211 lines
8.0 KiB
Markdown
211 lines
8.0 KiB
Markdown
# SLOs & You: A Guide To Service Level Objectives
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- **期号**: SRE Weekly Issue #131（2018-07-22）
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- **作者**: Kevin Kamel — Circonus
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- **链接**: https://www.circonus.com/2018/07/a-guide-to-service-level-objectives/
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## 简介
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A solid definition of SLIs, SLOs, and SLAs (from someone other than Google!). Includes some interesting tidbits on defining and measuring availability, choosing a useful time quantum, etc.
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## 正文
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Agentic-Ready Telemetry Management
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# Telemetry Control Plane for AI-Era Data Volumes
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Apica helps enterprises be **“Agentic-Ready”** by giving them the telemetry infrastructure, metrics foundation, and data readiness AI agents demand.
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**Gartner® Magic Quadrant™**Observability Platforms, 2025 & 2026
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## Trusted by Leading Enterprises
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The Challenge
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## The Telemetry Cost & Growth Crisis
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Observability budgets are at their breaking point and AI is about to make it exponentially worse.
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- AI and ML workloads generate 10–100x more telemetry than traditional applications, driving observability costs to unsustainable levels.
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- Legacy platforms charge for every byte ingested, forcing teams to either pay a platform tax or sacrifice visibility into critical systems.
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- Cloud modernization and microservices architectures further multiply data volumes while budgets stay flat, pushing observability spend to an unsustainable level.
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**This isn’t just a cost problem. It’s an architectural problem.**Traditional platforms weren’t designed for AI-scale telemetry. They store everything indiscriminately and charge at every step. That model breaks when a single AI agent generates more telemetry in an hour than an entire app stack produced in a day.
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54%
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of enterprises saw telemetry data triple in the past 12 months — average increase of 3.7×
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9.5x
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expected average telemetry growth from agentic AI workloads within 2 years — 44% expect 6×–100×
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$3.17M
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average annual enterprise observability spend — growing 28% year-over-year with no natural ceiling
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*Source: Apica + Omdia/Informa TechTarget Survey Report*
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The Architecture Shift
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## Be Agentic-Ready With Apica
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From Platform to Pipeline: Build telemetry infrastructure that powers AI, not platform costs.
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For 15+ years, teams treated telemetry pipelines as “dumb pipes” shoveling data into “smart platforms.” That collapses under AI. Agentic systems demand
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**millisecond-level context**. If intelligence lives only after storage and indexing, your costs spike first and your agents learn later.
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### The Problem: Platform-Centric Thinking
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- Data is ingested (charged), stored (charged), and indexed (charged) before you see value
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- AI agents need real-time intelligence in milliseconds, not batch processing after storage
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- Platform vendors optimize for their revenue, not your efficiency
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- Scaling agentic workloads = 10x platform bill before generating value
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### Apica Inverts This Model
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- Process and enrich telemetry in the pipeline before expensive ingestion
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- Route intelligently — send only what needs indexing to expensive platforms
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- Store cost-efficiently in your data lake or ours (powered by InstaStore™)
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- Real-time access for both human operators and AI agents
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- Monitor multi-agent user journeys and automate testing continuously
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This isn’t about replacing your observability platform. It’s about taking control of
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**what**reaches it,**when**, and at what**cost**.
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Apica Ascent
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## Built for the Agentic Era
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### Apica Fleet
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Collection Plane
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Centrally manage every telemetry agent across your entire environment. This is the organized data collection layer that feeds Flow (configuration-driven, dynamically adaptive.)
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### Apica Flow
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Control Plane
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Shape, govern, and route telemetry before platform ingestion. 100% pipeline control with zero data loss.
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### Apica Lake
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Storage Plane
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Retain everything, query instantly, replay on demand. The data foundation agents need for context and audit.
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### Apica Observe
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Intelligence Plane
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AI-powered correlation of logs, metrics, traces, and events with out-of-the-box LLM monitoring dashboards.
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### Apica Forge
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Metrics Plane
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Store and analyze massive, high-cardinality metrics in real time without sacrificing performance or economics.
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### Apica Vanguard
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Safety Net
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Synthetic checks simulating real agent workflows end-to-end.
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### Apica Wayfinder
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Test-Data Engine
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Provision right-sized, compliant test data on demand for agentic journeys.
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Perfect For:
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- Enterprise teams drowning in telemetry costs from Splunk, Datadog, or Dynatrace
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- Organizations with compliance or audit requirements where zero data loss is mandatory
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- Engineering teams building agentic AI systems that need real-time, clean telemetry
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The Real Shift Left
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## Your Pipeline Becomes the Brainstem
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Real intelligence isn’t in your platform UI. It’s in your configs, CMDB, runbooks, and post-mortems. With Apica, enrich context at collection, govern routing by business value, and enable instant “time-travel” replay before indexing and without storage tax.
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Why Now
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## The Telemetry Data Problem Is Accelerating
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Every enterprise technology leader faces the same challenge: costs outpacing budgets, with AI making it exponentially worse.
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01 — Driver
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### AI Adoption
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Production AI agents generate 10-100x more telemetry than POCs. Most enterprises haven’t budgeted for this reality.
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02 — Driver
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### Cloud Modernization
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Kubernetes and microservices multiply telemetry sources exponentially. Traditional tools weren’t designed for this cardinality.
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03 — Driver
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### Compliance Requirements
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Regulations demanding complete data retention conflict with platform pricing. The cost of compliance is becoming prohibitive.
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04 — Driver
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### Market Maturity
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CIOs and CTOs with large observability spend are reevaluating their entire approach based on cost of operations.
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“This is the tip of the iceberg. Organizations experimenting with AI agents today haven’t felt the full growth and cost impact yet. But it’s coming, which is why you need to make the right architectural choices before your AI-driven telemetry and costs explode.”
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Mathias Thomsen
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CEO, Apica
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