Quernos is one endpoint in front of Snowflake, Databricks, BigQuery, Athena, Redshift, and Trino. Every query is weighed on live cost models — performance and price together — and routed to the engine that wins it, with routed answers checksum-verified against the engine you left. Cheaper is only cheaper if the answer is right. One copy of your data, in open formats, in your account.
Run the same work on two engines and the winner flips query by query — one takes the short interactive work, the other takes the heavy joins. The gap between them is real money, and it compounds silently on every run. Committing every query to one vendor means systematically overpaying on the queries it loses.
"ETL on Databricks, BI on Snowflake" — decided in a meeting, revisited never. The per-query winners inside each workload are invisible, so the losses compound silently on every run.
Quernos maintains live models of what each engine actually charges and how fast it actually runs — your queries, your data — routes each query to the engine that wins it, then verifies the winner's answer agrees with the engine you left. The decision is re-weighed as vendors change prices, hardware, and optimizers.
We ran the industry-standard analytics benchmark across the major engines ourselves — same queries, one copy of the data, submitted through our own MVP, zero tuning. Putting each query on its winning engine beat committing to either vendor — on cost and on time. The full per-query ledger is being prepared for publication. Until then, the numbers stay in the vault.
These were untuned first runs — the room above that floor is ours to take. Design partners see the full ledger and methodology before anyone else.
Grant read access to your query history and we hand you a savings report: what you spent, what each query would have cost elsewhere, and the number you'd save. No routing trust required — the report is free and yours to keep.
Cost models learn your real query shapes across engines — cost, latency, correctness — and stay fresh as vendors change prices, hardware, and optimizers. Your data stays in your account, in open table formats every engine can read.
Point your tools at the Quernos endpoint. Each query executes on the engine that wins it; results are checksum-compared against the source engine on a sampled basis, and any disagreement falls back to your original engine — with the evidence logged.
The same query, run on two engines, must agree — checksum by checksum. When it doesn't, you find out from us, not months later from silently wrong dashboards. Disagreements quarantine the route, fall the query back to your original engine, and are preserved as auditable evidence. Routing without verification is a bet; Quernos doesn't ask you to make it.
Not per-workload — per query. Cheap scans go where scans are cheap; heavy joins go where joins are fast. Latency SLAs pin speed where it matters, and the scale weighs both axes at once — the engine that wins a query on performance is often the one that wins it on price too. Policies let you pin, exclude, or prefer engines per team and per budget.
An engine throttles, queues, or goes down — your query doesn't. Quernos reroutes around vendor incidents automatically, making your analytics more reliable than any single warehouse can be.
Every query gets two prices: what it cost, and what it would have cost on the engine you left. Logged per query, immutable, exportable. Your CFO's favorite screen — and the receipt behind every invoice we send.
Open table formats matured in 2023–26 — Snowflake, Databricks, Google, and AWS now read the same Iceberg and Delta tables, and they keep improving it. The data layer is shared; pricing is not. Existing tools optimize inside one vendor or route out of a single warehouse. We found no shipping product that routes per-query across the commercial warehouses and verifies the answers across engines.
And because your tables never move, neither does your leverage: adding an engine is a connection, dropping one is a policy change, and leaving Quernos itself is just pointing your tools back. Lock-in isn't something we reduce — it's something the architecture makes impossible.
| WORKS ACROSS ALL YOUR ENGINES | ROUTES PER QUERY | VERIFIES RESULTS ACROSS ENGINES | AUDITS EVERY DECISION | |
|---|---|---|---|---|
| Warehouse optimizers | No — tune one vendor | — | No | Partial |
| Single-vendor routers | No — route out of one warehouse | Yes | No | Partial |
| Federated engines | Is itself another engine | — | No | No |
| Quernos | Yes — six engines | Yes | Yes | Yes |
COMPETITIVE SCAN OF SHIPPING PRODUCTS AND PUBLISHED RESEARCH, JUNE 2026 — METHODOLOGY AVAILABLE ON REQUEST.
Savings are measured per query against a logged counterfactual — what the same query would have cost on your original engine — and every measurement is in your audit export. If the ledger says zero, the invoice says zero. Annual floor and cap agreed up front, so your exposure is bounded in both directions.
Any workload that can run on multiple vendors — queries today; ingestion, transformation, and inference next — deserves to be weighed before it's bought. Quernos' cost models, routing policies, and verification are workload-agnostic by design. We start where the spend is largest and the data is already shared: the tens of billions in cloud analytics now converging on open table formats.
Teams spending meaningfully on cloud warehouses, working directly with the founders. Your workloads shape the routing and verification roadmap; you see your savings analysis before anyone else. It starts read-only — days, not a migration.
OR WRITE TO OMER@QUERNOS.COM
We're a team with database-internals and distributed-systems depth, a working six-engine product, and a benchmark corpus that compounds with every customer. The deck covers the rest — including the honest risks.