OpenTelemetry
Open-source standard for collecting and exporting telemetry.
Available information varies by company and source.
Profile record updated:
Company facts
- Official name
- OpenTelemetry
- Entity type
- COMPANY
- Founded
- 2019
- Market role
- Other / Non-Digital Advertising Relevant
- Official website
- opentelemetry.io
What OpenTelemetry does
OpenTelemetry operates as a foundation-hosted open-source project rather than a standalone commercial business. Its value creation model is ecosystem standardisation: it defines shared telemetry specifications and reference implementations that developers, enterprises and observability vendors adopt to instrument applications and infrastructure consistently. Commercial value is captured indirectly by ecosystem participants such as cloud providers, APM vendors, managed service providers and systems integrators that build products and services on top of the standard.
Category differentiation
OpenTelemetry is not a standalone commercial APM vendor or advertising technology platform. It is an open-source telemetry standard and framework that other vendors and enterprises implement within observability stacks.
Strategic context
AI-supported assessment from the existing company research; distinguish interpretation from sourced facts.
OpenTelemetry is a vendor-neutral open-source observability framework and telemetry standard hosted by the Cloud Native Computing Foundation. It provides common instrumentation, collection and export capabilities for metrics, logs and traces, enabling software teams and platform operators to generate portable telemetry across cloud-native systems and send that data into downstream monitoring and observability tools. The project creates value by reducing fragmentation in telemetry instrumentation and by establishing a common standard across vendors and enterprise environments. It does not operate as a conventional commercial software vendor and there is no evidence of direct standalone revenue. Its direct users are software developers, platform engineering teams, SRE teams, cloud-native operators and observability vendors that build integrations or commercial products around the standard.
Company news briefing
Briefing updated:
Building on its role in AI and microservice observability, OpenTelemetry has promoted its Go Logs API and SDK to release candidate status (v1.47.0-rc.1), marking a final step towards stable v1 compatibility. This core milestone is complemented by the growing adoption of OpenTelemetry-style instrumentation for tracing LLM calls and managing release-specific performance. Together with Prometheus integrations and service.version tracking, these updates reinforce OpenTelemetry's position as the vendor-neutral standard for end-to-end system visibility.
Business model & monetisation
There is no evidence of direct monetisation by OpenTelemetry as an independent entity. The project is open source and foundation-hosted, with value distributed through free adoption, community contribution and ecosystem integrations. Commercial monetisation occurs around the project via third-party vendors that sell observability software, managed services, support, integrations or cloud infrastructure built on the standard.
- Open-source framework adoption
- Foundation-hosted ecosystem value capture by third parties
Products & capabilities
No products with linked sources are available in this view.
Recent recorded signals
Dates refer to the source publication. Older entries are historical context, not evidence of a new event.
OpenTelemetry Go Logs API and SDK reach release candidate status
Recorded impact score: 3.5/5
OpenTelemetry Go v1.47.0-rc.1 is here. This release promotes the Logs API and SDK to release candidate (RC), the final stage before we provide stable v1 compatibility guarantees. We believe the design is ready, and now we need the community to test …
LLMOps for Compound AI Systems: Observability & Cost
Large Language Models (LLM) & AI · Recorded impact score: 2/5
The article argues that most GenAI pilots fail in production due to insufficient system-level engineering rather than poor models. It presents an LLMOps playbook for compound AI systems (embedders, retrievers, vector stores, re-rankers, validators, tool calls, and multiple LLMs) centered on five controls: a model gateway for routing and budgeting, pipeline-level traces for end-to-end observability, semantic caching keyed by query embeddings, lightweight eval gates for safety and quality, and tiered scaling of heavy infrastructure. A concrete engineering example reports a 38% reduction in token spend and 25% lower median latency after implementing a gateway, semantic cache, and tracing. The post includes a short pseudocode example (using qdrant-style vector operations) and an operational checklist for iterating LLMOps as an operating model.
- The article defines five LLMOps controls: model gateway, pipeline-level traces, semantic caching, eval gates, and tiered scaling.
- Author recommends using OpenTelemetry-compatible spans to instrument embed, search, rerank, prompt build, LLM call, and tool call stages.
agent-cost: Measure LLM Usage, Separate Task Attribution
Large Language Models (LLM) & AI · Recorded impact score: 2/5
The author describes agent-cost, a small tooling primitive that reads local logs from LLM CLIs (e.g., Claude Code and Codex) to produce auditable, machine-readable usage facts (model, token kind, timestamp, count) and an estimated price. The tool is designed to run with no network calls at runtime, carry a versioned price catalog (with SHA-256 digest), and keep session measurement distinct from task attribution. Unknown or unsupported pricing and ambiguous session-to-task bindings are surfaced (labels like "unpriced" or "lower_bound") rather than silently allocated. The author re-ran the published coding-agent-cost 0.1.0 package and notes a catalog version 2026-07-29 and workflows that validate the measure/v1 protocol and data quality.
- agent-cost reads local logs from LLM CLIs (examples: Claude Code and Codex) and normalizes usage events into facts containing model, token kind, timestamp, and count.
- At runtime agent-cost makes no network calls and declares no Python runtime dependencies; installation from PyPI still requires trust in the supply chain.
Designing Durable Async Summarization Jobs for CRM
Infrastructure · Recorded impact score: 2/5
Technical guide describing best practices for a Node.js marketplace call summarization API that consolidates multiple transcripts into verified CRM exports. Recommends durable async jobs (job records, item-level outcomes, explicit state machine) when several documents must produce a single reviewed CRM action, and inline requests for single short transcripts. Provides TypeScript types and code samples for job/item states, idempotency rules, bounded concurrency, observability metrics, retry policies, and a safe export format that preserves source document IDs and error codes.
- Recommends three API patterns: inline request for single short transcripts, bounded parallel calls for small independent sets, and durable async jobs for multiple documents that feed one CRM export.
- Defines TypeScript types and a state machine (JobState: accepted/running/completed/cancelled; ItemState: pending/running/succeeded/failed/skipped) plus data models (DocumentInput, CrmAction, ItemResult, BatchJob) as a suggested contract.
Trace TypeScript AI Agents with Adapter Layer
Infrastructure · Recorded impact score: 2/5
This technical guide explains using an adapter layer to normalize tracing across multiple TypeScript AI agent frameworks (e.g., Vercel AI SDK, LangChain.js, OpenAI Agents SDK). It proposes defining a small, versioned TraceEvent model (span_started, span_ended, adapter_diagnostic), declaring adapter capabilities, and implementing a FrameworkAdapter interface that translates framework-specific callbacks into normalized events. The article includes a NormalizingAdapter example, guidance for preserving identity/parentage, handling streaming lifecycles, semantic normalization, and testing adapters with event fixtures. The goal is to keep framework volatility at the edge while enabling framework-independent analysis, CI quality gates, and exports (for example to OpenTelemetry). It recommends introducing adapters when multiple frameworks or migrations create repeated observability work.
- Recommends an adapter layer to translate framework-specific callbacks into a single versioned trace model for TypeScript AI agent frameworks.
- Defines a TraceEvent schema (schemaVersion 1) with event types: span_started, span_ended, adapter_diagnostic.
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Questions about OpenTelemetry
What is OpenTelemetry?
OpenTelemetry is a vendor-neutral open-source observability framework and telemetry standard for collecting, processing and exporting metrics, logs and traces.
Who uses OpenTelemetry?
Software developers, platform engineering teams, SRE teams, enterprises, cloud providers and observability vendors use OpenTelemetry to instrument systems and standardise telemetry.
How does OpenTelemetry make money?
OpenTelemetry does not have a direct standalone revenue model in the provided data; it operates as a CNCF-hosted open-source project, while commercial vendors monetise products and services built around it.
Sources & coverage
This profile uses public, official and technically observable information. Missing information does not prove that a product or relationship does not exist. The list below does not imply that every profile statement has been verified.
8 publicly documented primary sources and citations linked across the market graph.
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