Solve cost, control, and coverage with a first-mile control plane

Reduce SIEM & observability costs

Shape telemetry upstream before metered tools ever see it.

The pain

• •Licenses are based on ingest volume
• •Noise, duplicates, and verbose events swamp indexes
• •Retention windows drop from months to days

How LyftData helps

• •Filter redundant and noisy events before ingest
• •Mask PII upstream so masking logic is consistent
• •Enrich once and reuse everywhere
• •Route curated subsets into expensive tools, archive full fidelity cheaply

Standardize & centralize telemetry pipelines

Replace ad-hoc scripts and per-tool agents with signed Jobs.

The pain

• •Each region and team builds its own ingestion path
• •Masking and enrichment drift across clusters
• •No single place to audit who changed what

How LyftData helps

• •Model pipelines as Jobs (Input → Actions → Output)
• •Promote Jobs through environments with signatures and approvals
• •Let Workers enforce the exact same policy everywhere

Governed telemetry for security & compliance

Apply policy before data hits downstream tools.

The pain

• •Different masking rules per cluster or vendor agent
• •Auditors can’t see how telemetry was shaped
• •SOC teams drop data to stay in budget but lose coverage

How LyftData helps

• •Centralize masking, redaction, and tokenization in Actions
• •Use Run & Trace output as evidence for regulators
• •Archive full-fidelity copies to customer-owned storage
• •Clone curated streams to SIEMs while storing raw events

Vendor-neutral routing / tool mobility

Keep telemetry portable so tools can change without rewrites.

The pain

• •Observability or SIEM migrations require re-instrumenting everything
• •Vendors lock logic inside proprietary agents
• •Teams are stuck paying for tools they no longer love

How LyftData helps

• •Define pipelines once in LyftData
• •Use Channels to clone and fan out governed outputs
• •Swap downstream tools by updating Outputs, not agents

Long-term, low-cost retention

Keep full fidelity for years without bloating indexes.

The pain

• •Critical logs need to be stored for investigations/ML but indexes are expensive
• •Legacy archives are brittle and hard to replay
• •Data science teams need raw copies to train models

How LyftData helps

• •Use Channels to branch full-fidelity streams to cheap storage
• •Keep curated subsets feeding real-time tools
• •Replay archives into new tools without re-instrumenting sources