Redshift Alternatives: An Honest Guide for Teams Running on AWS

You're on AWS, so someone — maybe your CTO, maybe your board, maybe you — said "just use Redshift." It's Amazon's data warehouse. You're already paying Amazon for everything else. Why wouldn't you?

Here's the honest answer: defaulting to Redshift because you're on AWS is like defaulting to Amazon Basics headphones because you have a Prime membership. It's a reasonable instinct, but the product doesn't hold up once you actually use it.

I'd steer most teams away from Redshift as a starting point — even if you never look at Definite. The pricing is hard to predict without careful configuration. The surrounding stack you need to build is the same regardless. And for teams without a dedicated data engineer, the operational overhead is real.

This guide covers actual alternatives with real cost math, migration difficulty, and an honest assessment of when Redshift is still the right call. No recycled feature grids. No "Top 10" list where every tool gets a glowing review.

The Quick Answer

If you're not sure, keep reading — the next two sections will help you decide.

When to Stay on Redshift

Before we talk alternatives: Redshift is genuinely the right answer for some teams. If any of these apply, switching may cost more than staying.

• You've committed to Reserved Instances. If you're locked into 1- or 3-year RI pricing, your Redshift compute cost is already sunk. Evaluate alternatives for after the term expires, but don't eat the penalty to leave early.
• Redshift Serverless is working for you. AWS significantly improved Redshift with Serverless (launched 2022), which separates storage from compute and eliminates cluster management. If your workloads are light enough that the billing stays predictable — and you've set spending limits — the pain points in this article may not apply to you.
• You're using Redshift-specific features. Redshift Spectrum (querying S3 directly), Redshift ML, data sharing, and especially zero-ETL integrations with Aurora and DynamoDB — if you rely on these, the migration cost to replicate them elsewhere is real. Zero-ETL is a genuine differentiator: it replicates your AWS database into Redshift automatically, without needing a separate ETL tool. The catch: it only works with AWS databases, not SaaS tools like Salesforce or HubSpot.
• Your team is productive and your costs are stable. "It works" is a legitimate reason to stay. The goal isn't to leave Redshift — it's to have analytics infrastructure that works reliably and costs what you'd expect it to cost.

If none of those apply — or if you're still evaluating Redshift and haven't committed yet — read on. One thing working in your favor: if you're on AWS, your data likely already lives in S3, RDS, or Aurora. Most alternatives can connect to those directly, which makes switching easier than it sounds.

The Warehouse Isn't Your Cost Problem

Here's the piece most guides skip: the warehouse is the cheapest part of your data stack.

A Redshift cluster might cost you $300–$700/month for a small provisioned setup, or less on Serverless for light workloads. That's the number people fixate on.

But a working analytics setup isn't just a warehouse. It's a warehouse + an ETL tool to get data in + a transformation layer to model it + a BI tool to visualize it. For a typical 50-person B2B SaaS company, that stack looks like this:

Layer	Tool example	Typical monthly cost
ETL / ingestion	Fivetran	$20–$500
Warehouse	Redshift	$300–$700
Transformation	dbt Cloud	$300–$500
BI / dashboards	Looker or Tableau	$800–$2,100
Tech total		$1,400–$3,800
Data team (quarter of a full-time hire)		$3,500–$4,000
Fully loaded total		$5,000–$7,800/mo

Ranges based on 2025–26 vendor pricing for a 50-person company with 4 data sources. See our warehouse cost comparison for vendor-by-vendor breakdowns.

The warehouse bill is 15–20% of your total spend. Switching from Redshift to Snowflake saves you on the warehouse line — and changes nothing about the other 80%.

This is why all-in-one platforms exist as an alternative to assembling a stack. But even if an all-in-one isn't right for you, understanding the full cost changes how you evaluate alternatives. Don't optimize the warehouse bill in isolation.

For more detail on how these costs compound as you grow, see our complete B2B SaaS data stack cost guide.

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Quick-Reference Comparison Table

Tool	Type	AWS-native?	Monthly cost	Ops overhead	Best for
Definite	All-in-one platform	Runs on AWS	$250/mo (Platform)	Near-zero	No dedicated data team
Snowflake	Cloud warehouse	Runs on AWS	$400–$3,000+ (credits)	Low–medium	Teams already building a stack
Google BigQuery	Serverless warehouse	GCP only	Usage-based	Low	Teams open to multi-cloud
ClickHouse Cloud	Analytical database	Runs on AWS	Usage-based	Low	High-volume query workloads
DuckDB / MotherDuck	Embedded analytics	Any cloud	Free / $20+/mo	Medium–high	Small data, technical teams
Databricks	Lakehouse platform	Runs on AWS	$500–$5,000+ (DBUs)	High	Data engineering + ML teams

Definite is not a warehouse — it replaces the full stack (ETL + warehouse + BI). It's included here because teams searching for Redshift alternatives sometimes need a different approach, not just a different warehouse.

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The alternatives below fall into two categories. Options 2–6 are warehouse replacements — you swap Redshift for a different warehouse and keep building the stack around it. Option 1 is a different approach: skip the warehouse decision and get the full stack in one platform.

1. Definite — Skip the Warehouse Decision Entirely

Connectors	Starting price	Ops overhead	Eng. required?
500+ (Salesforce, HubSpot, Stripe, Postgres, and more)	$250/mo	Near-zero	No

Most alternatives on this list replace your warehouse with a different warehouse. You still need to pick an ETL tool, set up a transformation layer, choose a BI platform, and wire them together. Definite replaces the need to make those decisions at all.

It's a data platform in an app — connectors to your SaaS tools and databases, a built-in warehouse, a semantic layer for governed metrics, dashboards, and Fi — an AI assistant that lets anyone on your team ask questions in plain English without writing SQL. It feels as lightweight as signing up for a SaaS tool, but it has full data infrastructure behind it.

What you're actually replacing: The full stack — Redshift + Fivetran + dbt + Looker (typically $1,400–$3,800/month in tools alone) — with a single platform at $250/month. For startup-stage teams that don't need the customizability a multi-tool stack provides, the math is straightforward.

Migration from Redshift: Hours with onboarding support, not weeks. You're reconnecting to your sources directly, not moving warehouse data. You could realistically have dashboards running by Friday.

Best for: Series A–C teams that want answers from their data without assembling a multi-tool stack — especially when the person doing data work has "and also analytics" tacked onto their actual job title.

Honest caveat: If your team has a data engineer who's built a mature Redshift + dbt setup that's working, the migration is real work. Definite is strongest for teams that haven't invested heavily in a custom stack yet — or teams where the stack investment isn't paying off.

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2. Snowflake — The Most Common Warehouse Swap

Deployment	Starting price	Ops overhead	Eng. required?
Multi-cloud (incl. AWS)	Credit-based (varies)	Low–medium	Yes

Snowflake is the most common Redshift migration target. It runs on AWS, separates storage and compute cleanly, and eliminates the cluster-management overhead that makes Redshift frustrating — no sizing nodes, no vacuuming, no distribution keys.

What doesn't change: You still need ETL, a transformation layer, and a BI tool. The total stack cost is often similar to or higher than Redshift, because Snowflake's credit pricing can be unpredictable at scale. For a detailed comparison, see Redshift vs Snowflake vs Definite.

Best for: Teams with at least one data engineer who want a better warehouse experience and are willing to continue operating a multi-tool stack.

Honest caveat: It's a lateral move — a better warehouse, but still just a warehouse. If your problem with Redshift is the stack complexity around it, Snowflake doesn't solve that.

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3. Google BigQuery — Serverless, But You're Leaving AWS

Deployment	Starting price	Ops overhead	Eng. required?
GCP only	On-demand or editions	Low	Yes

BigQuery is genuinely serverless — no clusters, no nodes, no capacity planning. You run a query, Google handles the compute, you pay for the bytes scanned (on-demand) or reserve capacity (editions pricing). For teams whose Redshift frustration is about cluster management, BigQuery eliminates the problem entirely.

The AWS question: BigQuery runs on GCP. If your data lives in S3 and your infrastructure is on AWS, you have two options: move your data to GCS (one-time transfer cost + ongoing storage), or use BigQuery Omni to query data in S3 directly (limited functionality, higher per-query cost). Either way, you're introducing cross-cloud complexity.

AWS charges you to move data out of S3 — currently $0.09/GB for the first 10 TB/month. For a startup with 50 GB of analytical data, that's a one-time $4.50 transfer. For a company with 5 TB, it's $450. Not prohibitive for a one-time migration, but if your production systems stay on AWS and you're syncing continuously to BigQuery, it's a recurring cost.

Pricing: On-demand pricing charges $6.25 per TB of data scanned per query. For teams running a few dozen queries a day on moderate data volumes, this is often cheaper than Redshift. For heavy workloads, editions pricing (reserved capacity) is more predictable.

Best for: Teams that are open to multi-cloud, already use some GCP services, or want the simplest possible operational model for a warehouse.

Honest caveat: If "staying on AWS" is a hard constraint — for compliance, data residency, or just organizational preference — BigQuery creates friction. It's a great warehouse on its own terms, but it's not an AWS-native option.

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4. ClickHouse Cloud — For High-Volume Analytical Workloads

Deployment	Starting price	Ops overhead	Eng. required?
Multi-cloud (incl. AWS)	Usage-based	Low (Cloud)	Yes

ClickHouse is an analytical database optimized for speed on aggregation-heavy queries. If your Redshift pain is about standard BI reporting on SaaS data (CRM, billing, marketing), this probably isn't your section — skip to the FAQ. If your pain is query performance on large event datasets, product analytics, or real-time dashboards, keep reading.

ClickHouse Cloud is the managed service (launched 2022). You don't need to operate ClickHouse yourself — the cloud version handles scaling, backups, and maintenance. It runs on AWS, so your data stays in the same region.

Where it shines: Product analytics, event data, time-series analysis, and any workload with high query concurrency. ClickHouse can be significantly faster (10–100x depending on query type) than general-purpose warehouses on aggregation queries.

Where it doesn't: ClickHouse is not a general-purpose data warehouse. Its SQL dialect has differences from standard SQL (particularly around joins and subqueries). The ecosystem of ETL and BI tools that integrate with ClickHouse is narrower than Snowflake or Redshift. You may need to adjust your tooling.

Pricing: ClickHouse Cloud uses usage-based pricing — compute (per-second billing), storage, and data transfer. For read-heavy analytical workloads, it's often cheaper than Redshift or Snowflake. For mixed workloads, model it carefully.

Best for: Teams with a data engineer who need fast analytical queries on large event datasets, product analytics, or real-time dashboards.

Honest caveat: ClickHouse is exceptional at what it does, but it's a narrower tool than Redshift or Snowflake. If your workload is standard BI reporting on SaaS data (CRM, billing, marketing), a general-purpose warehouse or an all-in-one platform is likely a better fit.

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5. DuckDB / MotherDuck — The Open-Source Option

Deployment	Starting price	Ops overhead	Eng. required?
Any (local, cloud, embedded)	Free (DuckDB) / $20+/mo (MotherDuck)	Medium–high	Yes

DuckDB is an in-process analytical database — it runs inside your application or notebook, not as a separate server. Think SQLite, but designed for analytical queries instead of transactional ones. It's fast, free, open source, and increasingly popular in the data engineering community.

MotherDuck is the managed cloud version of DuckDB. It adds cloud storage, collaboration, and a web UI while keeping DuckDB's query engine.

Why it's here: DuckDB has become a legitimate option for teams with small-to-mid analytical data volumes (under 100 GB) who want a fast, SQL-compatible query engine without paying for or managing a cloud warehouse. For teams currently running Redshift Serverless at low query volumes, DuckDB + MotherDuck can replace the warehouse at a fraction of the cost.

What it doesn't do: DuckDB is not a production data warehouse for most teams — it's single-node (no distributed compute), doesn't have built-in ETL, role-based access controls, or the governance features enterprises need. You'll build everything around it yourself: ingestion, transformation, dashboards. For datasets above roughly 500 GB or workloads with many concurrent users, DuckDB hits its ceiling.

If DuckDB's speed appeals to you but building the surrounding stack doesn't, that's the use case all-in-one platforms are designed for — you get the fast query engine with connectors, dashboards, and governance already built around it.

Best for: Technically capable teams with Python/SQL expertise, moderate data volumes, and a preference for open-source tooling. Good for local development, prototyping, and embedded analytics even if it's not your production warehouse.

Honest caveat: The DuckDB ecosystem is young and evolving quickly. MotherDuck is still building features that Snowflake and BigQuery have had for years. If you need a production-ready, fully managed warehouse today, DuckDB may not be there yet — but it's worth watching.

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6. Databricks — Only If You Actually Need a Lakehouse

Be honest about whether you need this. If your analytics work mostly comes down to answering business questions from SaaS tools — MRR by segment, campaign attribution, churn cohorts — that's not a Spark problem. Databricks is a very expensive way to answer those questions.

Databricks runs on AWS natively, so there's no ecosystem friction. But the stack around it (ETL, BI, orchestration) still needs to be assembled and maintained.

Best for: Companies with dedicated data engineering teams, ML workloads, and petabyte-scale data. Not for a Series A startup that needs dashboards by next Tuesday.

For a deeper evaluation, see our Databricks alternatives guide, which covers who actually needs a lakehouse and who doesn't.

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What Actually Breaks When You Migrate Off Redshift

This is the section nobody writes. Here's what the migration actually involves, by destination.

Migration Difficulty Matrix

Target	SQL compatibility	Data transfer	Pipeline rewiring	Estimated timeline
Definite	N/A — reconnects to sources	Not needed	Not needed	Hours–days
Snowflake	High — minor dialect diffs	S3 → Snowflake stage	Update ETL destinations	1–3 weeks
BigQuery	Medium — more dialect diffs	S3 → GCS (egress cost)	Repoint + adapt ETL	2–4 weeks
ClickHouse	Medium — different SQL flavor	S3 → ClickHouse Cloud	Repoint ETL + adapt queries	2–4 weeks
DuckDB/MotherDuck	High — Postgres-compatible SQL	S3 or direct load	Rebuild ingestion	1–4 weeks
Databricks	Medium — SparkSQL differences	S3 native (no transfer)	Repoint ETL + adapt	3–6 weeks

Redshift-Specific Gotchas

Performance tuning doesn't transfer. Redshift's speed depends on how you physically organize data (distribution keys and sort keys). Other warehouses use completely different approaches — Snowflake has clustering keys, BigQuery uses partitioning. You won't migrate your tuning; you'll re-learn it on the new platform.

COPY and UNLOAD are Redshift-only. If your pipelines use Redshift's COPY command to load from S3 or UNLOAD to write back, those commands don't exist elsewhere. You'll need to replace them with the target platform's data loading mechanism.

Redshift-specific SQL functions. Functions like LISTAGG, NVL2, CONVERT_TIMEZONE, and Redshift's date/time handling have slightly different syntax on other platforms. Plan a day for query auditing.

The S3 advantage. If your data lives in S3, several alternatives can read it directly: Databricks and Redshift Spectrum already do, Snowflake stages from S3, and DuckDB reads Parquet files from S3 natively. Your data doesn't necessarily need to "move" — sometimes you just point a new engine at it.

Estimated Migration Effort

For a typical startup setup (5–10 connectors, under 100 GB, 10–20 dashboards):

• Connector parity check: 1–2 hours. Verify your data sources exist in the new tool.
• Data migration or reconnection: Hours to 1 day, depending on whether you're moving data or reconnecting to sources.
• Query and model auditing: 1–2 days. Audit dbt models and saved queries for Redshift-specific syntax.
• Dashboard rebuild or reconnection: 1–3 days if changing BI tools; hours if keeping the same one.
• Parallel testing: 1–2 weeks minimum. Run both systems before cutting over.

Total: 2–4 weeks of data engineer time for a warehouse-to-warehouse migration. Hours to days for an all-in-one platform that reconnects to sources directly instead of migrating warehouse data.

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FAQ

Is Redshift Serverless better than regular Redshift?

Yes — meaningfully so. Redshift Serverless (GA mid-2022) eliminates cluster sizing and management. You pay per RPU-hour (Redshift Processing Unit) for the compute you use. For variable or light workloads, it's a significant improvement over provisioned clusters. The tradeoff: RPU pricing can be hard to predict for complex queries, and heavy sustained workloads may cost more than reserved provisioned instances.

Can I use Snowflake on AWS?

Yes. Snowflake runs natively on AWS (as well as Azure and GCP). Your data stays on the cloud you choose.

What's the cheapest Redshift alternative?

DuckDB is free and open source. If you're comparing warehouse-to-warehouse, BigQuery's on-demand pricing and Snowflake's auto-suspend can both be cheaper than Redshift for light or intermittent workloads. For all-in-one platforms that replace the full stack (ETL, warehouse, BI, AI), Definite starts at $250/month — but the comparison isn't warehouse-to-warehouse, it's stack-to-platform.

Do I need a data engineer to switch off Redshift?

It depends on where you're going. Migrating to another warehouse (Snowflake, BigQuery, Databricks) is a data engineering project — query auditing, pipeline rewiring, performance tuning. Migrating to an all-in-one platform doesn't require a data engineer because you're reconnecting to sources, not moving warehouse data. And for what it's worth: ops and RevOps people make this decision all the time. You don't need to be a data engineer to choose the right analytics path for your team.

Can I keep my data on AWS if I switch?

Yes — for most alternatives. Snowflake, ClickHouse Cloud, Databricks, and DuckDB all run on AWS infrastructure. BigQuery is the exception (GCP only, though BigQuery Omni can query S3 data cross-cloud). Definite runs on AWS as well. "Leaving Redshift" does not mean leaving AWS.

Should I wait for Redshift to improve?

AWS continues investing in Redshift — Serverless, RA3, AQUA, streaming ingestion, zero-ETL integrations with Aurora and DynamoDB. If your complaints are about cluster management and you haven't tried Serverless yet, that's worth testing before migrating. But the broader ecosystem issues (needing separate ETL, transformation, and BI tools around it) are architectural — they won't be solved by Redshift feature updates.

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The warehouse is 15–20% of the decision. The real question is how much stack you want to build and maintain around it. Two places to start:

Model your current stack costs → — see what teams your size typically pay across ETL, warehouse, BI, and people.

If you want to skip the stack entirely: try Definite free.