“Observability cost reduction” is easy to say and hard to quantify. Vendors quote per-gigabyte rates, finance sees a large and growing number, and engineering is asked to cut it without going blind. So let us do the arithmetic. This post walks through the math of observability cost reduction: where the money actually goes in a logging and SIEM bill, and what changes when you apply lossless compression instead of dropping data.
Where your observability bill comes from
Most observability and SIEM costs decompose into four buckets:
- Ingestion: a per-gigabyte charge to accept your logs, metrics, and traces.
- Indexing and storage: keeping the data queryable over your retention window.
- Query and search compute: the cost of actually asking questions, sometimes metered separately.
- Egress: the cloud transfer fees to move telemetry to the vendor, which many teams forget to count.
The dominant term is almost always volume. Ingestion and storage both scale with how many bytes you produce, which is why every cost-cutting playbook starts with “send less data.” The problem, as we cover in sampling vs. lossless, is that sending less data means keeping less data.
A worked example
Take an illustrative team ingesting 10 TB of telemetry per day and retaining it for 12 months for operational and compliance reasons. The two ways to cut that bill look very different:
- The sampling path. To cut cost in half, you drop half the data. Storage falls, but so does fidelity: every investigation, detection, and audit now runs on a coin-flip of your telemetry. You saved 50% and lost 50% of your answers.
- The lossless path. You keep all 10 TB and compress it losslessly. When compression reaches an order of magnitude or more, the same data occupies a fraction of the footprint. You keep 100% of your answers and the storage line still collapses.
The difference is that sampling trades cost against fidelity, while compression removes the trade entirely. One is a discount you pay for in blind spots; the other is a discount you keep.
Why the compression ratio is the number that matters
Generic gzip on log data gets you a few times smaller. That helps, but it is not enough to change the economics, and it does not make the data queryable in compressed form. The lever that moves the bill is a high, content-aware compression ratio that still supports full-fidelity search.
Sasquatch Labs uses a patent-pending, property-aware compression approach built specifically for telemetry, reaching roughly 100x on logs, traces, and metrics and higher still on security data. Crucially, the compressed data stays queryable: our Snowman query engine reads the compressed chunks directly, so there is no re-ingestion step and no separate search-compute line item. Higher ratio plus in-place query is what turns a compression feature into an actual cost-reduction platform.
Sampling lowers cost by lowering the number of questions you can answer. Compression lowers cost by lowering the cost per byte. Only one of those is free.
Do not forget egress and retention in the model
- Egress: if telemetry leaves your cloud to reach a SaaS vendor, you pay transfer fees on top of ingestion. Keeping data in your own cloud removes that entirely. See observability data egress costs.
- Retention: long compliance windows are painful only because storage is expensive per byte. Drop the cost per byte and long retention stops being a budget problem.
- Search compute: querying compressed data in place avoids a second bill for re-hydrating or re-ingesting data just to search it.
The bottom line
Observability cost reduction does not have to mean data reduction. Once you separate the volume of telemetry from the cost of keeping it, the entire “what do we drop” exercise becomes unnecessary. Keep everything, compress it by an order of magnitude, store it in your own cloud, and query it in place. That is the math that lets you cut the bill without cutting the data.
Run the numbers for your stack at sasquatchlabs.io, and read more on keeping telemetry in your own cloud.