A practical guide to observability TCO and cost reduction

Source: Dev.to

Key takeaways

• Observability costs are driven by misaligned models – punitive SaaS pricing based on data ingestion or per‑host metrics forces a choice between visibility and budget.
• Incumbent architectures are inefficient – traditional tools built on search indexes suffer massive storage overhead and struggle with high‑cardinality analytics, causing costs to explode.
• Columnar architecture is the solution – shifting to a columnar database like ClickHouse provides superior compression (15‑50×) and excels at high‑cardinality queries that cripple other systems.
• A true TCO must include “people costs” – self‑hosted stacks require engineering maintenance and on‑call duties ($1,600‑$4,800 / month), often making a managed service like ClickHouse Cloud more cost‑effective, especially for bursty workloads.
• A unified stack (ClickStack) eliminates silos – consolidating logs, metrics, and traces removes data duplication and the high TCO of managing multiple federated systems.
• Significant savings are achievable – industry leaders such as Anthropic, Didi (30 % cost cut, 4× faster), and Tesla (ingesting a quadrillion rows) have realized substantial reductions using this approach.

Why your observability bill is exploding (and it’s not your fault)

The explosion in observability costs stems from architectural failure, not budget failure. Two core problems drive these costs:

1. Inefficient technology – Many traditional observability platforms rely on search indexes (e.g., Lucene). While great for text search, they are mismatched for the aggregation‑heavy analytical workloads modern observability demands.
2. Misaligned pricing models – SaaS vendors charge a “tax” on visibility: ingestion fees, separate retention SKUs, and per‑host or per‑container counts that punish micro‑service architectures.

Cost drivers of the legacy approach

• Massive storage and operational overhead – Inverted indexes create huge storage overhead and compress poorly. A team ingesting 100 TB daily could face storage costs exceeding $100 k / month. A single node failure can trigger costly data rebalancing that throttles the cluster for days.
• The high‑cardinality crisis – Modern distributed systems emit telemetry with many unique dimensions (e.g., user_id, session_id, pod_name). Systems like Prometheus generate a new time series for every label combination, exploding memory usage and slowing queries. Index‑based systems crumble under this load.

The columnar solution

Switching to a columnar database such as ClickHouse addresses both cost problems:

• Compression – Columnar storage groups similar data types, enabling specialized codecs that achieve remarkable compression ratios (e.g., 15‑50×, up to 170× in some workloads). ClickHouse’s internal observability platform compresses 100 PB of raw data down to 5.6 PB, delivering up to 200× cost savings versus leading SaaS vendors.
• High‑cardinality analytics – ClickHouse is built for fast analytical queries across billions of rows, handling high‑cardinality aggregations that would cripple other systems. Tesla’s platform ingests over one quadrillion rows with flat CPU consumption, demonstrating the scalability of this approach.
• Separation of storage and compute – By using cheap object storage (e.g., S3) for the primary data tier and scaling compute independently, you avoid the “pay‑for‑everything” trap of traditional SaaS models.

How to calculate your observability TCO: a practical framework

A comprehensive Total Cost of Ownership (TCO) model must include all direct and indirect costs, especially engineering time that is often overlooked. The framework below compares three primary architectural models:

Steps to perform the calculation

1. Gather usage metrics – total ingested GB per month, number of hosts/containers, retention periods, and query volume.
2. Apply the formulas for each cost category based on the chosen architecture.
3. Add “people costs” – estimate engineering time for deployment, maintenance, and on‑call duties (e.g., $1,600‑$4,800 / month for a small team).
4. Compare scenarios – plug the numbers into the table to see the cost differential between SaaS, federated OSS, and a unified ClickHouse‑based stack.
5. Factor in growth – model future data growth (e.g., 20 % YoY) to ensure the chosen architecture remains cost‑effective at scale.

By following this framework, you can make data‑driven decisions that align observability spend with business goals, eliminating waste while preserving full visibility into your systems.