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There is no universal winner. OpenSearch and Elasticsearch (Elastic Observability) place AI-agent telemetry inside broad search and observability stacks. Purpose-built products such as Langfuse and Arize Phoenix put AI traces, evaluation, prompt iteration, and experimentation at the center. Choose by the provider and framework you must instrument, the detail your traces need, your evaluation workflow, deployment and data controls, operating skills, and expected ingestion and retention costs—not by a feature checklist alone.
What AI-agent observability must show
An AI agent is a chain of operations rather than one request. A useful investigation may start with a user message, follow one or more model calls, show the tools selected by the model, trace retrieval from a knowledge store, and end with a response or an error. Request-level logs rarely preserve those relationships or the timing and token data needed to explain an outcome.
In OpenTelemetry terminology, a trace represents the full execution flow and a span represents an operation within that flow. For an agent, spans can represent model inference, tool execution, retrieval, a guardrail check, or an application step. The exact attributes depend on the instrumentation. A backend cannot display fields that the SDK or application never emitted.
- Model activity: model identifier, request and response events, duration, errors, and token consumption where instrumentation supplies them.
- Tool activity: which tool ran, its arguments and result, duration, retries, and failures.
- Retrieval: query text or an appropriately redacted representation, retrieved documents or IDs, ranking information, and latency.
- Relationships: parent-child links that explain which model decision triggered a tool or retrieval step.
- Operational context: service, environment, deployment, user or session identifiers, and correlation IDs subject to your privacy policy.
- Quality signals: evaluation scores, human labels, safety outcomes, and links to prompts, datasets, or experiment versions when your workflow records them.
Before comparing products, define the minimum span set and attributes that answer your incidents and quality questions. A visually polished trace made mostly of empty spans is less useful than a simpler system with complete model, tool, and retrieval context.
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At-a-glance comparison
| Option | Primary orientation | Documented AI-agent capabilities | Important qualification |
|---|---|---|---|
| OpenSearch with Agent Traces | Open-source search and observability stack with an agent-tracing interface | OpenTelemetry GenAI semantic-convention ingestion, trace storage in OpenSearch, hierarchical trace trees, DAGs, timelines, span details, and metrics | Requires an OpenSearch cluster, OpenSearch Dashboards, and Data Prepper; Agent Traces is documented as introduced in OpenSearch 3.6 |
| Elasticsearch / Elastic Observability | Broad Elastic observability platform with LLM integrations and APM tracing | Provider log and metric integrations plus APM tracing that can expose model, duration, errors, token consumption, and prompt/response interaction | Provider, language, framework, and instrumentation status vary; the cited EDOT LLM use-case documentation labels its described support as a technical preview |
| Langfuse | AI-application tracing and development workflow | Traces for prompts, responses, tokens, latency, tools, and retrieval, alongside evaluation, prompt management, experiments, datasets, and dashboards | These capabilities are the vendor’s product description; compare deployment, retention, access controls, and integration coverage for your use case |
| Arize Phoenix | Open-source AI/LLM experimentation, evaluation, and troubleshooting | OpenTelemetry and OpenInference instrumentation support, with an AI-focused workflow | The documentation result used for this comparison was marked as an older crawl; confirm the current release, hosting choices, and licensing |
The table describes documented product direction, not a performance test or a price ranking. Exact capabilities can change with release, edition, provider, language, and hosting model.
OpenSearch Agent Traces
What the feature is
OpenSearch describes Agent Traces as an interface for exploring, debugging, and monitoring execution traces from large-language-model agents. Its documentation characterizes the feature as observability for generative-AI applications and LLM agents. The documented Agent Traces capability was introduced in OpenSearch 3.6.
The architecture follows a recognizable pipeline: applications emit OpenTelemetry data using GenAI semantic conventions; Data Prepper processes the telemetry; traces are stored in OpenSearch indices; and OpenSearch Dashboards presents the investigation views. This keeps agent telemetry in the same search and observability environment as other OpenSearch data.
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The documented workflow lists an OpenSearch cluster, OpenSearch Dashboards, and Data Prepper as prerequisites. Treat those as components to verify rather than assuming that every managed or packaged deployment includes them in the same way.
- Confirm that the target cluster and Dashboards versions support the Agent Traces feature and that any required feature flags are enabled.
- Deploy or obtain a compatible Data Prepper pipeline for the OpenTelemetry GenAI data you plan to send.
- Instrument the agent and its model, tool, and retrieval operations with the semantic conventions and attributes your investigations require.
- Send a small, non-sensitive test trace and verify that it reaches the intended OpenSearch index.
- Open the Agent Traces views and check the hierarchy, timing, span attributes, and error details before increasing traffic or retention.
What engineers can inspect
The documented interface includes hierarchical trace trees, directed acyclic graphs, timelines, span details, and metrics. A tree or DAG helps answer “which step caused this result?” A timeline exposes serial tool calls, parallel work, and waiting time. Span details are where token attributes, model information, tool arguments, or retrieval context appear—if instrumentation supplied them. Metrics help move from one failed trace to aggregate behavior.
Where OpenSearch fits best
OpenSearch is a strong candidate when a team wants agent traces alongside logs, metrics, and traces already held in an OpenSearch-centered architecture, and is prepared to operate or procure the ingestion components. Its OpenTelemetry-native positioning can reduce the need for a proprietary agent format, but it does not remove the work of choosing conventions, securing sensitive content, sizing storage, or validating dashboards.
Questions to settle before adoption
- Does the chosen OpenSearch release and deployment expose Agent Traces, and is the feature enabled by default?
- Which agent frameworks, model providers, and languages have maintained instrumentation that emits the spans you need?
- Will Data Prepper parsing preserve prompt, response, token, tool, and retrieval attributes without unacceptable cardinality or privacy risk?
- Do managed offerings provide the same components and controls as a self-managed installation?
- What index, shard, sampling, and retention design is appropriate for your expected trace volume?
Elasticsearch and Elastic Observability
Two documented telemetry paths
Elastic documents LLM observability through integrations that ingest provider logs and metrics and through APM tracing. Integrations can be useful when a provider already exposes operational telemetry. APM tracing is the path for following an individual application execution and correlating model activity with the rest of the service.
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The documented tracing fields include model, duration, errors, token consumption, and prompt/response interaction. Whether each field appears depends on the provider, language, SDK, framework, and instrumentation path. Validate the exact combination rather than inferring support from a general product page.
OpenTelemetry and EDOT status
Elastic documents OTLP intake and its own distributions. The cited EDOT LLM use-case page shows different provider and language combinations and explicitly labels the described instrumentation as a technical preview. That status matters: preview behavior, schema, support commitments, and availability can differ from generally available features.
Elastic’s broader OpenTelemetry documentation also warns that some contribution-based collector and SDK approaches do not provide Elastic enterprise APM features or Elastic implementation support. A pipeline can be standards-compatible and still lack the dashboards, support, or enrichment your operations team expects.
Where Elastic fits best
Elastic is a candidate for organizations already invested in Elastic logs, metrics, traces, security, and APM, or for teams that want AI telemetry correlated with those signals. Reusing identity, alerting, retention, and incident practices may reduce platform sprawl. The trade-off is that you must check data-schema fit and instrumentation maturity for every provider and framework, especially when the required path is preview or third-party supplied.
Validation checklist
- Record the Elastic product and agent versions you will deploy.
- For every model provider, confirm whether logs, metrics, APM spans, or all three are supported.
- Confirm the supported language and framework for your application’s SDK version.
- Determine whether the integration is generally available, preview, or community-maintained.
- Test prompt and response redaction, token fields, tool spans, and retrieval spans with representative traffic.
- Verify which Elastic enterprise APM features and support channels apply to your chosen collector and SDK path.
Purpose-built platforms: Langfuse and Arize Phoenix
Langfuse
Langfuse presents tracing as the foundation for debugging generative-AI applications. Its product description includes prompts, responses, token use, latency, tools, and retrieval in traces. It also places LLM-as-a-Judge evaluation, prompt management, experiments, datasets, and custom dashboards in the same AI-engineering workflow.
This orientation is valuable when the development loop is the central problem: compare prompt versions, connect production examples to a dataset, score quality, and investigate a bad answer without assembling those functions from a general observability stack. Confirm how your required frameworks emit data, how sensitive prompt and response content is handled, and which deployment and retention terms apply.
Arize Phoenix
Arize describes Phoenix as an open-source tool for experimentation, evaluation, and troubleshooting of AI and LLM applications. It states that Phoenix works with OpenTelemetry and OpenInference instrumentation, which can make it a useful AI-focused destination for teams already adopting those standards.
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The documentation result available for this comparison was marked as an older crawl. Treat current release, hosting, and licensing details as items to verify before making an architecture decision. Also test whether Phoenix’s current integrations emit the exact model, tool, retrieval, and evaluation fields your application needs.
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What specialists change—and what they do not
A specialist platform may shorten the path from a trace to an evaluation or prompt experiment. It does not automatically solve instrumentation completeness, privacy classification, sampling, retention, access control, or incident response. A general observability stack may offer stronger existing operations and correlation with infrastructure signals, while an AI-focused tool may offer a more direct quality-development workflow. Some teams will use both, with deliberate data boundaries and a clear system of record.
Compare the platforms on the questions that affect production
| Decision axis | Questions to answer | Evidence to collect in a proof of concept |
|---|---|---|
| Instrumentation coverage | Does the exact provider, framework, language, and version emit the required spans and attributes? Is support generally available, preview, or third-party? | A compatibility matrix and captured sample traces for every production path |
| Trace usefulness | Can an engineer inspect model calls, tool calls, retrieval, errors, timing, token usage, and parent-child relationships? | Five successful and five failed traces with expected fields present and correctly related |
| Evaluation workflow | Can production traces connect to datasets, experiments, prompt changes, and quality scores? | A repeatable evaluation run linked to a trace and a versioned prompt or model |
| Platform fit | Can the team reuse its OpenSearch or Elastic operating practices, or does an AI-focused workflow better match developer needs? | Time to investigate an intentionally seeded tool, retrieval, and model failure |
| Deployment and data handling | Is self-management or hosted operation available for the selected edition? Where do prompts, responses, and sensitive attributes go? | Data-flow diagram, residency statement, role model, redaction test, and deletion/retention behavior |
| Cost and operations | What are ingestion, retention, compute, storage, sampling, support, and staffing costs at expected trace volume? | A workload estimate using your own span counts and retention period; do not substitute unrelated list prices |
| Maturity and support | Is the exact feature stable and supported for your deployment? | Release status, support scope, upgrade policy, and an owner for each integration |
No aligned vendor pricing or independent benchmark establishes a universal cost or performance ranking. Model the workload you will actually send: traces per request, average spans, prompt and response sizes, sampling rate, retention, evaluation artifacts, and search frequency.
A practical selection process
1. Start with incident and quality questions
Write the questions your team must answer, such as “Why did this request call the wrong tool?”, “Which retrieval step added latency?”, “Did token use change after the prompt update?”, or “Which production examples failed the quality rubric?” Map each question to required spans, attributes, and evaluation records. This prevents choosing a platform because it displays attractive but incomplete traces.
2. Inventory the real application matrix
List every model provider, model family, agent framework, language, SDK version, deployment region, and execution mode. Mark each proposed instrumentation path as generally available, preview, or third-party. The matrix should include fallback models and background jobs, not only the primary chat path.
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Create a small test scenario that performs a model call, a tool call, a retrieval operation, a retry, and a controlled failure. Include parallel work if the agent uses it. Send the same fixture to each candidate where practical. Compare parent-child relationships, timestamps, token fields, errors, redaction, and searchability rather than counting dashboard tiles.
4. Test the developer workflow
Ask an engineer unfamiliar with the fixture to find the injected failure, identify the responsible span, inspect the inputs and outputs permitted by policy, and connect the trace to an evaluation result. Record the steps and time required. Repeat for a latency investigation and a prompt-regression investigation.
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5. Model data handling and operating cost
Classify prompts, responses, tool arguments, retrieved documents, identifiers, and evaluation labels. Decide what must be redacted, hashed, sampled, or excluded. Then estimate storage and compute from measured payload sizes and retention, adding the operational work for upgrades, collectors, dashboards, access reviews, and incident response.
6. Choose a system of record and an escape path
Decide whether the general observability stack or the AI-focused platform is authoritative for traces, evaluations, and incident correlation. Preserve OpenTelemetry-compatible export where possible so a change in backend does not require rewriting application instrumentation. Document what data is intentionally not duplicated because of privacy or cost.
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Implementation blueprint for OpenTelemetry-based agents
- Define trace boundaries. Create one trace for the user-visible agent execution or background job, and propagate context through model, tool, retrieval, and guardrail operations.
- Name spans consistently. Use stable operation names that distinguish model inference, tool execution, retrieval, and application orchestration. Include service and environment attributes so traces can be filtered.
- Capture useful, governed attributes. Record model, duration, status, token counts, and provider metadata when available. Treat prompts, responses, tool arguments, and retrieved content as sensitive by default; apply redaction or exclusion before export.
- Preserve parent-child relationships. A tool span should remain a child of the model decision or orchestration step that caused it. Parallel branches should retain their shared parent and independent timing.
- Instrument failures explicitly. Set span status, record exception details, and preserve retry counts and timeout reasons. A generic HTTP 500 without the failed operation is rarely enough to debug an agent.
- Add evaluation linkage. Store trace, session, dataset, prompt-version, and evaluator identifiers in a controlled schema so quality results can be joined to operational evidence.
- Control volume. Use tail or head sampling appropriate to your incident goals, retain complete examples for evaluation, and avoid high-cardinality attributes that make indexing unpredictable.
- Verify end to end. Inspect the exported payload, ingestion pipeline, index or storage record, and user-facing trace. Confirm that timestamps, token units, and error status survive every transformation.
Common failure modes and fixes
Only the outer request appears
Cause: the application emits a server span but not model, tool, or retrieval spans. Fix: add framework or provider instrumentation where supported, then create manual spans around uncovered operations and propagate context through asynchronous work.
Traces exist but contain no prompts, tokens, or tool details
Cause: the instrumentation does not emit those attributes, a processor removes them, or privacy settings intentionally redact them. Fix: inspect the raw exported event and each ingestion transformation; document which fields are intentionally absent and add governed attributes only where policy permits.
Model calls are visible but the agent path is unreadable
Cause: missing parent-child context, inconsistent operation names, or clock differences between services. Fix: standardize context propagation and span naming, then verify timestamps and links with a multi-step fixture.
Elastic instrumentation behaves differently across providers
Cause: provider, language, framework, or release differences, including preview status. Fix: validate each combination independently and record the support status; do not infer parity from another provider’s integration.
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OpenSearch views show ingestion but not the expected agent analysis
Cause: a missing or incompatible Data Prepper pipeline, unsupported semantic-convention fields, an incorrect index mapping, or a version mismatch among cluster, Dashboards, and ingestion components. Fix: verify the documented prerequisites and versions, inspect the processed event, and test with a minimal trace before changing dashboards.
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Cause: full prompt and response bodies, long retrieval payloads, high-cardinality identifiers, excessive sampling, or long retention. Fix: classify fields, hash or omit unnecessary content, sample routine successes while retaining failures and evaluation cases, and recalculate capacity from measured payloads.
Quality teams cannot reproduce a production result
Cause: the trace is not linked to the prompt version, model configuration, retrieved context, tool result, or evaluation dataset. Fix: record immutable version identifiers and the minimum reproducibility context permitted by data policy.
Which option fits common situations?
Choose OpenSearch when
Your organization already operates OpenSearch and wants agent traces, logs, metrics, and search in that environment; you can run or procure the cluster, Dashboards, and Data Prepper components; and your team is comfortable validating OpenTelemetry GenAI instrumentation and index design.
Choose Elastic when
Your incident and application-operations workflows are already centered on Elastic, and correlating AI telemetry with existing APM, logs, and metrics is more valuable than introducing a separate AI-development platform. Make the decision only after confirming the exact provider and language path and whether it is generally available or preview.
Choose a specialist when
Your primary bottleneck is AI engineering—prompt iteration, evaluation, datasets, experiments, and troubleshooting—and the specialist’s current integrations and data controls meet production requirements. Langfuse’s documented workflow emphasizes those functions; Phoenix emphasizes open-source experimentation, evaluation, troubleshooting, and OpenTelemetry/OpenInference support.
Use a combination when
Operations needs a durable general observability record while AI developers need richer evaluation and prompt workflows. Define which system stores raw content, which stores redacted operational events, how trace IDs link the systems, and how retention and deletion requests propagate. Duplication without those rules increases cost and privacy exposure.
Questions to verify before signing off
- Which exact versions are supported today, including OpenSearch Agent Traces 3.6 or later where applicable?
- Does every production provider, framework, language, and fallback path emit the required spans?
- Are any required Elastic integrations or SDKs technical preview or community-maintained?
- Can the platform display model, tool, retrieval, timing, token, and error details with correct relationships?
- How will prompts, responses, tool arguments, retrieved documents, and identifiers be redacted, encrypted, accessed, retained, and deleted?
- What is the measured monthly volume after sampling, and what storage, compute, support, and staffing does it require?
- Can you export or preserve OpenTelemetry context if you change backends?
- Who owns upgrades, broken instrumentation, dashboard changes, and evaluation-schema maintenance?
Documentation and product status change quickly. Recheck release notes, provider coverage, hosting and licensing terms, regional availability, retention controls, and current costs on 2026-10-13 documentation or the latest vendor material available when you deploy. The evidence summarized here is documentation-based, not a hands-on benchmark, so it cannot establish a universal performance, quality, or price winner.
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