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Decision comparison

Exasol vs Snowflake

Exasol and Snowflake both serve as a central analytics store and differ most in how they are paid for and where they run. Exasol is an in-memory MPP database with capacity-based pricing, deployable on your own hardware or in your own cloud account, and built for fast analytical queries at high concurrency. Snowflake is managed-only, consumption-priced, with compute separated from storage and workload isolation through independent virtual warehouses.

cloud data warehouses
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Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.

All 2 are cloud data warehouses.

Quick Comparison

Exasol

What it is:
A high-performance in-memory analytics database with massively parallel processing
Performance approach:
In-memory columnar processing with automatic self-tuning indexes, aimed at very fast analytical queries
Deployment:
Self-hosted on your own hardware, in your own cloud account, or Exasol SaaS
Pricing shape:
Contact for pricing
Concurrency:
Designed for many concurrent analytical users on one cluster
Data sharing:
Standard connectivity to BI and ETL tools
Best fit:
Steady high-concurrency analytical workloads where predictable cost and raw speed matter

Snowflake

What it is:
A managed cloud data platform with elastic compute separated from storage
Performance approach:
Elastic virtual warehouses sized per workload, scaled up or out as needed
Deployment:
Managed only, running on AWS, GCP and Azure
Pricing shape:
Snowflake prices on consumption, not a subscription: its pricing page states "We keep pricing simple with a consumption-based pricing model" and publishes no monthly or per-user price. Editions are Standard, Enterprise, Business Critical and Virtual Private Snowflake. Per-credit rates are scoped by edition, cloud and region: the Service Consumption Table effective 2026-09-09 lists on-demand AWS US East at $2.00 (Standard), $3.00 (Enterprise), $4.00 (Business Critical) and $6.00 (VPS), rising to $2.60/$3.90/$5.20 in AWS EU Dublin, so no single platform-wide credit price exists. Storage is billed separately at $23.00 per TB per month on demand in US East, less under capacity commitments. A 30-day free trial ends when the period or the included credit balance runs out; that is a trial, not a free tier. Verified 2026-09-16.
Concurrency:
Separate virtual warehouses isolate workloads from each other, scaled independently
Data sharing:
Sharing data between accounts without copying is a core platform capability
Best fit:
Varied workloads, many teams, and organisations wanting elasticity and data sharing

Public signals

Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.

MetricExasolSnowflake
Docker Hub pulls(Product adoption)2.2MNot available
GitHub commits, 90d(Developer adoption)
25
68
GitHub stars(Developer adoption)
81
730
Search interest(Market interest)
0
2
Hacker News mentions, 90d(Community interest)
1
0
npm weekly downloads(Developer adoption)
355
1.7M
PyPI weekly downloads(Developer adoption)
239.4k
22.9M
Stack Overflow questions(Community interest)
89
12.2k

As of September 21, 2026 — updated weekly.

Health & risk evidence

Observed public-source checks for mapped package versions and repositories.

Exasol

September 21, 2026

Package vulnerabilities

npm · @exasol/exasol-driver-ts@0.8.0 · PyPI · pyexasol@2.4.1

0 vulnerabilities

across 2 packages

Repository security score

Not available

Snowflake

September 21, 2026

Package vulnerabilities

PyPI · snowflake-connector-python@4.7.4 · npm · snowflake-sdk@3.3.0

0 vulnerabilities

across 2 packages

Repository security score

github.com/snowflakedb/snowflake-connector-python

5.0/10

Feature Comparison

Performance

In-memory processing

ExasolFull support
SnowflakePartial support

Massively parallel execution

ExasolFull support
SnowflakeFull support

Automatic query tuning

ExasolFull support
SnowflakePartial support

Workload isolation

ExasolPartial support
SnowflakeFull support

Elasticity

Scale compute independently of storage

ExasolPartial support
SnowflakeFull support

Pay only for compute in use

ExasolNot verified
SnowflakeFull support

Instant scale for bursts

ExasolPartial support
SnowflakeFull support

Predictable fixed cost

ExasolFull support
SnowflakePartial support

Deployment

Self-hosted on your own hardware

ExasolFull support
SnowflakeNot verified

Run in your own cloud account

ExasolFull support
SnowflakeNot verified

Fully managed service

ExasolFull support
SnowflakeFull support

Runs on AWS, GCP and Azure

ExasolPartial support
SnowflakeFull support

Platform

Cross-account data sharing

ExasolNot verified
SnowflakeFull support

Semi-structured data support

ExasolPartial support
SnowflakeFull support

Native BI integrations

ExasolFull support
SnowflakeFull support

Ecosystem of partner tools

ExasolPartial support
SnowflakeFull support
Full supportPartial supportNot supportedNot verifiedNot applicable

Which approach fits

Exasol and Snowflake both serve as a central analytics store and differ most in how they are paid for and where they run. Exasol is an in-memory MPP database with capacity-based pricing, deployable on your own hardware or in your own cloud account, and built for fast analytical queries at high concurrency. Snowflake is managed-only, consumption-priced, with compute separated from storage and workload isolation through independent virtual warehouses.

When each approach fits

Choose Exasol if:

Choose Exasol when the workload is steady and heavy, and predictable cost matters more than elasticity. Capacity pricing means the bill is known in advance rather than determined by how carefully analysts write SQL, in-memory processing with self-tuning indexes targets fast analytical queries, and it can run on your own hardware or inside your own cloud account where data residency requires it.

Choose Snowflake if:

Choose Snowflake when workloads vary, many teams share the platform, or elasticity is worth more than predictability. Separating compute from storage lets each team have its own virtual warehouse that scales independently and costs nothing when idle, data sharing between accounts avoids copying, and semi-structured data is handled natively. It runs managed on AWS, GCP and Azure.

These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.

Frequently Asked Questions

Which pricing model will cost us less?

Neither in general — it depends on utilisation. Capacity pricing charges whether or not anyone queries, so a cluster busy most of the day is good value and one used for two hours a morning is not. Consumption pricing charges per compute-second, so idle periods are nearly free and a poorly written query on a large warehouse is expensive. Model your own daily profile; the answer flips depending on how spiky it is.

Does in-memory processing make a practical difference?

It shapes what the database is optimised for. Holding working data in memory with self-tuning indexes targets low-latency analytical queries at high concurrency, which suits interactive dashboards with many simultaneous users. Snowflake's approach is elastic compute: rather than making one cluster fast for everyone, it gives each workload its own warehouse. Both reach good performance by different routes.

Can either run outside a vendor's cloud?

Exasol can — on your own hardware, in your own cloud account, or as SaaS. Snowflake is managed only, running on AWS, GCP and Azure. If data residency, an on-premise requirement or a regulatory constraint rules out a managed service, that settles the comparison before performance enters it.

What does Snowflake's data sharing actually do?

It lets one account grant another live access to data without copying or moving it, so a partner or another business unit queries the current data rather than a nightly extract. Organisations that routinely exchange data with subsidiaries, customers or vendors find this removes a whole class of pipeline work. If you never share data outside your own account, it is capability you will not use.

How do they handle many concurrent users?

Exasol is designed for high concurrency on one cluster, with the engine scheduling across users. Snowflake isolates workloads by giving each its own virtual warehouse, so a heavy data science query cannot slow the executive dashboard. The isolation model is easier to reason about when teams have very different workloads; the single-cluster model is simpler when they do not.