Composable observability: How open standards power end-to-end visibility

Composable observability, built on open standards like OpenTelemetry and Prometheus, gives teams freedom to integrate best-in-class tools for deep visibility—without sacrificing unified system health, control, or cost efficiency across your organization.

Despite massive investment in observability, most organizations still operate with visibility gaps that slow detection, resolution, and optimization.

Composable observability offers a way out: open standards provide unified instrumentation across multiple vendors and platforms, letting teams deploy specialized platforms exactly where they deliver the highest ROI without creating data silos or technical debt.

This shift shakes up your traditional observability strategy. In the past, more tools meant more complexity: fragmented data, operational friction, spiraling costs. Instead of being constrained by a particular vendor’s roadmap, composable observability lets you select platforms to provide the depth of visibility your business actually needs.

Composable observability delivers three strategic advantages:

• Team autonomy – Domain experts choose tools that fit their needs and extend or augment existing telemetry.
• Future-proof architecture – Add new vendors and platforms without re-instrumentation, evolving your observability stack alongside the business.
• Optimized investment – Focus spend where depth drives impact, reducing waste and total cost of ownership.

Together, these integrations show how composable observability connects every signal, from user journey to code path profile, into one system of understanding.

Chronosphere specializes in observing cloud applications and infrastructure with logs, Prometheus metrics, and OpenTelemetry metrics and traces. The Control Plane gives you unmatched visibility and control over the volume and cost of your telemetry data.

But we know observability goes beyond cloud workloads, which is why we hand-pick partners based on their expertise and experience in areas such as synthetic checks, real user monitoring (RUM), observing AI workloads, and profiling. OpenTelemetry and Prometheus are the connective tissue: unified standards that let specialized platforms work together.

In this blog I show composable observability in action to give you deep observability up and down the stack using Chronosphere and our trusted partners.

Checkly: Proactive assurance through Synthetic Monitoring

When regressions slip into production, business impact is immediate: lost revenue, eroded trust, and delayed releases. Even when backend infrastructure reports as healthy, external dependencies like third-party APIs, CDN routing, regional configurations can silently degrade customer experiences in ways that only surface through support tickets or abandoned carts. By the time issues are reported by users, you’ve already lost conversions and damaged trust.

Generic uptime checks provide false confidence; a “200 OK” doesn’t mean that the page load performance meets user expectations or that critical integrations are functioning correctly.

Checkly enables organizations to validate business-critical user journeys continuously, catching experience-breaking failures before they impact revenue. Checks are the automated tests that power this validation—simulating real user actions and monitoring your critical workflows on a defined schedule.

Key Capabilities:

• Monitoring as code – Define complex user flows with Playwright/Puppeteer that mirror actual user behavior.
• Multi-region execution – Run checks across 20+ global locations to catch geography-specific failures.
• Deep failure context – Capture screenshots, console logs, and network activity when checks fail.

Chronosphere can ingest Prometheus metrics and OTel traces from Checkly checks as first-class signals. Teams can visualize failed checks alongside application latency or correlate synthetic failures with recent change events.

Outcome: Detects and resolves customer-impacting regressions before they affect revenue or brand trust.

While Checkly ensures your critical workflows stay reliable before deployment, real-world visibility requires understanding how users experience your app in production.

Frontend visibility gaps directly translate to lost conversions, churn, and engineering fire drills. Low-frequency crashes or errors that disproportionately affect high-value customers or performance regressions isolated to specific browser versions or operating systems are actionable signals for mobile and web applications, but most organizations lack the tools to surface them before business impact compounds.

Status quo real user monitoring (RUM) tooling leaves most organizations operating with an incomplete, aggressively-sampled or incorrect understanding of the client-side user experience. Without the full picture, engineering teams are forced into reactive scrambles to investigate issues that have damaged user trust.

Embrace delivers purpose-built RUM across web and mobile with flexible session capture, giving developers full visibility and control over how much data they collect – up to every session – based on their observability goals and cost profile. Teams gain a complete view into frontend reliability and stability to understand what users actually experience and protect mobile and web-driven revenue.

Key Capabilities:

• Full interaction context – Investigate complete user journeys, including clicks, page navigation, form interactions, and API calls—enabling teams to reproduce failures and understand behavioral patterns that lead to abandonment.
• Scalable session capture – Capture the right amount of data for your observability needs. From strategic sampling to monitoring every mobile and web session in full fidelity, teams can uncover issues affecting specific browsers, devices, experiments, OS versions, or user cohorts. Understand every user experience, including client-side performance degradations that impact revenue, with a cost structure that won’t break the bank.
• OpenTelemetry-native instrumentation – Capture and receive mobile and web telemetry modeled according to the open standard, including full interoperability with Chronosphere metric and trace ingestion.
• Performance snapshots – Expose memory leaks and resource exhaustion, misused threads, malformed or dropped requests, and frame rate drops in real-time.

Embrace attaches W3C traceparent IDs (a distributed tracing standard) to every network request, enabling seamless connection between frontend experience in Embrace and backend traces in Chronosphere. Engineers investigating issues can pivot directly from backend span to the exact user session and vice versa, in a single click.

High-level metrics (crash and error rates, session counts, crash-free percentages) are exported to Chronosphere as Prometheus metrics, enabling teams to monitor frontend reliability and backend performance from a single dashboard. Below you can see a Chronosphere dashboard featuring helpful links directly to Embrace for deeper diagnosis.

Outcome: Connects frontend reliability directly to backend performance, reducing triage cycles and improving customer satisfaction through full-stack visibility.

Once user experience is fully visible, the next challenge is understanding how intelligent systems behave—especially when AI decisions affect customer outcomes.

As organizations deploy AI at scale, unseen model drift or hallucinations can quietly undermine compliance, brand reputation, and customer trust. AI-powered products create new failure classes that infrastructure metrics can’t surface — hallucinations, bias, and model drift. These issues are business-critical, affecting trust, compliance, and brand integrity.

Standard platforms monitor whether AI endpoints respond, but don’t evaluate whether responses meet quality, safety, or accuracy requirements. Developers don’t just need to know that an LLM call failed—they need to know why, for whom, how often, and under what conditions. Staying in the dark about toxic outputs, inefficient prompts, and overprovisioned model capacity is a risk.

Arize extends observability into LLM app and AI Agent behavior and output quality, providing the governance layer necessary for production AI systems.

Key Capabilities:

• End-to-end AI tracing – Monitor complete model request flows, including retrieval pipelines, prompt construction, and embedding generation across any LLM, agent framework, RAG pipeline, or vector database.
• Evaluations – Assess outputs against accuracy, relevance, toxicity, and hallucination criteria using customizable or pre-built evaluators, including custom metrics for brand guideline compliance or domain-specific quality measures.
• Prompt IDE – Iterate and experiment with prompts, compare model performance side-by-side, and test guardrails or fine-tuning approaches using production data sets.

Arize’s enriched OpenTelemetry spans are ingested into Chronosphere. Each span carries metadata like model version, token count, and evaluation scores (groundedness, relevance, toxicity). This enables teams to:

• Monitor compliance SLOs (“zero toxic outputs per 10K responses”) alongside existing SLOs
• Track token efficiency and cost-per-query across models to optimize performance-to-cost ratios
• Correlate AI quality degradation with specific deployments or infrastructure changes

Outcome: Transforms AI features into auditable, governed systems — reducing compliance risk, improving model performance, and increasing the return on AI investment.

Where Arize ensures intelligence behaves correctly, the resource intensity of AI workloads, particularly GPU consumption, demands equally sophisticated visibility into performance and efficiency.

Infrastructure inefficiencies accumulate invisibly — wasting cloud resources and eroding margins long before they trigger alerts. By the time performance degradation becomes visible in aggregate metrics, months of incremental inefficiency may have already inflated cloud costs and degraded the user experience.

Teams resort to reactive optimization: waiting for cost crises or performance incidents and running one-off profiles that don’t reflect production workload patterns. This approach misses the gradual regressions that account for the majority of wasted infrastructure spend.

Beyond cost control, continuous profiling helps teams prevent latency regressions and performance bottlenecks before they reach customers. By comparing profile data across deployments, teams identify new hot paths, memory leaks, or inefficient algorithms earlier in the development cycle.

Polar Signals adds continuous profiling to the composable observability stack, revealing how code actually consumes CPU, GPU, memory, and I/O at the function level in production environments. With the latest release, continuous NVIDIA CUDA profiling, teams now gain kernel-level visibility into GPU utilization with less than 1% overhead, addressing the resource demands of AI workloads without the 5-15% performance penalties traditional profiling tools impose. This production-safe approach delivers GPU insights that alternatives simply cannot match at scale.

Key Capabilities:

• Zero-overhead production profiling – eBPF-based collection profiles all processes continuously (<1% overhead) without code instrumentation or application changes.
• Historical efficiency analysis – Compare resource consumption across deployments, identify performance regressions, and quantify optimization opportunities before they impact user experience.
• Code-level accountability – Connect infrastructure spend directly to specific functions, enabling engineering teams to prioritize optimization efforts by financial impact.

Using low-overhead eBPF profiling, Polar Signals continuously maps resource usage down to the function level, revealing where inefficiency drives cost. Profiling data is exported as Prometheus metrics and sent to Chronosphere, allowing teams to correlate CPU, GPU, or memory consumption with service versions, traffic patterns, deployments, and other dimensions.

Outcome: Reduces cloud spend and performance drift by connecting code-level inefficiencies directly to financial impact.

With continuous visibility across user experience, model behavior, and infrastructure efficiency, teams can finally govern reliability as a unified system rather than isolated silos.

Continuous profiling shifts optimization to a proactive practice, giving teams the ability to detect performance drift long before it becomes a reliability problem and reduce cloud costs by identifying wasteful code paths.

Across these examples, composable observability isn’t theoretical, it’s a living architecture. Each domain—synthetic, real user monitoring, AI models, and profiling extends visibility where it matters most, while unified open standards keep everything connected.

Open standards like OpenTelemetry and Prometheus don’t just make integration possible — they provide the governance backbone that ensures every signal is collected, correlated, and understood the same way.

Composable observability isn’t one-size-fits-all. The specialized platforms you choose depend on your architecture’s maturity, the depth your teams need, and how quickly your organization is evolving. The “right” answer is rarely absolute.

The strategic advantages compound over time:

• Correlate seamlessly: Unified telemetry schema across vendors and platforms accelerates issue investigations and increases coordination across domains
• Adopt dynamically: Integrate emerging tools without re-instrumentation, adapting your observability stack as needs evolve
• Optimize investment: Direct resources to domains with the highest business impact, reducing both cost drift and complexity

When every layer speaks the same telemetry languages, observability becomes more than a set of tools—it becomes organization-wide capability showing how technology drives business outcomes. Chronosphere serves as the central hub for this composable approach for cloud native environments, compatible with open standards, and a powerful correlation engine with DDx. By aligning observability architecture with business strategy, you can ensure reliability investments deliver sustained ROI, not redundant tooling.

Composable observability isn’t a trend—it’s the operating model for modern reliability. Explore how Chronosphere integrates with Checkly, Embrace, Arize, and Polar Signals, and see how open-standards observability can scale with your architecture.

Doesn’t composable observability create more vendor sprawl?

Not when it’s intentional and part of a unified telemetry strategy. The goal isn’t to buy every observability platform under the sun; it’s to invest in the ones that go deep and deliver real ROI.

The reality is, there will never be one perfect platform. Engineers will always use what helps them move fastest. The trick is to support that freedom with direction: give teams room to choose while steering them toward shared standards and open instrumentation.

Sprawl happens when you react instead of plan. Composable observability does the opposite—it makes choice work for you, not against you.

Don’t all-in-one platforms cover these domains?

They attempt to, but all-in-one platforms that optimize for breadth can mean compromising on impact detection accuracy and business outcomes. Composable observability optimizes for depth where it matters most to your business.

Mobile and web

• All-in-one: Typically sample 1-5% of sessions, missing low-frequency crashes and errors that affect high-value users, offer little-to-no performance measurement, and lack depth and usefulness for frontend practitioners who own mobile and web reliability
• Embrace: Flexible data capture, up to complete session coverage, ensures no user segment goes unseen – critical for identifying issues in specific device/OS/region combinations. Scale user-focused observability with control and customization, with detailed insights across backend and frontend teams.

Continuous Profiling

• All-in-one: Generic APM profilers require code instrumentation. They can’t run continuously due to 5-15% overhead.
• Polar Signals: eBPF-based profiling runs continuously with <1% overhead—no code changes required

AI Observability

• All-in-one: Monitor endpoint availability but lack evaluation frameworks for quality, safety, bias.
• Arize: Purpose-built evaluators assess hallucinations, groundedness, toxicity—not just uptime.