Race 1 - Sort 1,000,000 floats
Trader dashboards, log viewers, market depth. Cloud-default: ship the data to a server, sort there, ship the result back. Datadog Pro at $23/host/month makes this a $276/user/year line item, and the data left the device. Hadron: sort on the user's own GPU, zero bytes out.
Race 2 - Filter and aggregate 1,000,000 sales rows
The BI dashboard query a Grafana / Snowflake / DuckDB-WASM customer runs all day. Cloud-default: ship every dashboard refresh to a warehouse, pay per credit. Snowflake compute at typical analyst load is roughly $30/user/month = $360/user/year. Hadron: filter + groupBy + sort on the user's device. Naive in-tab JS is faster at 1M rows because JS is fast on hot loops - but it can't scale to 10M+ without freezing the page, and Hadron is the only path that holds up at warehouse scale.
Race 3 - Find every "fox" in 2,000,000 characters of text
Log viewers, find-in-page, in-product search. Cloud-default: Algolia hosted search at $1 per 1K queries, ~5K queries/user/month = $60/user/year. Every query leaves the device. Hadron: two-phase pre-filter on GPU when the workload fits, a CPU literal-search path on CPU when it doesn't. Replaces a hosted SaaS with a static asset.
Race 4 - Multiply two 1024 x 1024 matrices
Inference, on-device training, AI completion re-rank. Cloud-default: ship the data to an LLM endpoint. OpenAI re-rank judge calls at $1/1K, ~1K calls/user/month = $12/user/year - and every prompt left the device. Hadron: Float32 precision-sufficiency check passes for this shape, dispatches to a WebGPU compute shader, zero bytes out, no API key required.
Annual savings tally
Each Hadron lane replaces a real, named SaaS line item. The tally below is the sum across the four races, per user, per year. Use the slider to scale to your deployment.
Numbers use 2026 public list prices. Hadron is a static CDN ship - storage + bandwidth is well under $0.50 per user per month at typical traffic, so the net saving is essentially the cloud line. See the Cost tab for the full breakdown including five workloads.
Three lanes, three sizes
Lane A is the naive main-thread baseline. Lane B is a vanilla WebGPU port that forces every call to GPU regardless of shape (forceGPU: true). Lane C is Hadron's adaptive scorer. The winner per row is highlighted; the dispatch decision Hadron made is shown alongside.
| Workload · size | Lane A · Main thread | Lane B · Vanilla WebGPU (force GPU) | Lane C · Hadron dispatch | Hadron picked |
|---|---|---|---|---|
| Sort - 200 floatstiny - GPU upload dominates | - | - | - | - |
| Sort - 50,000 floatsborderline - Worker often wins | - | - | - | - |
| Sort - 2,000,000 floatsheavy - GPU is the right call | - | - | - | - |
| Matmul - 32×32tiny - shader compile overhead | - | - | - | - |
| Matmul - 256×256borderline - precision matters | - | - | - | - |
| Matmul - 1024×1024heavy - GPU is the right call | - | - | - | - |
| Search - 10K chars, literalsmall corpus - CPU wins | - | - | - | - |
| Search - 2M chars, literalheavy - two-phase GPU wins | - | - | - | - |
| Search - 2M chars, regexGPU-hostile workload shape | - | - | - | - |
Bytes that left this browser session
Live counter. Press the cloud-default buttons to see what the bytes-out would be if each workload ran server-side instead. Press the Hadron button to confirm that the same job runs to completion without sending anything.
| Workload | Cloud sends out | Hadron sends out | Privacy delta |
|---|---|---|---|
| Sort 1M floatse.g. log-line ranking, market depth | 8 MB upload | 0 B | 100% |
| Pipeline 1M sales rowsfilter + groupBy + sort | 32 MB upload (PII in rows) | 0 B | 100% |
| Find-in-files 20K-file repofind-in-page, secret scanner | ~50 MB source code upload | 0 B | 100% |
| RAG over 5K documentssemantic search, support agent | ~120 MB embeddings + every query | 0 B (query stays local) | 100% |
| Email urgency classifieron-device training | Every email body + every label | 0 B (labels never leave) | 100% |
Monthly cloud-default spend (the bill Hadron displaces)
Public list prices as of 2026 (Datadog Pro, GitHub Advanced Security, OpenAI, Snowflake credits, Algolia). Drag the slider to scale to your deployment.
| Workload | Cloud-default monthly | Cloud-default annual |
|---|---|---|
| Real-time dashboardsDatadog Pro @ $23/host/mo | - | - |
| Repo secret scanningGitHub Advanced Security @ $49/committer/mo | - | - |
| AI completion re-rankOpenAI judge @ $1 / 1K calls, 1K calls/user/mo | - | - |
| In-browser analytical SQLSnowflake compute, ~$30/user/mo equivalent | - | - |
| Hosted searchAlgolia Build, ~$1 / 1K searches, 5K/user/mo | - | - |
| Total cloud-default spend Hadron displaces | - | |
Hadron's own commercial terms (enterprise licence, support contract, source-escrow options) are deliberately not listed here. They're scoped per deployment and discussed directly. The cloud-default column is the spend you are walking away from once Hadron replaces these workloads on the user's device.
navigator.onLine = false.
Does it work when X breaks?
Five failure modes that strike SaaS implementations regularly. Hadron has none of them because it does not depend on a server round-trip.
plane, train, SOC, air-gapped lab
key rotation, billing pause, security incident
429 storms during onboarding spikes
EU/UK/CA can’t leave region
Okta / Auth0 / Cognito downtime
Live offline-mode test
Press the button. The page overwrites window.fetch to throw for the duration of the test - any code path that needs a network call dies. Then it tries to (a) run a small workload through each of the four Hadron libraries and (b) reach five named SaaS endpoints. The libraries should all complete; every cloud endpoint should fail. The button restores the original fetch when you press Reset.