Decision comparison
Exasol vs Amazon Redshift
Exasol and Redshift are both columnar MPP warehouses, and they differ on portability and tuning. Exasol runs on your own hardware, in any major cloud account, or as SaaS, is priced on capacity, and tunes its own indexes. Redshift is AWS-only, priced per node hour or capacity unit, and integrates deeply with S3, IAM, Glue and SageMaker.
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
| Decision factor | Exasol | Amazon Redshift |
|---|---|---|
| What it is | A high-performance in-memory MPP analytics database, deployable on your own hardware, in your own cloud account, or as SaaS | AWS's managed data warehouse, with provisioned clusters or a serverless option |
| Cloud | Cloud-agnostic: your own hardware, your own AWS, Azure or GCP account, or Exasol SaaS | AWS only, integrated with S3, Glue, QuickSight, SageMaker and IAM |
| Performance approach | In-memory columnar processing with automatic self-tuning indexes | Columnar MPP over RA3 managed storage, with result caching and concurrency scaling |
| Pricing shape | Contact for pricing | Amazon Redshift bills by usage and publishes no monthly plan. Provisioned clusters start at $0.543 per node-hour; Serverless is billed per RPU-hour from $0.375 in US East (N. Virginia), charged per second. New Serverless accounts get a $300 credit expiring after 90 days -- a free trial, not a free tier. Verified 2026-09-16 against aws.amazon.com/redshift/pricing/. |
| Lake integration | Standard connectors to object storage and external tables | Spectrum queries S3 data in place, joined against warehouse tables |
| Tuning burden | Indexes and statistics maintained automatically by the engine | Distribution and sort keys matter, though automatic table optimisation now handles much of it |
| Best fit | Teams wanting speed at concurrency without cloud lock-in or a tuning specialist | Teams already on AWS with data in S3 and bursty query patterns |
Exasol
- What it is:
- A high-performance in-memory MPP analytics database, deployable on your own hardware, in your own cloud account, or as SaaS
- Cloud:
- Cloud-agnostic: your own hardware, your own AWS, Azure or GCP account, or Exasol SaaS
- Performance approach:
- In-memory columnar processing with automatic self-tuning indexes
- Pricing shape:
- Contact for pricing
- Lake integration:
- Standard connectors to object storage and external tables
- Tuning burden:
- Indexes and statistics maintained automatically by the engine
- Best fit:
- Teams wanting speed at concurrency without cloud lock-in or a tuning specialist
Amazon Redshift
- What it is:
- AWS's managed data warehouse, with provisioned clusters or a serverless option
- Cloud:
- AWS only, integrated with S3, Glue, QuickSight, SageMaker and IAM
- Performance approach:
- Columnar MPP over RA3 managed storage, with result caching and concurrency scaling
- Pricing shape:
- Amazon Redshift bills by usage and publishes no monthly plan. Provisioned clusters start at $0.543 per node-hour; Serverless is billed per RPU-hour from $0.375 in US East (N. Virginia), charged per second. New Serverless accounts get a $300 credit expiring after 90 days -- a free trial, not a free tier. Verified 2026-09-16 against aws.amazon.com/redshift/pricing/.
- Lake integration:
- Spectrum queries S3 data in place, joined against warehouse tables
- Tuning burden:
- Distribution and sort keys matter, though automatic table optimisation now handles much of it
- Best fit:
- Teams already on AWS with data in S3 and bursty query patterns
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.
| Metric | Exasol | Amazon Redshift |
|---|---|---|
| Docker Hub pulls(Product adoption) | 2.2M | Not available |
| GitHub commits, 90d(Developer adoption) | 25 | 13 |
| GitHub stars(Developer adoption) | 81 | 71 |
| Search interest(Market interest) | 0 | 1 |
| Hacker News mentions, 90d(Community interest) | 1 | 0 |
| npm weekly downloads(Developer adoption) | 355 | 204.3k |
| PyPI weekly downloads(Developer adoption) | 239.4k | 9.8M |
| Stack Overflow questions(Community interest) | 89 | 8.8k |
| Product Hunt comments(Community interest) | Not available | 1 |
| Product Hunt reviews(Community interest) | Not available | 0 |
| Product Hunt votes(Community interest) | Not available | 68 |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Exasol
September 21, 2026Package vulnerabilities
npm · @exasol/exasol-driver-ts@0.8.0 · PyPI · pyexasol@2.4.1
0 vulnerabilities
across 2 packages
Repository security score
Not available
Amazon Redshift
September 21, 2026Package vulnerabilities
npm · @aws-sdk/client-redshift@3.1136.0 · PyPI · redshift-connector@2.1.16
0 vulnerabilities
across 2 packages
Repository security score
github.com/aws/amazon-redshift-jdbc-driver
4.5/10
Feature Comparison
| Feature | Exasol | Amazon Redshift |
|---|---|---|
| Performance | ||
| In-memory processing | Full support | Partial support |
| Massively parallel execution | Full support | Full support |
| Automatic index and statistics tuning | Full support | Partial support |
| Result caching | Full support | Full support |
| Operations | ||
| Pause compute when idle | Full support | Full support |
| Automatic concurrency scaling | Partial support | Full support |
| Serverless option | Partial support | Full support |
| Predictable fixed cost | Full support | Partial support |
| Deployment | ||
| Self-hosted on your own hardware | Full support | Not verified |
| Run in your own cloud account | Full support | Full support |
| Available on more than one cloud | Full support | Not verified |
| Fully managed service | Full support | Full support |
| Ecosystem | ||
| Query data in object storage | Partial support | Full support |
| Native cloud identity integration | Partial support | Full support |
| In-database machine learning | Partial support | Full support |
| Native BI integration | Full support | Full support |
Performance
In-memory processing
Massively parallel execution
Automatic index and statistics tuning
Result caching
Operations
Pause compute when idle
Automatic concurrency scaling
Serverless option
Predictable fixed cost
Deployment
Self-hosted on your own hardware
Run in your own cloud account
Available on more than one cloud
Fully managed service
Ecosystem
Query data in object storage
Native cloud identity integration
In-database machine learning
Native BI integration
Which approach fits
Exasol and Redshift are both columnar MPP warehouses, and they differ on portability and tuning. Exasol runs on your own hardware, in any major cloud account, or as SaaS, is priced on capacity, and tunes its own indexes. Redshift is AWS-only, priced per node hour or capacity unit, and integrates deeply with S3, IAM, Glue and SageMaker.
When each approach fits
Choose Exasol if:
Choose Exasol when you want analytical speed at concurrency without committing to one cloud, or when the database must run on your own infrastructure. In-memory processing and self-tuning indexes mean performance does not depend on having a specialist maintain distribution keys, and capacity pricing fixes the bill regardless of how the SQL is written.
Choose Amazon Redshift if:
Choose Amazon Redshift when you are on AWS and data already sits in S3. Spectrum queries it in place, RA3 nodes separate managed storage from compute, concurrency scaling absorbs bursts without permanent headroom, and IAM, Glue, QuickSight and SageMaker integrate natively. Redshift Serverless removes cluster sizing when you would rather not make that decision.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
How much tuning does each need?
Redshift performance depends on distribution and sort keys, though automatic table optimisation now chooses many of them and the gap has narrowed considerably. Exasol maintains indexes and statistics itself by design, so there is less to get wrong and less to tune when a query is slow. Teams without a dedicated warehouse specialist tend to notice this difference more than teams with one.
Does cloud portability matter in practice?
It matters when a real constraint exists: an on-premise requirement, a data-residency rule, a parent company on a different cloud, or a genuine multi-cloud strategy. It matters much less as insurance against a hypothetical future migration, because the pipelines, BI tools and identity integrations around the warehouse are usually harder to move than the warehouse itself.
What does Spectrum give Redshift?
It makes S3 a queryable tier rather than an archive. Cold data stays there and is still joinable against warehouse tables in the same SQL, so retention is not a choice between loading everything and losing access. For an organisation with years of data already in S3, this frequently decides the comparison on its own.
Which is cheaper?
Capacity pricing is cheaper when the cluster is busy most of the day, because you are using what you bought. Per-node or per-capacity-unit pricing with pausing and concurrency scaling is cheaper when load is concentrated in a few hours, because the quiet time costs little. Measure the fraction of the day your warehouse is actually working; that number decides it more reliably than a price list.
Can both handle streaming data?
Redshift has native streaming ingestion from Kinesis and Managed Streaming for Kafka, so recent events appear without a batch load. Exasol ingests through standard connectors and pipeline tooling instead. If sub-minute freshness is a requirement, Redshift's built-in path is less work; if hourly batches are enough, the difference is mostly plumbing preference.
How hard is it to migrate between them?
The SQL moves more easily than everything around it. Both speak standard analytical SQL, so most queries and views port with modest editing. What takes the time is the surrounding system: ingestion pipelines, orchestration schedules, BI semantic layers, row-level security rules and the access model. Budget for those rather than for the table definitions, and plan to run both in parallel while you reconcile numbers.