Transparent pricing. Nothing hidden.
Compute, storage, and AI are pass-through. You pay your cloud or your model provider directly, or we pass the actual bill through if we host the tenant. LogSeam charges one thing: per-node support.
Two components. That's it.
Your bill from LogSeam has exactly two parts. One varies with what you actually run; the other is a flat per-node line for our software and support. There's nothing else.
Compute · Storage · AI
The raw infrastructure that runs the platform: EC2-type compute, S3-class object storage, and AI model usage. You pay your cloud and model providers directly at your contract rate. If LogSeam manages the tenant, we pass the invoice through at cost, with no margin.
Per-node software & support
One flat rate per node for the LogSeam platform: the lake, the agents, the orchestrator, the UI, and the support tier you choose. Always-on nodes and dynamic nodes (auto-scaled per query) are priced separately. A node is any EC2-type compute node, any size.
Pricing simplified
Pick your perimeter.
LogSeam Managed
We host it. Cloud costs, and AI if you want us to hold the provider account, pass through on your invoice with no margin. Bring your own model keys instead and AI never touches our bill.
- LogSeam-managed cloud account
- Compute and storage at cost
- AI at cost, or your own model keys
- + per-node support
Your Cloud
You host it. Your cloud account, your AI provider contracts, your data residency rules. We charge only per-node support.
- Your AWS / GCP / Azure tenant
- Your existing AI vendor contracts
- Your security boundary
- Per-node support only
On-Premises
Your hardware, your data center, your air-gap. Self-hosted models, no outbound network requirement. Custom-scoped pricing.
The nodes that run it.
Five logical node types make up a LogSeam deployment. Each is composed of one or more compute nodes, from 8 cores up to 192 with memory to match, sized to your workload. Pricing is per compute node, regardless of size.
Ingest the firehose.
Parses incoming events, normalizes schemas, compresses to Parquet, and writes to the lake.
Scan the lake.
Distributed scans across the partitioned tables: the workhorse of day-to-day investigation.
Crunch the heavy queries.
Aggregator pattern for complicated CTEs, window functions, and large analytical joins.
Run the room.
Powers the UI, AI agents, Alerts, Incidents, Spaces, and Dashboards and every analyst-facing surface.
Drive headless.
Exposes every agent and dataset as native MCP tools for AI agents and external clients.
How many nodes do we need?
Sizing depends on the roles your deployment needs: the same five above, which are log processing, search, aggregation, master and MCP. Each scales independently based on ingest volume, analyst concurrency, workflow load, integration patterns, and the CPU/memory shape of each node.
Ingest, parse, normalize, enrich, and write telemetry into the lake. This tier scales with daily volume and transformation cost.
Interactive queries, hunts, dashboards, and rule execution. This tier scales with analyst concurrency, query shape, and retention window.
Control-plane coordination for schedules, workflow state, cluster membership, and query orchestration. Usually small, but critical.
API and tool-facing nodes for agents, integrations, and MCP clients. This tier scales with automation volume and external access patterns.
Want a real number?
We'll sketch the node count for your data volume and query pattern, and walk the pass-through bill end to end.