Solutions

Solve cost, governance, and routing — before ingest

LyftData helps Security, SRE, Platform, and Data Engineering teams rein in ingest costs, standardize pipelines, and keep telemetry and event data portable — before it hits downstream tools. The same model also supports governed datasets for analytics and AI/ML.

View Pricing →Open the Docs →

Solutions overview

Explore six patterns teams use to cut SIEM and observability spend, enforce governance, and prepare data for analytics and AI/ML — before ingest.

Reduce SIEM & observability costs

Shape telemetry before ingest so metered tools only get what matters.

Standardize & centralize telemetry pipelines

Replace ad-hoc scripts and per-tool agents with versioned, auditable pipelines.

Route & prepare data for AI/ML

Turn exports, documents, and events into model-ready datasets with policy built in.

Governed telemetry for security & compliance

Apply consistent masking, redaction, and auditability before ingest.

Vendor-neutral routing & tool mobility

Keep telemetry portable so tools can change without rewrites.

Long-term, low-cost retention

Keep a full-fidelity archive in your own storage for years, cheaply.

Reduce SIEM & observability costs

Shape telemetry before ingest so metered tools only get what matters.

Before LyftData

SIEM and observability licensing scales with raw volume, not signal.

Duplicates and verbose payloads swamp indexes and dashboards.

Teams cut retention from months to days when indexes get too expensive.

With LyftData

Drop redundant and noisy events before premium tools ingest them.

Apply consistent PII masking so policy never drifts.

Route curated subsets to SIEM/APM while archiving full fidelity cheaply.

Read the full solution →See integrations →

Standardize & centralize telemetry pipelines

Replace ad-hoc scripts and per-tool agents with versioned, auditable pipelines.

Before LyftData

Each team builds its own ingestion path and policies diverge by environment.

Masking and enrichment rules change between clusters and vendors.

It’s hard to prove who changed what and what ran in production.

With LyftData

Version pipelines and promote them through environments with a clear change history.

Run the same transformation and masking rules everywhere by design.

Validate outputs and capture lineage before changes reach production.

Read the full solution →View how it works →

Route & prepare data for AI/ML

Turn exports, documents, and events into model-ready datasets — with reproducibility and policy built in.

Before LyftData

Data arrives as exports, documents, and event streams with inconsistent schemas.

Teams maintain one-off scripts per dataset, environment, and model iteration.

Sensitive fields make sharing and training pipelines risky without consistent controls.

With LyftData

Extract, normalize, and protect data in one versioned workflow.

Generate model-ready features as part of the pipeline.

Route curated datasets to warehouses, storage, and APIs while keeping a raw archive for replay.

Read the full solution →Browse actions →

Governed telemetry for security & compliance

Apply policy before ingest so downstream tools stay compliant.

Before LyftData

Different vendor agents and clusters apply different masking rules.

Auditors can’t see how telemetry was shaped or which rules ran.

Teams drop data to stay in budget and lose visibility when it matters.

With LyftData

Apply masking, redaction, and tokenization consistently at ingestion time.

Use Run & Trace to show exactly what changed and what data shipped.

Clone curated streams to SIEMs while archiving full fidelity to owned storage.

Read the full solution →Open the docs →

Vendor-neutral routing & tool mobility

Keep telemetry portable so tools can change without rewrites.

Before LyftData

SIEM and observability migrations often require re-instrumenting pipelines.

Vendors lock critical logic inside opaque UIs and per-host configs.

Teams keep paying for tools they no longer prefer because switching is hard.

With LyftData

Define collection and shaping once, independent of any downstream vendor.

Clone and route governed streams to multiple tools in parallel.

Change destinations without redeploying agents.

Read the full solution →See integrations →

Long-term, low-cost retention

Keep a full-fidelity archive for years without bloating indexes.

Before LyftData

Storing everything in metered indexes makes long retention unaffordable.

Legacy archives are hard to replay and don’t preserve consistent shape.

Investigations and ML need full-fidelity copies, not just curated subsets.

With LyftData

Archive full-fidelity streams to cheap object storage.

Keep high-signal subsets feeding real-time tools without duplication.

Rehydrate archives into new tools without re-instrumenting sources.

Read the full solution →Open the docs →

Workflows

See workflow patterns teams run in production

Workflows show how teams apply LyftData end-to-end — from collection to shaping to routing and archive — before ingest.

Explore workflows →

Understand the architecture

Walk through Server → Workers → Jobs in detail.

Want to see what you can actually build?

Explore the capabilities unlocked by this model.

Ready to choose a plan?

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