--- title: "The real cost of your observability stack" url: https://daily.dev/posts/the-real-cost-of-your-observability-stack-l2ll4sujq source_url: https://opensearch.org/blog/the-real-cost-of-your-observability-stack type: article source: "OpenSearch" published: 2026-08-23T12:21:03.360Z updated: 2026-08-23T12:43:36.029Z tags: ["ai-agents", "observability", "vector-search", "opentelemetry", "opensearch"] reading_time: 13 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # The real cost of your observability stack **[OpenSearch](https://daily.dev/sources/opensearch)** · 13 min read · 0 upvotes · 0 comments ## Summary AI agents generate significantly more telemetry than traditional services—reasoning traces, token logs, confidence scores, tool invocations—and per-GB pricing from proprietary observability vendors makes this unaffordable at scale, pushing teams to sample aggressively and lose visibility exactly when they need it most. The OpenSearch Observability Stack is presented as an open-source alternative that unifies logs, traces, metrics (via native Prometheus/PromQL integration), agent telemetry, and vector embeddings in a single engine, using PPL or SQL for querying, OpenTelemetry GenAI semantic conventions for agent traces, and Apache 2.0 licensing to avoid vendor lock-in and per-GB ingestion fees. It also covers agent-specific observability signals (reasoning traces, token economics, confidence scoring, decision quality correlation), MCP apps for investigating from Claude Desktop and VS Code, and compliance/data sovereignty benefits for regulated industries. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why does AI agent telemetry cost so much more to store than traditional API observability data? AI agents generate far more telemetry per request than a REST endpoint because each interaction produces reasoning traces documenting every logic step, token consumption logs across multiple model calls, confidence scores per decision, tool invocation records, and RAG lookup data. A traditional service by comparison only generates a trace span, a few log lines, and metric increments, so per-GB pricing punishes agent-heavy workloads disproportionately. _Anyone budgeting for agent observability costs can follow ongoing coverage of this trade-off on daily.dev._ ### How does OpenSearch handle metrics versus logs and traces for observability? OpenSearch queries Amazon Managed Prometheus using native PromQL for time-series metrics while storing logs, traces, and agent telemetry directly as first-class data, correlating both signal types in one interface without duplicating data between systems. Logs and traces are queryable via Piped Processing Language (PPL) or SQL, letting each backend handle the signal type it is optimized for. _Teams comparing observability architectures can track deep dives like this on daily.dev._ ### What is the OpenSearch MCP app for agent observability in IDEs like VS Code and Claude Desktop? It is an open-source Model Context Protocol integration that lets developers run observability investigations directly from Claude Desktop, VS Code, and compatible agentic IDEs, returning a text summary plus interactive visualizations in the conversation thread. This covers log search, metrics correlation, trace exploration, service maps, and topology views without switching consoles. _Developers wiring AI tools into their observability workflow can follow tooling updates like this on daily.dev._ ## Similar posts on daily.dev - [Why Your Observability Stack Is Costing You More Than Your Cloud Bill](https://daily.dev/posts/why-your-observability-stack-is-costing-you-more-than-your-cloud-bill-lg5pnxevg) · DevOps.com · 0 upvotes · 0 comments - [Why Your AI Agent is a Black Box and How to fix it With OpenTelemetry](https://daily.dev/posts/why-your-ai-agent-is-a-black-box-and-how-to-fix-it-with-opentelemetry-oakutrahj) · DevOps.com · 0 upvotes · 0 comments - [AI Raises the Stakes for Observability Engineering](https://daily.dev/posts/ai-raises-the-stakes-for-observability-engineering-i4tkn4oti) · DevOps.com · 0 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#observability](https://daily.dev/tags/observability), [#vector-search](https://daily.dev/tags/vector-search), [#opentelemetry](https://daily.dev/tags/opentelemetry), [#opensearch](https://daily.dev/tags/opensearch) [View this post on daily.dev](https://daily.dev/posts/the-real-cost-of-your-observability-stack-l2ll4sujq)