Observability Data Lake

Every signal in one store, inside your own cloud.

Logs, metrics, traces, events, RUM sessions, profiles and LLM telemetry land in the same columnar lake, correlated by design rather than stitched together during an incident. You set the retention. You hold the keys.

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Three problems the architecture solves.

When logs, traces, metrics and RUM live in four systems, every incident starts with the same tax: someone has to piece the story together across four tabs while it is still on fire.

Cost, governance and correlation are usually treated as three separate negotiations with a vendor. They are all consequences of where the data sits and how it is stored.

Pay for capacity, not per signal

Per host, per metric, per gigabyte and per seat all grow with your infrastructure whether you ever query that data or not. Kloudfuse prices on one axis, terabytes ingested, so storing another dimension is an engineering decision rather than a budget one.

Your data, your keys, your boundary

Production telemetry never leaves your VPC. Customer-managed encryption keys across AWS, Azure and GCP, access control at org, team and per-user level, and every admin action logged and exportable to your SIEM.

One incident, one investigation

Every signal is written into the same store, so a frontend RUM error and the backend trace behind it are correlated where they land. No cross-tool stitching, no exporting one system to explain another.

One store underneath everything you already do.

Ingest is OpenTelemetry-first and accepts the agents you already run. Everything below it, including the query surfaces and the AI layer, sits inside the same boundary and the same access controls.

Kloudfuse observability data lake architectureLogs, metrics, traces, events, RUM, profiles and LLM telemetry enter an ingest and normalize tier inside your VPC and land in one unified observability data lake, which holds them in the lake with retention per team, app or stream, archives them to your own object storage, and hydrates them back on demand filtered by time range and tag. Query surfaces and the AI layer read the same store and feed experiences such as dashboards, alerts and investigations, all under one governance layer of customer-managed keys, RBAC, SSO, audit logs and retention policies.KLOUDFUSE OBSERVABILITY DATA LAKESIGNALS INlogsmetricstraceseventsRUMprofilesLLMYOUR VPCIngest and normalizeOpenTelemetry-first · existing agents · Prometheus remote write · cloud services · schema mapping · enrichment · rate controlscrubbingUnified observability data lakeone columnar store · every signal · high cardinality kept, not sampledIN THE LAKEqueried directlyretention per team, app or streamARCHIVEyour object storagelong retention, your bucketHYDRATEfiltered, on demandby time range and tagQuery surfacesPromQL · LogQL · TraceQL · FuseQL · GraphQL APIGrafana-compatible dashboards query the same storeAI layerMCP server · LLM observability · Dexter drafts, you decidesame lake, same RBAC, same retention as your engineersEXPERIENCESdashboards · alerts and SLOs · investigations · APIs and webhooks · your own analyticsGOVERNANCEcustomer-managed keys · RBAC org / team / per-user · SSO · audit logs · retention policies

Storage, query and the AI layer all sit inside one governance boundary. Nothing leaves your VPC.

Explore the Self-SaaS architecture

Built for the workloads other platforms ask you to sample.

Cardinality, retention and query language are where an observability bill quietly turns into an engineering constraint. On this architecture each one is a decision you make, not a tier you buy.

High cardinalityEvery dimension. Stored, not sampled.

Kubernetes generates cardinality faster than any pricing model built to punish it. Every pod, namespace, replica and label combination is a dimension somebody will eventually need at three in the morning, and the platform that drops them is the platform that fails that morning.

  • Cardinality is bounded by policy rather than by pricing tier, and the platform scales horizontally as series volume grows.
  • No custom-metric tax, so nobody has to argue about whether a label is worth keeping.
  • Log patterns are fingerprinted at ingest, so repetitive lines cluster into signal instead of arriving as volume.
  • Multi-resolution rollups keep dashboard queries fast without discarding the underlying points.
Kloudfuse metrics cardinality explorer: 205 matching metrics, 1,931 of 87,066 series, and every label value kept and queryable

RetentionYour data, your retention, your infrastructure.

Retention policy is usually written by whoever priced the contract. Here it is written by whoever has to answer the audit, because the storage is in your account.

  • Set retention independently for any stream, team, or application. Metrics, logs, traces and events, or individual teams and applications, can each run on their own retention clock.
  • Attach custom policies by label, so different applications, teams, production environments, and staging environments can have different retention without splitting the platform.
  • Older logs archive to your own object storage, where long retention stops competing with query performance.
  • Hydrate an archive back filtered by time range and tag, so an audit pulls its window rather than terabytes.
  • Compliance windows are yours to set, and they do not expire because a plan changed.
Kloudfuse Logs fingerprints view clustering 273,310 info and 4,325 error lines into a handful of patterns per source

Open queryQuery it in the language your team already knows.

A proprietary query language is a hiring problem and a lock-in problem at the same time. Kloudfuse supports the open ones your engineers learned somewhere else, and adds its own only where they run out.

  • PromQL for metrics, LogQL for logs and TraceQL for traces, all against the same store.
  • FuseQL for the log analytics the open languages cannot express, including parsing, aggregation, pattern extraction and scheduled search.
  • A GraphQL API for anything you want to drive programmatically.
  • Grafana-compatible, so the dashboards your team already maintains keep working against Kloudfuse as a data source.
PromQLLogQLTraceQLFuseQLGraphQL API
Kloudfuse Logs queried in code mode with a source and span filter, showing matched documents and correlated log lines

In production. Inside customer clouds.

Three different industries, three different reasons, one architecture underneath.

Cloud security

Observability at hyperscale, inside the customer cloud

  • 300+ TB of telemetry a day, and growing
  • 78 engineering teams on one queryable lake
  • More than 30 tools consolidated into one
  • Deployed inside the customer environment
Intelligent automation

From vendor lock-in to open standards

  • Consolidated off legacy SaaS observability
  • Predictable, capacity-based cost model
  • 86% reduction in mean time to resolution
  • High-cardinality metrics kept without a premium tier
Healthcare data

Unified observability for healthcare engineering

  • Hundreds of users across engineering, DevOps, QA and support
  • 23% reduction in mean time to resolution
  • 50% fewer customer-reported incidents
  • Data residency preserved inside the customer VPC
Read customer stories

What engineers ask before they ingest a single byte.

What is the underlying storage?

A columnar, compressed store built for high-cardinality telemetry across logs, metrics, traces, events, RUM, profiles and LLM telemetry. Ingest is OpenTelemetry-first and also accepts Prometheus, the Datadog agent, Fluent Bit, Beats and OpenTelemetry SDKs.

What is the cardinality limit?

Kloudfuse is built for high-cardinality workloads and scales horizontally as series volume grows. Cardinality is bounded by the policy you set, not by a pricing tier.

Can we keep using Grafana?

Yes. Grafana-compatible workflows mean existing dashboards, alerts and data sources query the unified lake without rework.

What is FuseQL?

FuseQL is Kloudfuse's query language for log analytics that open standards don't cover: parsing, aggregation, pattern extraction and scheduled search. It runs against the same lake as PromQL, LogQL and TraceQL, under the same access controls.

Can we bring dashboards and alerts from our current vendor?

Yes. Dashboards, alerts and saved views convert onto open standards without writing code, so the panels your on-call team knows are there on day one and the thresholds someone tuned after a bad quarter are still the thresholds.

Can retention vary by team or environment?

Yes. Retention is set per stream, and any team or application can carry its own retention policy independently. Custom policies attach to records by label, so production and staging, or different services, are retained on different clocks.

Does the AI layer query the same lake?

Yes. Agents and MCP-connected tooling query the same governed store as your engineers, under the same access control, retention and audit policy. The telemetry never leaves the data plane.

What counts as LLM telemetry?

Prompts and completions, token usage, model latency and errors, agent traces and tool-call telemetry, ingested as first-class signals alongside logs, metrics and traces rather than into a separate product.

Does it run on more than one cloud?

Yes. AWS, Azure, GCP and selected private or on-prem environments. The lake runs where your telemetry already lives.

Independently verified Know more
SOC 2 Type IIIndependent CPA examination
27001Certification in progress
140-3 validatedCMVP #5186 · #5209
GDPREU data protection compliance

Audited by third parties, not asserted by us. Deployment inside your own VPC means residency and data-handling requirements are governed by your controls, not ours.

Bring an incident. We'll bring the platform.

Thirty minutes on your telemetry. The cause, before the call ends.

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