Opik Alternatives: An Exit and Replay Plan for Traces and Evals

Opik combines LLM tracing with offline evaluation. Its traces contain nested spans for model calls and tools. They also capture other application steps. Its evaluation workflow binds a task to a dataset and scoring functions, then records an experiment. An alternative has to preserve that operating model or provide an explicit mapping. Comparing dashboard screenshots misses the expensive part: replaying historical work without losing lineage or changing the meaning of a metric.

TL;DR

• Inventory Opik traces and spans separately from datasets and experiments because each object has different lineage and replay requirements.
• Freeze prompt versions, metric code, thresholds, and model settings. Freeze the sampled test rows too.
• Export raw objects with stable IDs and timestamps. Then prove they can be reconstructed outside Opik.
• Run both systems on the same fixed corpus and compare per-sample outputs rather than only aggregate scores.
• Exit only after evaluator parity and missing-field handling have passed a signed acceptance test. Evidence retention and rollback must pass it too.

Start with the objects that must survive

Opik's trace logging documentation distinguishes a trace from its spans. A trace is the complete operation, while spans are nested work such as an LLM call or tool invocation. Names and types matter. Inputs and outputs can matter during analysis too. The same applies to tags and metadata, along with model details and usage, plus project assignment.

Create an inventory before choosing an alternative. For traces, record the trace ID and parent relationships, plus the project and environment. Record timestamps and duration. Record status too. Keep the input and output fields, plus tags and feedback scores. Capture model identity and token usage. Keep the cost fields actually used by your team. For spans, include the parent span and ordering, plus type and errors. Include any custom metadata that supports debugging.

I would reject a migration plan that promises "all telemetry" without naming these fields. Put one real trace on a screen and expand its tool span. Ask the candidate platform to reconstruct the same tree. A missing parent ID turns a useful execution path into a bag of events. The LLM observability guide explains the monitoring layer; an exit plan proves custody of its underlying records.

Datasets and experiments need a second mapping

Opik's evaluation documentation describes an offline run in six parts. Add tracking and define the task, then choose a dataset and metrics. Execute the evaluation. Analyze the results. The resulting experiment links dataset rows and task outputs to scoring results, and that link must survive migration.

Inventory dataset names and IDs, plus row IDs and schemas. Also inventory attachments and version behavior, plus tags and creation timestamps. Then inventory experiments and their dataset reference. Record the prompt or task version and model parameters separately. Preserve feedback scores and experiment-level scores, plus errors and run metadata. Preserve deleted or superseded objects according to your retention policy rather than silently exporting only the current UI state.

The candidate does not need identical object names, but it needs a documented conversion. An Opik dataset might be a versioned test set elsewhere, and an experiment might be a run. The migration worksheet should state which fields map directly and which are stored as metadata. It should also name fields that lack a destination. Treat an unmapped field as a decision. Never bury it in a footnote.

Evaluator parity is an executable test

Metric names rarely establish parity. Two products can both offer a hallucination metric while using different prompts and grader models. Their normalization and retry behavior can differ too. Thresholds can also differ. Export the metric definition where possible. Preserve custom scoring code and dependencies. Keep grader prompts and parsing rules. Record model settings and the expected output range.

Build a parity corpus containing clear passes and clear failures. Add borderline cases and malformed outputs, plus timeouts and missing values. Run the incumbent evaluator and the candidate against exactly those saved task outputs so model generation variance stays out of the comparison. Compare each sample's raw score, label, explanation, and error state before calculating aggregate means.

Set tolerances before running the test. Exact metrics should produce an exact match across both systems. Model-graded metrics need an agreed range and a process for reviewing flips near the release threshold. If the candidate cannot execute the same evaluator, label that metric as redesigned and re-baseline it. Calling a redesign a migration creates false continuity.

The DeepEval alternatives guide provides adjacent tool categories. My preference is to keep evaluation policy in source control even when the platform offers a visual editor. A pull request shows the grader prompt that changed; a renamed dashboard card does not.

Prompt and metric migration require version custody

Prompts appear in application code and observability metadata. They also appear in experiment definitions and SDK objects. Evaluation tasks use them too. Identify the system of record for each prompt before export. Preserve template text and variables, plus model defaults and tags. Keep the owner and creation time. Keep links to experiments. Retain a content hash so the same prompt can be recognized after its platform ID changes.

Metrics need similar custody. Move custom functions into a repository. Add tests and a dependency lock. For vendor-provided evaluators, record the visible configuration and version. If the underlying implementation is unavailable, treat the result as vendor-specific and keep a reference corpus with expected outputs. That corpus is the regression test for future changes.

Do not migrate names without semantics. Labels such as "quality" and "helpfulness" mean nothing until the scoring instructions and thresholds are visible. The same applies to "safety." Put the exact release rule beside each metric. Then require the candidate to produce the fields consumed by CI and dashboards, along with alerts and review queues.

Exports must be tested before notice is given

An export is usable only when it can be parsed and joined. It must also be replayable outside the product. Retrieve a bounded set of traces and spans plus their feedback scores. Retrieve a dataset and its experiments. Save raw responses before transforming them, then create a manifest containing export time, API or method, object counts, and pagination state. The manifest should also carry the schema version and checksums.

Test referential integrity across the exported object set. Every span should resolve to its parent trace. Every experiment item should resolve to a dataset row. Metric results should retain the evaluator and task output they scored. Sample attachments and large payloads need a deliberate path, as do redacted or truncated values.

Test rate limits and pagination against production-shaped volumes. Test deletion behavior too. A ten-row export proves the syntax while saying little about three years of traces. Keep an encrypted copy in customer-controlled storage and verify that another engineer can rebuild the selected records without an Opik session.

Dual run turns selection into evidence

Instrument one representative application so the same invocation reaches Opik and the candidate. Another option is to mirror telemetry after the application creates it. Avoid sending two model requests when one response can feed both systems. For offline evaluation, use one frozen dataset and task output set with one evaluator version.

Run long enough to cover routine requests and error paths. Compare trace counts and span trees, then check latency fields and token and cost attribution. Compare feedback attachment, searchability, evaluator outputs, and alert behavior. Check export completeness and record expected differences. A candidate may calculate cost using another price snapshot, for example, while still preserving usage correctly.

Keep rollback available during the dual run. The old instrumentation and dashboards should remain intact until the candidate passes acceptance. The AgentOps comparison shows why observability records differ from authorization evidence. Verify that the replacement preserves the policy-decision identifier beside each trace without treating the trace as proof of authorization.

DeepInspect

DeepInspect sits on authenticated HTTP model routes deliberately sent through its proxy. It evaluates identity, data classification, destination, and policy before forwarding allowed traffic, then records the decision. That job is distinct from Opik's trace and evaluation workflow.

During an observability migration, preserve the transaction identifier that connects a model execution to its authorization record. The candidate can then explain what the application and model did, while DeepInspect shows why the request was permitted or refused. Book a technical deep dive at deepinspect.ai.