§ Price vs Performance

Security can't afford to choose.

Every platform forces a trade-off — visibility vs. budget, retention vs. speed, AI vs. cost. LogSeam is the first one where security teams don't have to pick. Here's how we do it.

Why it matters

In security, the cost model is the threat model.

When the platform punishes you for keeping data, your detection coverage shrinks with your budget. When search is slow, MTTR grows. When AI is a separate bill, agents become a special-occasion tool instead of part of the loop. Every dollar you spend the wrong way is a gap an attacker uses.

Coverage = cost

Per-GB SIEMs force you to choose which sources to keep. Every source you drop is a blind spot — and attackers love blind spots. Detection coverage should be a security decision, not a procurement one.

Speed = MTTR

Slow queries don't just frustrate analysts — they extend the dwell time of every incident. A 30-minute query is a 30-minute extension of an active breach. Sub-second search compresses the entire response loop.

Retention = recall

APT campaigns unfold over months. If your platform deletes data after 30 days, you can't reconstruct lateral movement, locate patient zero, or answer the questions your incident report needs. Unlimited retention isn't a luxury; it's a forensic requirement.

AI = leverage

If AI is a separate, metered, bolted-on service, your team uses it sparingly. If AI is part of the platform — running on the same lake, sharing the same audit trail — it becomes part of how the SOC actually works. The cost model decides whether agents are everyday tools or occasional curiosities.

How we save

Every opportunity. Pulled.

Cheap doesn't happen by accident. LogSeam looks at every layer of the stack and asks the same question: where can we strip cost without losing fidelity? Five answers.

Compression

Data the way it should be stored.

Raw JSON in, columnar Parquet out — typically 10× smaller on disk, with bloom filters and page indexes baked in so queries skip what they don't need.

Object storage

S3-class as the primary tier.

No hot/warm/cold gymnastics. The lake lives on object storage at cents per gigabyte. Every byte you keep stays queryable forever — for the cost of an archive.

Dynamic compute

Scale to zero. Burst to hundreds.

Dynamic nodes spin up per query and dissolve on idle. You pay for the compute you actually use — not the cluster you provisioned for the worst case.

Caching

Files cached for real-time analysis.

Hot partitions and frequent query plans stay cached on always-on nodes, so re-scans cost milliseconds instead of dollars. Same lake, sub-second answers when you need them.

AI embedded

Not a bolt-on.

Agents run on the same lake, the same orchestrator, the same governance harness. No separate AI tier, no separate vendor invoice, no per-call margin. The cost is one line you understand.

What makes us different

You compose the compute. That's never been on the menu.

Every other platform locks you into one compute pattern. Splunk and Elastic are tuned for low-latency search but don't fan out for analytics. Spark and Snowflake aggregate beautifully but aren't built for sub-second incident work. ClickHouse is fast on cold data but isn't a data lake. Databricks is a lake but isn't a SOC.

LogSeam gives you always-on search nodes, dynamic burst nodes, aggregation nodes for heavy analytics, and AI agents — all on the same open data lake, all under one governance harness. You pick the shape of compute for the work in front of you. No other security platform offers that.

	Always-on search	Dynamic burst	Aggregation	Open data lake	AI native
LogSeam	✓	✓	✓	✓	✓
Splunk	✓	—	limited	—	bolt-on
Elastic	✓	limited	limited	—	bolt-on
ClickHouse	✓	—	✓	partial	—
Spark	—	✓	✓	✓	—
Snowflake	limited	✓	✓	partial	bolt-on
Databricks	limited	✓	✓	✓	bolt-on

Directional, based on common deployments. Every platform on the list is good at what it's built for — none was built to be all of these things at once, for security, with AI agents as a first-class citizen.

What that buys you

In practice.

SOC operations

Triage on always-on search nodes for sub-second alerts. Investigate across years of retention on dynamic burst nodes when you need depth. Same query language, same lake, same analyst surface.

Threat hunting

Aggregation nodes crunch the heavy CTEs and window functions that hunts actually need — campaign reconstruction, lateral-movement timelines, UEBA-style baselining — without renting a separate analytics warehouse.

Compliance & audit

Seven years of logs at object-storage rates. Audit exports off the same data your detections run on — no parallel archive to reconcile.

AI-driven response

Agents are part of the platform, not a separate bill. Triage, hunt, and respond at the price of the platform — not at "intelligent" tier markup.

See the math for your environment.

We'll size the node mix to your actual ingest volume and query pattern, and walk the bill end to end — pass-through and per-node, no surprises.

See pricing → Talk to sales