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Langfuse, Arize Phoenix, and Opik are the clearest starting points for teams that need to observe a self-hosted LLM application. Langfuse combines tracing with prompt management, evaluation, and cost and latency views. Phoenix focuses on OpenTelemetry/OpenInference trace inspection, evaluation, prompt iteration, and experiments. Opik emphasizes turning production traces into test cases and monitoring quality, latency, cost, and errors. None is a proven universal winner: the right choice depends on what your instrumentation already emits, which development workflow you need, how much operational work you can own, and how you will protect prompts and user data. This guide explains those differences and gives you a practical selection and deployment framework.
What is LLM observability, and what does a trace contain?
LLM observability is the practice of recording and inspecting an AI application’s execution so you can explain a result, locate latency, and evaluate output quality. A trace represents one request or agent run; spans inside it represent work such as a model call, retrieval query, embedding operation, tool invocation, database access, or external API request. Multi-turn sessions and agent graphs can connect several traces into a user-level view.
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This is broader than logging the final model response. A useful trace can show the retrieved context supplied to the model, the prompt version, token or cost attributes when available, tool arguments and results, retries, errors, and timing for each step. The exact fields depend on your instrumentation and on what the platform stores. Observability therefore answers three different questions:
- What happened? Follow the complete path from user input through retrieval, tools, model calls, and the returned answer.
- Why was it slow or unreliable? Compare span durations and error locations instead of treating the model as a single opaque timer.
- Was the result useful? Apply human feedback, automated evaluators, test datasets, or production scoring to outputs and traces.
Recording more data improves diagnosis but increases privacy, storage, and access-control obligations. Decide deliberately which prompts, documents, tool payloads, and user identifiers are captured.
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Which self-hostable platforms should you compare first?
The following shortlist reflects capabilities documented by the projects, not an independent benchmark or feature score. Confirm current releases, licenses, and deployment instructions before committing.
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| Platform | Documented strengths | Best comparison question | Evidence |
|---|---|---|---|
| Langfuse | Traces LLM and non-LLM calls; sessions and user tracking; prompt management; evaluation; cost and latency visibility; capture through SDKs, integrations, OpenTelemetry, or a gateway. The project describes itself as open-source and self-hostable. | Do you want one integrated workspace for tracing, prompts, and evaluation, and do its license boundaries and operating requirements fit your team? | Langfuse documentation |
| Arize Phoenix | Trace inspection, evaluation tests, prompt iteration from production examples, and experiments that compare changes on the same inputs. It is built on OpenTelemetry and uses OpenInference instrumentation. | Will an OpenTelemetry/OpenInference-oriented debugging and evaluation workflow match the way your application is instrumented? | Phoenix documentation |
| Opik | Self-hosting guidance; records LLM calls, tool invocations, and agent steps; supports creating test suites from traces and online scoring or monitoring of feedback, latency, cost, and errors. | Is a trace-to-test workflow, followed by production scoring and monitoring, central to your development process? | Opik documentation |
| Instrumentation libraries (for example, OpenLLMetry) | Emit LLM-aware spans to an OpenTelemetry backend. Instrumentation alone is not automatically a complete product with a trace interface, storage, evaluation workflow, or administration. | Do you already operate an OpenTelemetry backend and only need libraries that instrument your framework and model calls? | Verify the exact project’s current documentation before selection. |
The shortlist is not exhaustive. A current secondary comparison lists additional open-source and commercial candidates; use it to discover names, then validate each claim in the project’s own documentation: VDF.ai’s October 2, 2026 comparison. A separate June 13, 2026 overview can provide more discovery questions, but its time-sensitive ownership and maintenance statements also require direct verification: The Context Company’s comparison.
How does Langfuse differ from Phoenix and Opik?
Langfuse: an integrated trace, prompt, and evaluation workspace
Langfuse documents capture across model and surrounding application calls, including retrieval, embeddings, APIs, sessions, and agent graphs. Its workflow is suited to a team that wants to inspect a run, manage the prompt involved, attach evaluations, and examine cost or latency in one system. Capture can come from SDKs and integrations as well as OpenTelemetry or a gateway, which gives teams several adoption paths. Read the project’s current self-hosting and license terms rather than assuming that every module has identical conditions.
Phoenix: OpenTelemetry/OpenInference debugging and experiments
Phoenix describes a loop that starts with inspecting traces, then uses production examples for prompt iteration, evaluation tests, and experiments comparing alternative changes on identical inputs. Its foundation is OpenTelemetry with OpenInference instrumentation. That orientation is attractive when your organization already standardizes on OpenTelemetry or wants instrumentation that can be reused across backends. It does not mean that a shared telemetry vocabulary makes storage schemas, user interfaces, evaluation features, or operating procedures interchangeable.
Opik: moving from traces to tests and online monitoring
Opik’s documentation centers on recording LLM calls, tool invocations, and agent steps, then deriving test cases from observed traces. It also documents online evaluation and monitoring for feedback, latency, cost, and errors. This is a natural fit when production behavior should continuously feed regression tests and scoring rules. Confirm how its current release handles the specific model providers, frameworks, and deployment mode in your application.
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Instrumentation-only approaches
An instrumentation library can be the right answer when your organization already runs an OpenTelemetry collector and backend. You gain control over the destination and can change analysis tools later, but you must supply the trace UI, retention policy, evaluation data model, access controls, and operational ownership yourself. Treat “supports OpenTelemetry” as a transport or compatibility statement, not proof that a platform provides every workflow you need.
What should you compare before choosing a platform?
1. Capture coverage
Draw the path of a real request and mark every operation you need to debug: model calls, retrieval, reranking, embeddings, tools, API requests, queues, and multi-turn sessions. A platform that records only model spans may leave the actual failure outside the trace. Ask whether agent steps and user or session grouping are supported, and how you will avoid storing sensitive payloads unnecessarily.
2. Instrumentation fit
Inventory your frameworks, model providers, and languages. Compare native SDKs and integrations with OpenTelemetry and OpenInference support, then estimate the code changes and maintenance required. OpenTelemetry’s Generative AI semantic conventions provide a vocabulary for GenAI telemetry; they improve portability, but adapters may still differ in attribute names, sampling behavior, and feature coverage.
3. Workflow depth
Separate the activities your team actually performs:
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- Debugging: search, inspect nested spans, compare runs, and find errors.
- Prompt iteration: edit or version prompts and replay representative inputs.
- Evaluation: run automated or human scoring against datasets and attach feedback to traces.
- Experiments: compare changes on the same inputs so a prompt or model change is attributable.
- Production monitoring: watch quality-related feedback, latency, cost, and error rates over time.
Do not infer that a product includes all five because its homepage mentions “observability.” Verify each workflow and its export or API options.
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4. Deployment and operations
Read the self-hosting documentation for the exact release you plan to run. Record required services, supported databases or object storage, upgrade steps, backup and restore procedures, scaling guidance, health checks, and how access is administered. Decide who owns patching, on-call response, retention changes, and disaster recovery. No production sizing or performance benchmark is established for this comparison, so validate capacity with your own trace volume and payload sizes.
5. Data governance
Write a data-flow diagram before enabling capture. It should identify where prompts, retrieved documents, outputs, tool arguments, user identifiers, and evaluator comments are stored; which components can read them; and how long each class is retained. Implement redaction or field-level filtering at the instrumentation or collector layer where possible. Test that secrets, authentication headers, and regulated content are excluded before sending a trace to any shared service.
6. License and project continuity
Inspect the current repository license, notices, and any enterprise-only modules. “Open source” and “self-hostable” do not answer whether a particular feature can be modified, redistributed, or used in your deployment model. Review recent releases, issue activity, security advisories, and governance, and document the version you approved. Ownership or maintenance reports are time-sensitive; verify them directly rather than relying on a comparison article.
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How can you design a self-hosted observability architecture?
Use a layered design so you can change the analysis platform without rewriting every application integration.
- Instrument the application. Add the platform SDK, an OpenTelemetry integration, or an instrumentation library at model, retrieval, tool, and session boundaries. Define a stable run or request identifier.
- Normalize telemetry. Use OpenTelemetry conventions where they fit, and document any custom attributes for prompt versions, tenant IDs, evaluator names, or experiment labels.
- Control collection. Configure sampling, payload filters, redaction, and failure behavior. Decide whether an observability outage should block a user request or only drop telemetry.
- Route to the backend. Send spans through your collector or directly to the self-hosted platform according to its supported configuration. Keep credentials and endpoints in secret management rather than source code.
- Separate sensitive and routine data. Apply shorter retention or stricter access to raw prompts and retrieved documents than to aggregate latency or error metrics. Use tenant boundaries if multiple teams share the installation.
- Connect evaluation. Define representative datasets, automated checks, human-review fields, and feedback labels. Link scores to trace and prompt versions so a failing result can be reproduced.
- Operate it like production software. Monitor ingestion health, storage growth, queue backlogs, backups, failed upgrades, and administrative access. Exercise restore procedures before the telemetry becomes your only record of a production incident.
How do you instrument a self-hosted LLM application with OpenTelemetry?
OpenTelemetry is a conventions and transport layer, not a complete observability product. Start by choosing an SDK or integration for your language and framework, then emit spans for the boundaries that matter to your diagnosis. Include model and provider metadata, operation names, timing, status, and a correlation ID; add prompt or dataset identifiers rather than raw sensitive text when policy requires it.
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Next, send telemetry to a collector or backend that your chosen platform supports. Validate one complete request in a development environment: the trace should show the parent request, nested retrieval and tool work, the model call, errors, and total duration. Compare the emitted attributes with the current GenAI semantic-convention specification, but retain a local schema document for fields that are platform-specific. When changing backends, test sampling, timestamps, nested spans, and payload redaction; a syntactically valid OpenTelemetry export can still lose information your evaluation workflow needs.
What deployment checklist catches the expensive mistakes?
- Confirm the project’s current license and whether the required UI, evaluators, or integrations have separate terms.
- Pin a tested release and record the upgrade and rollback path.
- Estimate storage from trace count, span count, payload size, evaluation artifacts, and retention—not request count alone.
- Set authentication, role-based access, tenant boundaries, encryption, and audit logging before onboarding production data.
- Define redaction rules for prompts, retrieved text, tool arguments, headers, and personal identifiers, then test them with adversarial samples.
- Decide sampling and tail-based retention for high-volume or low-value traces while preserving failures and evaluation examples.
- Back up the metadata and evaluation stores, and perform a restore drill on an isolated environment.
- Alert on ingestion failures and backend health separately from application failures; otherwise a broken observability path can remain invisible.
- Document who can change prompt versions, evaluators, retention, and access policies.
Which platform fits common team situations?
| Your priority | Most relevant starting point | What to verify in a proof of concept |
|---|---|---|
| One integrated workflow for traces, prompts, evaluations, cost, and latency | Langfuse | Coverage of your retrieval and tool chain, prompt-version controls, data filtering, and self-hosting operations. |
| OpenTelemetry/OpenInference-based debugging and controlled prompt experiments | Arize Phoenix | Instrumentation effort, trace fidelity, evaluator workflow, experiment comparison, and license or deployment details. |
| Turning production traces into regression tests and continuous scoring | Opik | Trace-to-test ergonomics, online evaluator behavior, monitoring dimensions, and scaling under your retention policy. |
| Existing OpenTelemetry backend with a need for LLM-aware spans only | Instrumentation library plus your backend | Whether you can provide search, storage, evaluation, redaction, access control, and administration without a dedicated platform. |
Run the same small proof of concept against each candidate: instrument one normal request, one retrieval failure, one tool error, and one multi-turn session; then create an evaluation case and export the resulting data. Measure engineering effort and operational clarity, not just the number of checkboxes in a feature list.
What are the most common selection and rollout pitfalls?
Assuming “open source” means effortless
Self-hosting transfers responsibility for upgrades, backups, security, scaling, and incident response to you. Budget that work explicitly and verify the project’s deployment prerequisites.
Capturing everything by default
Raw prompts and retrieved documents may contain secrets or personal data. Start with a minimum useful schema, redact aggressively, and add fields only when a diagnosis or evaluation requires them.
Instrumenting only the model call
A slow retriever, failed tool, or malformed context can look like a model problem if those spans are absent. Trace the application path around the model, not just the provider request.
Confusing telemetry compatibility with workflow compatibility
Two products can accept OpenTelemetry while presenting different trace models, evaluators, prompt stores, and retention controls. Test an actual trace and evaluation loop before promising portability.
Choosing from a feature matrix alone
Documentation describes intended capabilities, not your production performance, privacy posture, or total operating cost. Validate with representative traffic and involve security and platform owners in the proof of concept.
Is there a universal winner?
No evidence-backed universal ranking exists among these tools. Langfuse is the logical first comparison for an integrated tracing-and-prompt workflow; Phoenix is compelling when OpenTelemetry/OpenInference debugging and experiments are central; Opik deserves close examination when traces must become tests and online scores. An existing OpenTelemetry backend may make instrumentation-only tooling the simplest route. Make the decision from capture coverage, workflow requirements, governance, license terms, and your capacity to operate the stack.
Frequently Asked Questions
Can an observability platform store prompts and user data safely?
It can, but safety depends on your configuration rather than the product label. Classify payloads, redact secrets and personal data before export, restrict access by role or tenant, encrypt storage and transport, and set retention periods that match your policy.
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Not always. A native SDK may be the fastest way to capture platform-specific features, while OpenTelemetry can provide a common transport and vocabulary; many teams use both, provided they prevent duplicate spans and document which attributes are authoritative.
How should I estimate storage for self-hosted traces?
Use a sample of real requests to measure spans and payload bytes per run, then multiply by expected traffic and retention. Include evaluation records, indexes, retries, and backups, and model separate policies for raw content and low-cardinality metrics.
What should a proof of concept test besides a successful model call?
Include a retrieval timeout, a tool failure, a multi-turn session, a redaction case, and an evaluation or feedback record. Check whether each event is searchable, whether timing is preserved, and whether an operator can restore the data after a simulated backend failure.
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