosystem has no real rival. The realistic 2026 outcome is coexistence, increasingly bridged by Apache Iceberg.
Yes, and at large enterprises this is now a standard pattern: Databricks for data engineering and ML, Snowflake for BI serving and data sharing. Iceberg support on both sides changed the economics: one copy of data in open table format can be read by both engines, where the old pattern required duplicating storage and syncing pipelines. You pay for the second platform in operational overhead, so mid-market teams should pick one and stretch it.
Databricks, and it is not close for teams doing serious ML. MLflow, feature engineering, distributed GPU training, and model serving are mature and integrated. Snowflake is investing (Snowpark ML, plus Cortex Training with managed-GPU fine-tuning of open-weight models, announced June 2026), and for teams that mainly want to call LLMs from SQL, Cortex is genuinely convenient. But for training, evaluating, and serving your own models at scale, Databricks remains the default recommendation.
Different audiences. Agent Bricks (100,000+ agents built as of DAIS 2026, with managed memory on Lakebase, sandboxed execution, and model choice across OpenAI, Anthropic, Gemini, Qwen, Kimi, and Grok) is the stronger platform for engineers building production agent systems. Snowflake's June 2026 lineup targets users of data rather than builders: CoWork gives business users a governed natural-language agent, CoCo handles coding tasks, and Cortex Sense assembles the semantic context agents need at query time. Builders lean Databricks; analytics-consumer organizations lean Snowflake.
Yes. Iceberg tables are supported, Snowflake open-sourced its Polaris catalog, and at Summit 2026 it announced Apache Iceberg v3 support. That materially weakens the old lock-in argument against Snowflake, though its deepest platform features still land on native tables first, so validate feature parity for your specific workload. Databricks supports both Delta Lake and Iceberg under Unity Catalog. Storage format is no longer the deciding factor between these two platforms.
As of early 2026, both can, via managed Postgres. Databricks bought Neon (announced May 2025, reported around 1 billion dollars) and shipped Lakebase, GA on AWS February 3, 2026, with agent memory and branching workflows built on top. Snowflake bought Crunchy Data (reported around 250 million dollars) and shipped Snowflake Postgres, GA February 24, 2026. Neither replaces a tuned dedicated OLTP estate for extreme transaction volumes, but for app and agent state living next to analytics, both are now credible.
BigQuery is a strong Snowflake alternative for GCP-committed organizations: comparable analytical strengths with deep GCP integration. Microsoft Fabric bundles the data stack for Microsoft-committed enterprises and keeps improving, though it is younger than either platform in this comparison. Our rule of thumb: single-cloud commitment plus stack alignment can justify BigQuery or Fabric; multi-cloud strategies and best-of-breed requirements keep landing on Snowflake or Databricks.
Plan for months, not weeks. The work is re-architecting data models, translating SQL dialects or rewriting Snowpark and PySpark code, moving data, and running both platforms in parallel while you validate results. Iceberg lowers the storage-migration cost if your tables are already in open format, but code, security models, and orchestration still move by hand. Before committing, run a two-week TCO spike on your heaviest real workloads; that evidence usually settles the decision faster than any comparison article.
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