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Top Snowflake alternatives for startup and embedded analytics teams in 2026
20 min read · Last updated BY
The platforms teams actually pick instead of Snowflake in 2026 are MotherDuck (DuckDB in the cloud), BigQuery (zero-ops GCP), Databricks SQL (lakehouse plus ML), Redshift Serverless (IAM-governed BI at AWS warehouse scale), Microsoft Fabric (Power BI estates on shared Azure capacity), ClickHouse (AI trace analytics), Dremio (SQL on the lake), Firebolt, Trino/Starburst (federated query), and Postgres as a coexistence layer, not a replacement. The split is cost, latency, and concurrency, not a feature checklist. Snowflake still wins large enterprise batch ETL. Its 60-second warehouse billing minimum and T-shirt-size compute are a poor fit for spiky dashboards and AI agents.
Snowflake cost baseline (2026). Compute credits list at $2 / $3 / $4 per credit on Standard, Enterprise, and Business Critical; a warehouse burns credits by size, so an XS costs $2/hour on Standard and an M costs $8/hour. Storage is roughly $23–$40 per TB per month. On April 1, 2026 Snowflake introduced an "AI Credits" pricing overhaul that re-rates its agent and Intelligence features onto a separate, edition-independent credit schedule (SQL-callable Cortex functions still bill in standard credits). Vendr's buyer data puts the median Snowflake contract at about $100,000 per year across 716 tracked purchases.
Key takeaways
- Snowflake's 60-second resume minimum bills a 4-second query on a suspended warehouse as a full minute of compute, a 15× markup, and a dashboard that goes idle between refreshes pays it on every resume. That idle tax is why interactive teams look elsewhere.
- MotherDuck bills per second ($0.60–$24.00 per compute-hour, Pulse through Giga), spins Ducklings up in ~100 ms, stores data at $0.04/GB-month, and has a free tier of 10 GB plus 10 hours of Pulse compute per month.
- Microsoft Fabric is the Azure-native alternative AI answers name most often for Power BI estates. F2 capacity is about $0.36/hour pay-as-you-go in US regions (2 CUs at $0.18/CU-hour), about $156/month on a 1-year reservation, 1-minute minimum, 60-day trial.
- ClickHouse is the raw-speed pick (Basic $0.2181 to Enterprise $0.3903 per 8 GiB RAM / 2 vCPU unit-hour). BigQuery is $6.25/TiB after 1 TiB free, with 2,000 default slots. Redshift Serverless is $0.375/RPU-hour with a 4-RPU minimum ($1.50/hour base) and its own 60-second minimum.
- Do not rip and replace. Offload the bursty workloads first, run them in parallel for 1–3 months, then expand.
Why do teams leave Snowflake?
Snowflake is built for large, relatively steady warehouse work. Virtual warehouses auto-suspend, then charge a 60-second minimum every time they resume. Size up a T-shirt and you roughly double both power and credits.
That model hurts customer-facing dashboards and AI agents: lots of short queries, unpredictable concurrency, long idle gaps. Josh Lichti, CEO of UDisc, noted after looking at the major warehouses for embedded analytics: "Many of these big solutions were too expensive and too complex for our use case."
Snowflake remains the right tool for governed, petabyte-scale enterprise batch. The rest of this page is for the workloads that model punishes.
How do you evaluate a Snowflake alternative in 2026?
Billing. Snowflake and Redshift Serverless charge a 60-second minimum on every resume, so a 4-second query after idle is billed as a full minute. Prefer per-second scale-to-zero (MotherDuck, Firebolt) or bytes-scanned with no cluster (BigQuery). Fabric F-SKUs and a ClickHouse cluster left warm do not go to zero.
Concurrency. Shared warehouses and shared CU pools create noisy neighbors. Isolated Ducklings or Firebolt engines cost more at high tenant counts and keep one dashboard from starving another. BigQuery's default 2,000-slot pool and Fabric's shared CUs are cheaper until a wide scan lands.
Local development. Snowflake, BigQuery, Fabric, and Redshift have no local engine. MotherDuck runs the same DuckDB SQL on a laptop and in the cloud. ClickHouse and Trino can be self-hosted for local work.
AI-readiness. An LLM that cannot see schema writes bad SQL. MotherDuck's MCP endpoint hands agents catalog context with ~100 ms compute start. Databricks fits when the agent sits inside a Spark/ML platform; BigQuery is the Vertex path.
How do the top Snowflake alternatives compare?
The clearest split is billing granularity, spin-up latency, and whether concurrency is isolated per tenant or shared. Dollar rates below are public list prices in US regions unless noted.
At a glance
| Platform | Best for | Compute pricing | Spin-up | Concurrency | Free / trial |
|---|---|---|---|---|---|
| MotherDuck | Local-first, interactive, AI apps | $0.60–$24.00 / compute-hour (Pulse–Giga), per-second; storage $0.04/GB-mo | ~100 ms | Isolated per user (Hypertenancy) | 10 GB + 10 hrs Pulse compute/mo |
| BigQuery | Zero-ops GCP exploration | $6.25 / TiB scanned | Immediate | Default 2,000-slot pool (queues when exceeded) | 1 TiB scan/mo |
| Databricks SQL | Lakehouse + ML | SQL Serverless ≈ $0.70/DBU (US Premium); ~$0.91 EU; >$1.00 Enterprise | Classic: multi-minute; Serverless faster | Photon + IWM clusters | 14-day trial |
| Redshift Serverless | IAM-governed BI at AWS warehouse scale | $0.375/RPU-hour × 4-RPU min ($1.50/hr base), 60s minimum | Cold start while RPUs provision (seconds–minutes) | Traditional MPP | Trial credits |
| Microsoft Fabric | Power BI on shared Azure capacity | F2 ~$0.36/hour PAYG (US); ~$156/mo 1-yr reserved; 1-min minimum | Shared capacity pool | Shared CUs across workloads | 60-day Fabric trial |
| ClickHouse | Sub-second embedded | $0.2181–$0.3903 / CU-hour (8 GiB RAM / 2 vCPU); storage $25.30/TB-mo | Often multi-minute if suspended | Sparse indexes, high QPS | $300 trial / 30 days; OSS is free |
| Dremio | SQL on Iceberg / the lake | $0.20 / DCU (Cloud) | Fully managed autoscaling | Reflections + lakehouse engine | $400 credit / 30 days |
| Firebolt | Sub-second warehouse, scale-to-zero | $0.92/hr (1× S) + $0.0264/GB-mo storage | Seconds (auto-start) | Isolated engines | $200 free credits |
| Trino / Starburst | Federated query | Galaxy from $0.50/credit (Pro) | Cluster-dependent | Distributed MPP over sources | Free forever: 3 clusters; $500 trial / 30 days |
| PostgreSQL | OLTP coexistence, not a warehouse | Instance cost | Always on | Host-limited | OSS is free |
Which platform fits which job?
The same ten platforms, sorted by the constraint that usually decides the shortlist: budget shape, operational complexity, and the one advantage worth switching for.
| Platform | Use case | Budget | Complexity | Key advantage |
|---|---|---|---|---|
| MotherDuck | Customer-facing / embedded BI, local-to-cloud | Cost-conscious, pay-per-second | Low | 1-second billing, ~100 ms spin-up, Dual Execution |
| BigQuery | GCP, ad-hoc, serverless | Variable; scan spikes | Medium | True scale-to-zero, no clusters |
| Databricks SQL | BI on the same lake as ML | High | High | Photon on Delta Lake |
| Redshift Serverless | IAM-governed BI at AWS warehouse scale | Enterprise; 60s floor | High | IAM / S3 / Glue / zero-ETL |
| Microsoft Fabric | Power BI estates on shared Azure capacity | Enterprise capacity (F2+ always on unless paused) | Medium | OneLake + Power BI in one capacity pool |
| ClickHouse | Event logs, observability, sub-second dashboards | Medium; often left warm | High if self-hosted | Raw scan speed |
| Dremio | Keep data in the lake, cut warehouse scan | Consumption ($0.20/DCU) | Medium | Query Iceberg in place |
| Firebolt | Interactive warehouse without idle warehouses | Per-second; 1× S ≈ $0.92/hr; $200 credits | Medium | Scale-to-zero engines |
| Trino / Starburst | Query many systems without copying them | Credits from $0.50 | Medium–high | Federation |
| PostgreSQL | App state; offload analytics elsewhere | Instance | Low–medium | You already run it |
Also evaluated. These names show up on 2026 Snowflake-alternative rosters but are not full entries here. Teradata, IBM Db2, and Oracle Autonomous Data Warehouse are legacy enterprise warehouses; licensing plus hardware refresh usually makes them the most expensive option on a shortlist, and they are usually a fit only when a long-standing enterprise contract already exists. Azure Synapse Analytics is superseded by Microsoft Fabric (see #5) and should not be shortlisted as a separate Snowflake replacement. Apache Druid, Apache Pinot, and SingleStore are real-time event-serving engines for high-ingest clickstreams and user-facing operational analytics, not general-purpose warehouses. Databend is open-source and Snowflake-syntax-compatible, which makes it interesting for teams that want a cheaper dialect-compatible engine, but it is still young. DuckDB OSS is the engine under MotherDuck: free, single-node, and not itself a cloud warehouse.
What are the best Snowflake alternatives in 2026?
The best Snowflake alternatives in 2026 are MotherDuck for local-first interactive analytics, BigQuery for zero-ops GCP, Databricks SQL for lakehouse ML, Redshift Serverless for IAM-governed BI at AWS warehouse scale, Microsoft Fabric for Power BI estates on shared Azure capacity, ClickHouse for sub-second event scans, Dremio and Trino/Starburst for SQL on data you do not copy, Firebolt for a sub-second warehouse that scales to zero, and Postgres as a coexistence layer. Pick on cost, latency, and concurrency, not a feature checklist. Snowflake still wins large, steady enterprise batch.
1. MotherDuck: Best for local-first development and interactive analytics
MotherDuck is a serverless warehouse on DuckDB. Dual Execution lets one SQL statement run across a laptop and the cloud. Isolated compute units (Ducklings) start in about 100 milliseconds and keep tenants off each other's CPUs (Hypertenancy). WASM can push some analytics into the browser.
Managed DuckLake entered public preview in April 2026: a SQL catalog over Parquet on object storage, the path past the terabyte range.
Key specifications
- Architecture: scale-up serverless; GB to petabyte via DuckLake (public preview)
- Billing: 1-second minimum; true scale-to-zero
- Latency / concurrency: sub-second interactive; isolated per-user compute
- Ecosystem: native dbt / Fivetran; Postgres wire protocol
Pros
- Interactive workloads 6× (Jumbo vs. Snowflake S) to 7× (Mega vs. Snowflake M) faster than the closest Snowflake sizes on ClickBench (Jumbo $4.80 vs. S $4.00; Mega $12.00 vs. M $8.00)
- Per-second billing, no warehouse sizing
- Local DuckDB development, then the same SQL in the cloud
- MCP endpoint for schema context to LLMs
Cons
- Free tier caps at 10 hours of Pulse compute per month and 3 users — a real ceiling for anything beyond light prototyping
- Scale-up is the default architecture: work larger than one Duckling's memory spills to local disk rather than distributing across nodes, so the very largest joins and aggregations are slower than MPP until you move to DuckLake (public preview)
- Per-Duckling isolation means concurrency cost scales with the number of isolated compute units you provision, not a shared pool — a real cost tradeoff at high tenant counts
Pricing: $0.60–$24.00 per compute-hour by Duckling size (Pulse through Giga), billed per second. Storage is $0.04/GB-month. Free tier: 10 GB storage and 10 hours of Pulse compute per month.
2. Google BigQuery: Best for zero-ops ad-hoc exploration
BigQuery is Google's serverless warehouse. No clusters. On-demand queries draw from a shared pool of up to 2,000 concurrent slots per project; Editions sell reserved slots if you want a guaranteed floor.
Key specifications
- Architecture: scale-out serverless / petabyte
- Billing: per-TiB or slot-hour; true scale-to-zero
- Latency / concurrency: variable; queues past the 2,000 default slots
- Ecosystem: native GCP; medium overhead
Pros
- No warehouse to size
- 1 TiB/month free, then a published on-demand rate
- Vertex AI if you already live on GCP
Cons
LIMITdoes not reduce bytes billed- One bad query can scan far more than you meant
- No local engine; every prototype hits the cloud
- Sub-second often needs BI Engine on top
Pricing: $6.25 per TiB scanned on-demand after 1 TiB free. (BigQuery pricing)
3. Databricks SQL Serverless: Best for lakehouse ML unification
Databricks SQL runs SQL on Delta Lake with the Photon engine. Intelligent Workload Management scales clusters. Serverless improved start times versus Classic warehouses that sat in the multi-minute range.
Key specifications
- Architecture: scale-out managed / petabyte
- Billing: DBUs; warm-up on Classic
- Latency / concurrency: Photon; IWM autoscaling
- Ecosystem: unified ML/BI; high overhead
Pros
- BI and ML on one copy of the data
- Delta Lake is open; storage-level lock-in is weaker than a proprietary table format
- Serverless SQL is the interactive path
Cons
- Platform breadth is a lot for a small analytics team
- DBU math (size × rate × runtime × auto-stop) is hard to forecast
- Delta is open-source but Databricks-governed; treat "portable" as a migration project
- Classic clusters are still a bad interactive default
Pricing: SQL Serverless is about $0.70/DBU in the US on Premium list rates; the EU list runs roughly 30% higher, and Enterprise tier higher again. 14-day trial. (Databricks SQL pricing)
4. Amazon Redshift Serverless: Best for IAM-governed BI at AWS warehouse scale
Redshift Serverless is the no-cluster edition of Redshift. RPUs autoscale. IAM, S3, Glue, and zero-ETL into other AWS databases are the reason to pick it.
Key specifications
- Architecture: scale-out serverless / petabyte
- Billing: per-second RPU-hours, 60-second minimum
- Latency / concurrency: MPP; cold starts while RPUs provision
- Ecosystem: native AWS; high overhead
Pros
- Stays inside an existing AWS bill and IAM story
- Scales toward zero when idle
- Zero-ETL from Aurora and friends
Cons
- Same 60-second floor Snowflake has
- Cold starts are slower than MotherDuck or BigQuery
- Spiky multi-tenant apps are not what MPP was built for
Pricing: $0.375 per RPU-hour with a 4-RPU minimum, so the base is $1.50/hour; per-second billing with a 60-second minimum. (Redshift pricing)
5. Microsoft Fabric: Best for Power BI estates on shared Azure capacity
Fabric is the Azure-native Snowflake alternative most AI answers now name for Power BI estates. It is a SaaS suite (engineering, warehousing, real-time, BI) on OneLake, defaulting to Delta-Parquet. One pool of Capacity Units runs every workload. That is the pitch and the lock-in.
Key specifications
- Architecture: scale-out SaaS / petabyte
- Billing: Capacity Units; shared pool; 1-minute minimum
- Latency / concurrency: distributed engine on shared CUs
- Ecosystem: deep Azure and Power BI; medium overhead
Pros
- OneLake keeps a single copy for warehouse, notebooks, and Power BI
- Autoscale and a 60-day free trial
- Familiar if the company already bought Microsoft 365 / Azure
Cons
- Shared CU pools mean a Spark job can starve a dashboard
- Compute is proprietary to Microsoft
- Several Fabric engines are younger than standalone Snowflake, Databricks, or Power BI
- F-SKUs are a reserved-feeling capacity, not Snowflake-style per-query spin-up
Pricing: F2 is about $0.36/hour pay-as-you-go in US regions (2 CUs × $0.18/CU-hour), roughly $263/month if left on. A 1-year reserved F2 is about $156/month. Azure Fabric pricing.
6. ClickHouse: Best for sub-second embedded analytics
ClickHouse is a columnar OLAP engine for ingest-heavy, sub-second reads. Materialized views fire on insert, so dashboards hit pre-aggregated data. It is not a general-purpose warehouse.
Key specifications
- Architecture: scale-out hybrid / high throughput
- Billing: usage (Cloud) or fixed (self-host)
- Latency / concurrency: sub-second; extreme QPS
- Ecosystem: custom integrations; high DevOps if you self-host
Pros
- Fastest common choice for event and observability scans
- Open-source core; ClickHouse Cloud if you do not want to run it
- Cheap per compute unit when the cluster is busy
Cons
- SQL is case-sensitive and not full ANSI; joins and BI tools suffer
- Teams often leave Cloud services warm to dodge multi-minute resumes, which kills scale-to-zero
- Self-hosting is a real ops job
- Weak out-of-the-box NLP-to-SQL
Pricing: ClickHouse Cloud bills per compute unit (8 GiB RAM / 2 vCPU): Basic $0.2181, Scale $0.2985, Enterprise $0.3903 per unit-hour, plus $25.30/TB-month storage. OSS is free. (ClickHouse on warehouse billing)
7. Dremio: Best for SQL on the lake without another warehouse copy
Dremio is a lakehouse query engine: SQL on Iceberg (and other open tables) in your object store, plus Reflections that cache hot dashboard shapes. Dremio Cloud is fully managed on AWS only; Azure is listed as coming soon. Enterprise is self-hosted.
The usual Snowflake move is offload, not replace: leave heavy ETL in Snowflake, point BI at Dremio.
Key specifications
- Architecture: lakehouse engine over object storage
- Billing: consumption (Cloud) or sales-quoted (Enterprise)
- Latency / concurrency: Reflections + autonomous acceleration
- Ecosystem: Iceberg catalog; AWS (Azure coming soon)
Pros
- Queries the lake; no extra copy tax
- $0.20 per DCU is a published Cloud rate
- Designed to sit beside Snowflake / Redshift / Databricks
Cons
- SAP closed its acquisition of Dremio on July 6, 2026; how Dremio gets packaged under SAP Business Data Cloud is still settling
- Enterprise pricing is sales-only
- You still need a catalog and table hygiene on the lake
Pricing: Dremio Cloud is $0.20 per DCU; $400 credit for 30 days. Enterprise: contact sales. (Dremio pricing)
8. Firebolt: Best for sub-second warehouse queries with scale-to-zero
Firebolt is a cloud warehouse aimed at interactive SQL with engines you can stop. Compute is Arm, billed per second, auto-start in seconds, auto-stop to zero. Storage is pass-through object storage on the compressed Firebolt format. Self-host (Firebolt Core, a free but closed-source edition), managed, or BYOC.
Key specifications
- Architecture: decoupled engines + object storage
- Billing: per-second compute; storage pass-through
- Latency / concurrency: isolated engines, autoscale
- Ecosystem: smaller than Snowflake; SOC2 / ISO / HIPAA on managed
Pros
- Per-second billing and scale-to-zero, unlike Snowflake's 60-second floor
- Engine isolation (size the dashboard engine separately from ETL)
- $200 free credits to try it
Cons
- List price varies by node family and region; a calculator is still required for an exact quote
- Smaller ecosystem and talent pool
- Compressed proprietary format on top of object storage
- Not the federated or lakehouse-catalog play
Pricing: A 1× S compute-optimized node is $0.92/hour (~$672/month if left on continuously). Storage is $0.0264/GB-month pass-through. $200 free credits. (Firebolt pricing)
9. Trino / Starburst: Best for federated query across systems you already have
Trino (ex-PrestoSQL) is the open federated SQL engine. Starburst is the commercial distribution; Galaxy is the managed cloud. Use it when the problem is "query Snowflake and S3 and Postgres in one statement," not "replace the warehouse."
Key specifications
- Architecture: distributed MPP over connectors
- Billing: Galaxy credits; self-managed is infra + support
- Latency / concurrency: cluster-sized; Warp Speed optional
- Ecosystem: wide connectors; you operate or pay Galaxy
Pros
- Federation without copying every source into Snowflake
- Galaxy free forever: 3 clusters
- Open Trino if you want to self-host
Cons
- Federation is not a warehouse: no single storage layer, weaker table services
- Credit prices stack with cloud VM cost
- Cluster ops if you are not on Galaxy
- Easy to accidentally scan an expensive source
Pricing: Galaxy Free $0. Pro from $0.50/credit. Enterprise from $0.75. Mission-Critical from $1.00. New customers: $500 trial / 30 days. (Starburst pricing)
10. PostgreSQL: Best as coexistence, not as a Snowflake replacement
Postgres is an OLTP database. It is in this list because teams try to make it the warehouse, then page the on-call. Keep it for app state. Offload analytics.
pg_duckdb embeds DuckDB in Postgres and can be up to 1000× faster than native Postgres on some analytical queries, less where indexes already exist. Run that on a replica. MotherDuck speaks the Postgres wire protocol, so existing BI clients can be repointed without rewriting SQL.
Key specifications
- Architecture: scale-up / host-bound
- Billing: instance
- Latency / concurrency: fine for OLTP; host-limited for OLAP
- Ecosystem: everything already talks to it
Pros
- You already have it
pg_duckdbis a cheap on-ramp- Wire-protocol compatibility eases offload
Cons
- Row store; wide scans hurt
- A heavy analytical query can stall the app database
- Not a cloud warehouse; no scale-to-zero analytics tier
pg_duckdbon primary is a good way to starve OLTP
Pricing: Open source. You pay the instance (and the replica, if you isolate analytics).
What if we are a startup or we need embedded analytics?
Startup and embedded analytics are a real slice of demand, with different billing and isolation constraints than enterprise batch.
- Startup / lean team: Cloud data warehouse startup guide
- Embedded / customer-facing: Best cloud data warehouses for embedded analytics
- Broader roster and TCO framing: Top 10 data warehouse platforms for 2026 and data warehouse TCO
Short version: startups usually want per-second billing and a local engine (MotherDuck, or ClickHouse if the product is an event dashboard). Embedded wants isolated compute so tenant A cannot drown tenant B. Snowflake's shared warehouse plus 60-second floor is the opposite of that.
How do you migrate off Snowflake without a big-bang rewrite?
- List the expensive, bursty workloads. Interactive BI, embedded product analytics, AI-agent SQL. Leave heavy, steady ETL on Snowflake until you have a reason to move it.
- Clone the schema, not the history. External tables / Iceberg / Parquet in the object store you already use. Do not start with a full unload unless you must.
- Run in parallel for 1–3 months. Same queries, two bills, two latency traces.
- Repoint clients. Postgres wire (MotherDuck), JDBC/ODBC, or a semantic layer. Change the connection string before you change the SQL.
- Turn Snowflake warehouses down, do not delete the account. Keep it for the batch jobs that still fit. Many "migrations" are offloads.
Timeline that matches how teams actually do this: week 1–2 identify and extract; week 3–4 prototype on a free tier; month 2 parallel run; month 3 decide what to keep.
How should you benchmark a Snowflake alternative?
Vendor TPC numbers are not your dashboard. Run your queries.
- Cold and hot. First query after idle (this is where 60-second floors and multi-minute resumes show up) and the 10th query on a warm cache.
- Concurrency. 1, 10, and 50 simultaneous users. A single-query bake-off hides noisy-neighbor failure.
- Cost per 1,000 queries, not cost per hour. Hourly rates hide the resume minimum.
- Bytes scanned vs rows returned. BigQuery and federated Trino punish sloppy
SELECT *. - Same data, same region. Copy a day of fact tables to each candidate. Do not compare Snowflake-on-warm-cache to a cold S3 scan.
If you cannot spend two weeks on that, at least run the ten queries that generate 80% of the Snowflake bill.
Start using MotherDuck now!
FAQS
Yes. MotherDuck bills per second ($0.60–$24.00 per compute-hour, Pulse through Giga) with no warehouse sizes and scales to zero when idle. Firebolt also bills per second and auto-stops engines. Snowflake and Redshift Serverless both keep a 60-second minimum on resume, which is the idle tax these newer models remove.
MotherDuck's Dual Execution runs SQL across a local DuckDB process and the cloud in one statement. ClickHouse and Trino can be self-hosted on a laptop or a VM. Snowflake, BigQuery, Fabric, and Redshift have no local engine; every prototype hits the cloud.
ClickHouse is the usual pick for raw sub-second scans on event data. MotherDuck is the pick when you also need per-user isolation (~100 ms Duckling spin-up) and per-second billing. ClickHouse Cloud resumes can take minutes, so many teams leave it warm and give up scale-to-zero.
MotherDuck isolates compute per user (Hypertenancy) so one tenant cannot starve another, with Ducklings starting in about 100 ms. ClickHouse handles extreme QPS on a hot cluster. BigQuery scales with slots but will queue. Snowflake auto-scales warehouses, then bills the 60-second floor on every resume.
MotherDuck exposes MCP so an agent can pull schema context and emit SQL with ~100 ms compute start. Databricks is the better fit when the agent is one piece of a Spark/ML platform. BigQuery is the GCP/Vertex path. Snowflake can do this; spin-up and idle billing are the tax.
Pick the bursty workloads that hurt on Snowflake. Prototype on a free tier (MotherDuck 10 hours of Pulse compute, BigQuery 1 TiB, Dremio $400, Firebolt $200, Starburst $500). Run in parallel for 1–3 months. Compare latency, dollars, and failure modes. Then expand or stop.
No. Redshift Serverless is the AWS-native alternative: $0.375/RPU-hour with a 4-RPU minimum ($1.50/hour base). It still has a 60-second minimum, so it does not fix the idle-tax problem. MotherDuck, Firebolt, and ClickHouse Cloud also run on AWS if the billing model matters more than staying inside IAM.
You do not migrate 20 TB on day one. Externalize the large facts (Iceberg/Parquet), move the 40 dbt models in dependency order, and keep Snowflake as the source of truth until the parallel run is boring. Federation (Starburst/Trino or Dremio) can cover the models you have not moved yet.
ClickHouse, Trino, Postgres, and Firebolt Core (free, self-hosted, closed-source) can be self-hosted. MotherDuck, BigQuery, Fabric, and Redshift Serverless cannot. Dremio Enterprise and Starburst self-managed exist if you need the engine in your VPC. Self-hosting trades license cost for a DevOps team you have to staff.
Replay the ten queries that dominate the bill, cold and hot, at 1 / 10 / 50 concurrent users, in the same region, on a day's data. Score cost per 1,000 queries and p95 latency. Ignore vendor TPC slides. Two weeks of that beats another architecture review.
No. Postgres is the OLTP system you keep. Use pg_duckdb on a replica for light OLAP, or offload to a columnar warehouse such as MotherDuck. Pointing a BI tool at primary Postgres is how application latency becomes a warehouse problem overnight.
MotherDuck's free Lite tier includes 10 GB of storage, 10 hours of Pulse compute per month, and a 3-user cap. ClickHouse OSS, DuckDB, and Postgres are free software. Self-hosting still costs you VMs, disks, and the people who run them. Free software is not a free warehouse.
No. Move the bursty, interactive workloads first — dashboards, embedded analytics, AI-agent SQL — and leave steady batch ETL on Snowflake. Run both for 1–3 months on the same queries. Many teams never fully leave; they just stop paying the 60-second idle tax on the short queries.
If the pain is idle warehouses and interactive latency, start with MotherDuck. If the pain is event-scan speed, start with ClickHouse. If the constraint is "we are not leaving this cloud," pick Fabric (Azure), BigQuery (GCP), or Redshift Serverless (AWS). If the constraint is "do not copy the data," pick Dremio or Trino/Starburst. Keep Postgres. Keep Snowflake for the batch it is good at until the parallel run says otherwise.
