The modern data stack is a dozen tools. FluxOS is one.
Warehouse, streaming, transformations, a semantic layer, git-style branching, in-engine ML, and governance — collapsed into a single Postgres-compatible platform. One system. One language. No orchestrator, no glue, no 2 a.m. pages.
Your data stack is a dozen systems pretending to be a platform.
Ingestion
Fivetran · Airbyte · Debezium
on-call: connector breakage
batch loads / CDC
Warehouse
Snowflake · BigQuery
on-call: credit burn
dbt run on Airflow
Transform
dbt · Airflow DAGs
on-call: 3 a.m. DAG failures
exposures / metrics
Semantic + BI
Cube · Looker · Tableau
on-call: metric drift
reverse ETL
Your app
reads data 8–90s stale
on-call: it's always the data
A vendor for every verb. A bill for every box.
Ingest in one tool, store in a second, transform in a third, define metrics in a fourth, visualize in a fifth. Then bolt on Kafka + Flink when someone needs real-time, a feature store when someone needs ML, and a git-for-data layer when someone needs to experiment safely. That's the dozen.
Every box is its own wire format, credential set, upgrade cycle, and pager. Data crawls from write to dashboard in 8 seconds on a good day, 90 on a bad one. The cloud bill clears five figures before a single user sees a feature.
You're not building a product. You're operating a supply chain.
THE COLLAPSE
One system for the entire data lifecycle.
FluxOS is the whole path above — ingest, store, transform, stream, branch, model, and govern — in a single platform that speaks Postgres. The row your app writes is the row the dashboard reads, the model scores, and the audit log records. No copies. No seams. No glue.
01
Store & serve, on one copy
Transactional and analytical on the same tables. ACID, MVCC snapshot isolation, and pgwire on port 5432 — so psql, Prisma, SQLAlchemy, or any ORM just connects — over columnar storage with time-travel built in. The app writes and the dashboard reads the same data. No ETL, no reverse ETL, no warehouse sync.
pgwireMVCCcolumnartime travel
02
Move & transform, in-process
Change data capture, pipelines, and continuous materialized views that stay fresh as rows arrive — no separate streaming cluster, no checkpoint files, no TaskManagers. Kafka and Kinesis connectors when you need to talk to the outside world. The transform layer lives where the data does.
CDCcontinuous MVspipelinesKafka / Kinesis
03
Build & govern, built in
Git-style branches for safe experiments, in-engine ML with lineage, a semantic metrics layer, and row/column security with a tamper-proof audit log and GDPR cascade. Four more products you don't have to buy, integrate, or reconcile — they're already in the box.
data branchingin-engine MLsemantic layergovernance
NOT JUST AN ENGINE
It ships with the whole workbench.
FluxOS isn't a headless database you wrap in five SaaS tools. Open a browser and you're in a full data operating system — explore, query, build, model, branch, monitor, and govern, all in one place, on the same data.
Query editor
Write Flux or SQL with schema autocomplete, syntax highlighting, and a built-in profiler that shows the execution plan node by node.
Notebooks
Python, Flux, and SQL cells in one notebook with managed kernels and package install. Explore, chart, and export to .ipynb.
Branches
Spin up a copy-on-write branch of the whole database in milliseconds. Diff against main, experiment safely, merge atomically.
Semantic layer
Define governed, versioned metrics once. Non-technical users explore them by point-and-click — no Flux, no metric drift, one source of truth.
AI & ML
Train models, forecast time series, and detect anomalies in-engine — with model lineage. Wire an LLM for natural-language insights.
Data engineering
Pipelines and DAG workflows, materialized views, connectors, triggers, CDC streams, visual lineage, and column-level data-quality profiling.
Dashboards
Flux-backed widgets are first-class catalog objects. A no-code chart wizard turns a table + a measure into a live dashboard in seconds.
Monitoring & advisor
Live queries, slow-query log, latency percentiles, and alerts — plus an advisor that explains plans and suggests indexes and rewrites.
Governance & cost
Roles, row/column security, masking policies, a tamper-proof audit log, GDPR erasure, and per-query / per-user cost limits — all in one console.
Sign up and you land straight in the workbench — no install, no setup.
ONE LANGUAGE
One language for the entire data layer.
The dozen-tool stack is also a dozen-dialect stack: SQL in the warehouse, Jinja in dbt, YAML in the semantic layer, Python in the feature store, another SQL for the stream processor. FluxOS speaks one — Flux. The same pipes that run a query define a materialized view, a metric, a model, and a branch. Learn it once; operate the whole layer.
Your warehouse · SQL
SELECT customer_id,
SUM(amount) AS revenue
FROM orders
WHERE status = 'paid'GROUP BY customer_id
ORDER BY revenue DESC;
FluxOS · Flux
from orders
|where status == "paid"|group customer_id {
revenue: sum(amount)
}
|sort -revenue
dbt + Materialize · SQL · Jinja · YAML
-- models/top_products.sql
{{ config(materialized='incremental') }}
select product_id,
sum(amount) as revenue
from {{ ref('orders') }}
{% if is_incremental() %}
where created_at > (selectmax(created_at)
from {{ this }})
{% endif %}
group by product_id
create metric paid_revenue asfrom orders
|where status == "paid"|sum(amount)
Feature store + training · Python
import pandas as pd
from sklearn.ensemble import GradientBoostingClassifier
df = warehouse.read("select * from customers")
X, y = featurize(df), df["churned"]
clf = GradientBoostingClassifier().fit(X, y)
feature_store.register("churn", clf) # + serving
FluxOS · Flux
train model churn
from customers
|features { tenure, plan, monthly_usage }
|label churned
from customers |predict churn
git-for-data · lakeFS CLI
# a separate system, just to branch
lakectl branch create \
lakefs://repo/experiment-q4 \
--source lakefs://repo/main
# then re-point dbt, BI & configs
# at the new branch by hand…
Append-only NDJSON, hash-chained. SOC 2 evidence-as-a-feature. Exportable to your SIEM.
GDPR cascade with lineage
One DELETE FROM users cascades through every dependent table, MV, and stream — and records the chain.
Encryption at rest
AES-256-GCM. Per-key envelopes. Key rotation. TLS at every public surface.
Every item above maps to a feature flag, a source file, and one of 8,900+ tests in the codebase. We can show you the line.
IT'S READY
Built in Rust. Hardened by audits. Live in your browser in 60 seconds.
60s
From signup to first query
Sign up free and land in a managed workspace — no install, no cluster, no setup. Run a query in your browser or connect over the Postgres wire protocol.
$0/mo
Free tier that scales to zero
Idle costs nothing — you pay only for the seconds your queries actually run. Upgrade to Pro or a Dedicated node the moment you need more.
Zero
External services to wire up
No Kafka. No ZooKeeper. No Flink cluster. No separate MV engine. No orchestrator. One managed platform is the whole stack.
BUILT FOR THE BUYER WHO ASKS HARD QUESTIONS
Security and governance aren't add-ons.
Every feature below is in the core platform, not behind a paywall or a separate sidecar. Bring FluxOS to a SOC 2 audit, a HIPAA review, or a GDPR DPA without bolting on three more vendors.
Tamper-protected audit log
Append-only NDJSON, hash-chained, exportable to your SIEM. Designed for SOC 2 evidence and incident forensics.
GDPR cascade with lineage
One DELETE FROM users cascades through every dependent table, materialized view, and stream — and records the chain in the audit log.
RBAC + JWT + API-key TTL
Built-in roles and permissions, brute-force lockout, password policy, key expiry. No external IdP required to ship v1. SSO / SAML on Enterprise.
Encryption at rest + in transit
AES-256-GCM with per-key envelopes and rotation. TLS at every public surface: pgwire-over-TLS, HTTPS for the API and dashboard.
Reading these features straight from the source code? Same. Every line above maps to a file, a test, and a Phase commit in the public history.
PRICING
Free to start. Scale to zero. Pay for what you run.
Start free and scale to zero — idle costs nothing, with no minimums and no credits to pre-buy. Storage is one all-inclusive price: backups, time travel, and branches ride along free.
Free
$0
A real managed workspace that scales to zero. For evaluating and small projects — pay nothing while it's idle.
Your own isolated, always-on node — no neighbors, no cold starts. The flat fee covers the hardware and all idle time; compute is billed only while your queries actually run.
Single-tenant, always-on node — self-serve, live in minutes
Node size XS $149 · S $449 · M $700 · L $1,100 /mo flat
Compute $0.25/worker-hour of measured query execution — idling is covered by the flat fee
Storage $50/TB-mo all-inclusive — backups, time travel, branches