AI observability companies, products & suppliers
Telemetry products for AI systems' quality, cost and behaviour. Jobs: prompt and generation traces; cost dashboards; quality monitors.
What is AI observability?
Telemetry products for AI systems' quality, cost and behaviour. Jobs: prompt and generation traces; cost dashboards; quality monitors.
What problems does it solve?
LLM and ML apps fail silently in ways APM never showed.
Typical business use cases
- Prompt and generation traces
- Cost dashboards
- Quality monitors
Important capabilities
- Instrumentation
- Dashboards
- Alerts
What buyers should evaluate
- What is captured
- PII
- Sampling
Risks and governance considerations
Full production prompts in an observability tenant abroad.
Procurement checklist
- Capture policy
- Retention
- Access
Relevant AI Trustmark assurance
AI Trustmark independent findings appear only when an assessment or certificate exists. Category membership does not imply verification.
Companies and providers
Claimed suppliers appear first so buyers can start with listings the company has taken ownership of. Payment does not buy this order.
Continuously improve AI agents with agent observability, evaluation, tracing, and experimentation. Arize AI publishes Arize AX Evaluation, Arize Phoenix, and Arize Model Monitoring
Cisco is a worldwide technology leader powering an inclusive future for all. Learn more about our products, services, solutions, and innovations. Cisco publishes Galileo and Cisco
Comet publishes Comet ML Platform and Opik as named AI products. Comet is used for Model governance and AI observability work. Comet is recorded in United States. Comet publishes p
Dash0 is modern OpenTelemetry Native Observability, built on CNCF Open Standards such as PromQL, Perses and OTLP with full cost control. Dash0 is used for AI observability work. Da
See metrics from all of your apps, tools & services in one place with Datadog’s cloud monitoring as a service solution. Try it for free. Datadog, Inc. trades as Datadog, based in N
Gain visibility, context, and control through evaluation, monitoring, enforcement, governance, and cost efficiency. Fiddler AI publishes Fiddler Guardrails, Fiddler AI Observabilit
Honeycomb is the observability platform built for AI-era software. Fast queries, unified telemetry, and LLM observability. Honeycomb is used for AI observability work. Honeycomb pu
LangChain enables every company to own their intelligence. Control, govern, and compound intelligence with an open agent engineering platform. Trusted by 7k+ organizations. LangCha
Comprehensive observability for AI agents with tracing, monitoring, and optimization. LangDB is used for AI observability work. LangDB publishes product information at langdb.ai. L
Trace, evaluate, and improve AI agents with one open platform. Use production data to understand behavior, collaborate on fixes, and ship better quality at lower cost and latency.
Helping open technology projects build world class open source software, communities and companies. Linux Foundation publishes Monocle, vLLM, and Kubeflow as named AI products. Lin
Monte Carlo positions as a data and AI observability platform for end-to-end reliability beyond classic data quality. Monte Carlo is recorded in United States. Monte Carlo publishe
SigNoz Cloud is a one-stop observability tool built on top of OpenTelemetry. Get APM, logs, traces, metrics, exceptions, AI observability & alerts in a single tool. SigNoz publishe
WhyLabs provides AI/ML observability and monitoring for data and model quality in production. WhyLabs is used for AI observability work. WhyLabs is recorded in United States. WhyLa
Products
Claimed products appear first. Ranking packs and payment do not change this list.
- Arize Phoenix· Arize AI
- Dash0· Dash0
- Datadog LLM Observability· Datadog
- Fiddler AI Observability· Fiddler AI
- Galileo· Cisco
- Honeycomb· Honeycomb
- LangDB· LangDB
- Monocle· Linux Foundation
- Monte Carlo· Monte Carlo
- Opik· Comet
- SigNoz· SigNoz
- WhyLabs· WhyLabs
Also used in this category
These products have a different primary category so they do not compete for the same ranking queries. They are listed here because buyers still encounter them in this job.
- Arize Model Monitoring· Arize AI
- Langfuse· Langfuse
- LangSmith Observability· LangChain
Related categories
Relevant procurement and assurance guides
Frequently asked questions
What is AI observability?
Telemetry products for AI systems' quality, cost and behaviour. Jobs: prompt and generation traces; cost dashboards; quality monitors.
What should not be listed as AI observability?
Products whose buyer job is agent-specific observability, generic APM, or evaluation workbenches. Those belong on their own category page so search queries are not split.
Has AI Trustmark independently assessed every AI observability supplier?
No. A category listing is descriptive. Independent assessment is shown only on company or product pages that carry Trustmark evidence.
What logging should an AI agent provide?
Buyers should be able to see who the agent acted as, which tool was called, what data was sent, what changed, and when. Logs that omit write actions or store raw customer prompts without access control are incomplete evidence.
What model or provider changes should a buyer insist on being told about?
Material change usually includes a new model family, new region, new subprocessor, new write-capable tool, or a change that affects logging, privacy or human oversight. Those changes should trigger evidence refresh rather than a silent release.
How should buyers verify where AI customer data is processed?
Ask for the named processing locations, cloud regions and any subprocessors that see prompts, files or outputs. A directory listing is not evidence of residency. Independent assessment records the locations that were in scope on the assessment date.
Does a TrustMark on one product cover the rest of the company?
No. Independent assessment is scoped to the named organisation and, where relevant, the named product. Category pages list suppliers as a topic label. They do not imply that every listed company has been assessed.
What incident-handling evidence is useful for AI suppliers?
Buyers should see how AI-specific failures are detected, contained and notified — including unsafe outputs, data leakage and unauthorised agent actions. An incident policy that never mentions models, prompts or tools is incomplete for this class of product.