How does Mezmo reduce AI and observability costs?

Mezmo applies context engineering to filter, sample, and enrich data in motion, reducing unnecessary storage and token usage—cutting observability costs by 70–90%.

What makes Mezmo’s context engineering AI-ready?

Mezmo delivers curated, high-fidelity context—combining logs, metrics, traces, and business metadata—so AI agents and teams get actionable insights with fewer prompts and token usage.

Can Mezmo’s platform improve root cause analysis accuracy?

Yes. By engineering the data pipeline to focus on clean, structured context, Mezmo enables instant, reliable RCA with dramatically reduced tool calls and token bloat.

Deliver AI-ready context with confidence: From systems to root causes

Are you challenged by: ‍

Underperforming AI agents that consume hundreds of thousands of tokens without delivering accurate root cause analysis
‍The "Haystack" problem: Overwhelming your LLMs with excessive, noisy data, which dilutes critical signals and drives up token cost without providing accurate results
Analysis paralysis that leads to slow, multi-prompt debugging when you need instant insight
‍Fragmented pipelines that starve AI and engineering teams of a clean, contextual signal

Mezmo applies context engineering to shape telemetry in motion so teams and AI agents get the right data, with the right structure, at the right time, while cutting observability costs by 70-90%.

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Context is the interface for AI and modern engineering. Instead of storing everything "just in case," Mezmo's Active Telemetry Platform profiles, enriches, and routes data in-stream, giving developers, SREs and AI agents the ability to act immediately with clean, contextual signals.

Context engineering turns raw telemetry into trusted, structured context for AI and humans. Mezmo unifies logs, metrics, and traces; enriches them with business metadata; and delivers exactly what each destination needs, without delay or runaway cost.

Why context engineering

Traditional observability hoards data and makes you parse and correlate after an incident. Context engineering answers questions now by engineering the inputs that systems and agents need to be reliable, safe, and efficient.

First-try root cause accuracy

Ensure clean, curated context for reliable, first-try answers, reducing required tool calls and token bloat by 90%.

Up to 10x faster MTTR

Instant, noise-free signals during incidents, moving from token-intensive guessing to decisive, intent-based context delivery.

90% cost reduction

Filter, sample, and route before indexing or storage. Drastically cut incident costs from an estimated $1-$6 per root cause analysis to pennies.

Key capabilities for context engineering

Mezmo's Active Telemetry platform is the engine that processes data in motion while keeping state where it matters.

AI agent enablement

Feed agents high-fidelity context through Mezmo's MCP Server, context engine and native support for providers like OpenAI, Bedrock, and LangChain.

Root cause analysis

Automatically analyze log patterns and system behavior to identify incident causes, definitive answers, not guesses.

Context in motion

Data is processed and enriched at ingestion time, enabling AI agents and teams to immediately act on clean signals.

Structured payloads

Deliver curated, scoped context to AI agents and destinations, replacing raw, noisy data dumps.

Signal filtering

Automatically detect and isolate critical signals from high-volume, low-volume noise, allowing AI to focus solely on interpretation and recommendations.

AI reasoning enrichment

Enhance every signal with business metadata and contextual data, standardizing formats to dramatically improve the AI agent's analysis and accuracy.

Real results from real teams

90%

Cost reduction

From $1-$6 per incident to $0.06 due to prioritized context over excessive prompting.

- Mezmo benchmarking data

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~27K

Token efficiency

Reduce token consumption from 500K to ~27K per incident for low-cost, high-fidelity analysis that scales.

— Mezmo benchmarking data

1st-try accuracy

RCA with less prompting

Clean context beats clever prompting. Mezmo's context-first pipeline reduced prompt bloat and stabilized outputs, improving quality while cutting per-incident costs.

— AI Engineer