VEGALAKE
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The context-native development + data platform

Build with your data, code and context.

Build applications, pipelines and AI agents with the data, code, ownership and policies they need. Run them together in Vegalake, from prototype to production.

Build with Vegalake Read the docs
Shared data, context and executionFor solo builders, product teams and global organizations
VEGALAKE / PLATFORM DEMO
EXPLORE VEGALAKE

See Vegalake
at work.

01ContextPEOPLE · POLICY · MEANINGVegaGraph
×ExecutionWORKFLOWS · AGENTS · ACTIONVegaFlow
×IntelligenceSQL · SIGNALS · ANSWERSVegaDB

A renewal at risk. A deployment gone sideways. A new data product. An agent with a difficult request.

CHOOSE A ROLE TO GET STARTED.CLICK THROUGH EACH SCENARIO.
CONNECTED BY CONTEXTInteractive illustration · sample data
Explore the platform
OBSERVE◆MODEL◆RETRIEVE◆ACT◆LEARN◆IMPROVE◆

The platform / three connected products

One Context Lake.
One durable runtime.
One analytical engine.

A pipeline, its source data, its owner and the applications that depend on it belong in the same picture. Vegalake connects them so teams and agents can look up dependencies, check permissions and trace results to their source.

Context plane

VegaGraph

The organizational Context Lake: a typed, connected model of data, software, infrastructure, people, policy and business meaning.

Explore VegaGraph
  • Typed organizational model
  • Impact and lineage
  • Context-aware retrieval
  • Evidence with every change
context plane.vl
revenue.kpi
  ← computed_from ← fact_orders
  ← written_by    ← orders_model
  ← owned_by      ← growth_data

Execution plane

VegaFlow

One durable runtime for ingestion, transformations, long-running workflows and native AI agents—built visually or in code.

Explore VegaFlow
  • QuickFlows and DAGs
  • Durable state and recovery
  • Native and visual AI agents
  • Human-in-the-loop controls
execution plane.vl
source("postgres.orders")
  .incremental("updated_at")
  .load("vegadb.raw.orders")
  .schedule("*/10 * * * *")

Data + intelligence

VegaDB

SQL, vector and streaming workloads on a massively parallel engine, with flexible storage and compute placement.

Explore VegaDB
  • Distributed SQL
  • Vector retrieval
  • Realtime operational data
  • Flexible managed or open storage
data + intelligence.vl
SELECT region, sum(amount) AS revenue
FROM lakehouse.sales.orders
GROUP BY region
ORDER BY revenue DESC;

How the products work together

Connect the inputs.
Run the work. Check the result.

01

Observe

Connect data sources, repositories, services, owners and policies.

02

Model

VegaGraph records their types and relationships so teams and agents can query them.

03

Act

VegaFlow runs pipelines, applications and agents durably using the context available at execution time.

04

Learn

VegaDB stores results and run history. VegaGraph links them to the work that produced them.

From prototype to production

Start small. Add controls as you grow.

Start with a prompt, a visual flow, SQL or Python. Add approvals, access policies and deployment controls when the work moves into production. Your team keeps the same tools and execution history.

Flexible authoringUse a canvas, SQL, Python or APIs while sharing one execution history.
Progressive controlAdd identity, approvals, policy and promotion when the work demands them.
Context + evidenceEvery run reads organizational context and writes back what actually happened.
Start with the architecture

Talk to the team

What do you want to build?

Tell us about a query, pipeline or application you are working on. We can help you plan the first version.

Talk to an engineer