Weave Alternatives: Choose by the Evidence You Need

W&B Weave records agent sessions and LLM calls alongside tool executions, function inputs, outputs, cost, tokens, and latency. It also runs evaluations and tracks prompt versions, dataset revisions, plus model configurations. That breadth turns a Weave replacement into a data and evidence migration rather than a dashboard swap.

I would reject any shortlist built from feature checkmarks alone. Ask which records engineering and security need after the old project becomes read-only, including evidence for auditors. Then make the test physical: put one failed production trace on the screen, print its request identifiers on a sheet of paper, and test each candidate against that case.

TL;DR

• Split Weave into tracing and evaluation jobs. Map versioning, feedback, monitoring, access, and exports separately before comparing replacements.
• Test migration with one agent session and one function trace. Add an evaluation set, a production failure, and identifiers that can be replayed.
• Treat hosted and self-managed Weave as separate custody choices; self-management adds Kubernetes and ClickHouse plus object storage, backups, upgrades, and incident ownership.
• Native Weave administration sits outside DeepInspect. Only authenticated HTTP model calls deliberately routed through DeepInspect produce its policy evidence.

Weave covers several separate jobs

CoreWeave's current Weave overview describes an observability and evaluation platform. Agent tracing covers sessions and LLM calls, including tool executions, while application instrumentation records arbitrary functions and can capture inputs, outputs, cost, tokens, latency, and errors. Evaluations compare responses through scoring functions. Version tracking records prompt changes alongside dataset and model-configuration revisions.

The same page exposes a portability clue. Agent tracing uses OpenTelemetry and GenAI semantic conventions, and Weave accepts existing spans. That gives a buyer a standard transport to test, but it does not prove that every Weave object has an equivalent field in another backend.

There is another distinction to preserve. Weave keeps agent traces and instrumented function calls in separate parts of its interface, so the export inventory should do the same.

Start with the job your team actually uses. A platform bought for evaluations can accumulate production traces that incident response later depends on. A tracing deployment can also acquire annotations that become the evaluation corpus. Before a procurement team turns those categories into one score, use the LLM observability tools guide to separate them.

Four replacement paths

Each path keeps a different part of the current operating model. First, score it against the representative corpus, then discuss the contract terms with that evidence in hand.

Keep hosted Weave and tighten the operating record

This path fits teams that use Weave's combined tracing and evaluation workflow with version history and have no unresolved custody requirement. The work centers on operating records: name project owners, define retention plus export procedures, record SDK versions and access changes, then preserve offboarding plus the removal test. It avoids a migration while fixing the common evidence gap around ownership.

Export a representative session and evaluation result before choosing this path. A successful login is weak acceptance evidence. The stronger test shows that an engineer can find the same failed call using a stable identifier, identify the prompt and model version, and reproduce the evaluation inputs.

Run Weave on customer-managed infrastructure

CoreWeave's self-managed Weave guide documents a production deployment on Kubernetes. The design uses the Altinity operator and Keeper for a replicated ClickHouse cluster, backed by S3-compatible storage, and requires an existing W&B Platform installation plus a Weave-enabled license. Operators must adapt security contexts and resource sizing. They must also align storage classes plus node placement with their environment.

This option changes custody and operating responsibility without changing the Weave workflow. Platform teams own cluster security and credentials, along with backup, restore, upgrades, capacity, metrics, monitoring, and incident response for trace storage. That is a large operating surface. I prefer this route only when the custody requirement is explicit enough to justify another stateful production service.

Move tracing to an OpenTelemetry backend

An OpenTelemetry path fits teams whose primary artifact is the span graph. Export one agent conversation and one custom function trace. Compare attribute preservation and parent-child relationships, then check tool events and timestamps before inspecting query behavior, sampling, export fidelity, and restore behavior in the candidate backend.

Treat evaluation datasets and human feedback as separate migration work unless the candidate imports them with field-level parity.

This path can reduce instrumentation lock-in because Weave already accepts OpenTelemetry spans. The migration worksheet still needs a row for every Weave-specific object because OpenTelemetry carries spans without carrying project permissions or evaluator definitions. Annotation workflows and user-interface concepts require their own parity tests.

Split evaluation and production evidence

A specialist evaluation framework can own test datasets and scoring logic, with release gates attached, while another backend stores production traces. That split can work well for engineering teams that want eval-as-code and already have an observability standard.

Test every join by recording the evaluation-run identifier and code revision, followed by prompt and model versions, then add the dataset version plus production trace identifier to the same acceptance packet.

Use the DeepEval alternatives guide and Langfuse pricing analysis as adjacent category reviews. During procurement, validate current product behavior against first-party documentation. Let the artifact drive the decision instead of a broad claim that one platform replaces everything.

The migration worksheet

Create one worksheet row for each Weave project and environment. Give every row a named owner and a review date. I use these fields:

• Workload: Record the application and agent framework, followed by environment and business owner. Add the technical owner plus support contact.
• Instrumentation: Record Weave SDK versions and OpenTelemetry exporters. Add patched libraries and custom attributes, followed by sampling rules plus export endpoints.
• Trace corpus: Select one normal session and one denied tool call. Add a malformed response plus a latency incident. Preserve stable identifiers and timestamps.
• Evaluation corpus: Record datasets and scorers, followed by thresholds and model judges. Add human annotations plus release decisions and run identifiers.
• Version lineage: Join the prompt and model configuration to the dataset. Add application revision, deployment time, and trace identifier.
• Data custody: Record storage location and encryption owner. Add retention, deletion, backup, restore, export format, and archive owner.
• Access: Record project roles and service identities. Test offboarding behavior and preserve the denied-access evidence.
• Parity result: Mark each object as preserved or transformed. Use archived or intentionally retired for objects that leave the active path. Name the reviewer who accepted the result.

Use a historical corpus for the first worksheet pass. Then capture live traffic during a bounded dual-run window, compare identifiers and field values, and investigate gaps before moving the write path. A screenshot of two similar trace trees cannot prove parity.

Runtime authorization remains a separate control

Weave observes and evaluates application behavior. Its documented workflow records calls and can run production scorers and guardrails, producing evidence that is useful for debugging and model quality. A separate authorization decision remains for a customer-controlled model route: the policy must determine whether this authenticated caller may send this classified prompt to this model.

Keep that decision separate in the architecture and in the migration worksheet. The post-authentication gap appears after identity has been established but before request entitlement has been evaluated. A production trace can show that a call occurred. An independent policy record shows the rule and supplied identity, then adds classification, destination, policy version, and the permit or deny result before forwarding.

Native Weave administration and hosted trace capture are outside DeepInspect's scope. The same boundary covers self-managed cluster operations, evaluations, and project permissions. DeepInspect applies only to authenticated HTTP LLM requests deliberately routed through its proxy. It cannot prove that a Weave export was complete or that a ClickHouse backup can be restored.

DeepInspect

DeepInspect can supply one bounded artifact in a Weave migration: the independent policy record for each routed LLM call. The stateless proxy evaluates the identity context provided by the application and the model route, applies data classification plus current policy before forwarding the request, and then writes the decision evidence outside the application's trace write path.

Keep the DeepInspect request identifier beside the old and new observability identifiers during dual run. That join lets reviewers compare trace coverage without claiming that either observability platform administered the policy decision. Signed audit logs for AI requests describes the integrity properties for that routed-call record. Native Weave administration and migration remain with the platform team.

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