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Choose the platform that matches how your team will operate after launch, not the one with the longest feature list. A production-grade service should reconstruct every request across model calls, retrieval, tools, retries, and context assembly, while exposing latency, token use, cost, quality, and safety. Then compare evaluation workflows, hosting and data controls, framework and APM integrations, and the vendor’s actual billing meter. LangSmith is a strong fit for LangChain or LangGraph teams; Braintrust for dataset- and experiment-led quality programs; Datadog for organizations already running its APM stack; Langfuse for open-source, self-hosted control; and Arize Phoenix for OpenTelemetry-first teams. Gateway products such as Helicone and Portkey suit API-level visibility, routing, and fallbacks, but may not replace deep application traces.
What must an LLM observability platform show in production?
The minimum useful unit is a causally complete trace: one user request linked to every model invocation, retrieval operation, tool call, retry, context-building step, and final response. A log of prompts and completions alone cannot explain why an agent was slow, expensive, inaccurate, or unsafe.
Trace the complete request graph
- Nested model spans: provider, model name, request and response timestamps, streaming status, retries, errors, and usage.
- Retrieval spans: query, index or store, filters, result identifiers, ranking data, and retrieval latency. Redact document text when policy requires it.
- Tool spans: tool name, arguments, return status, downstream latency, and whether the tool changed state.
- Context assembly: prompt-template version, system instructions, selected documents, conversation state, and truncation or summarization events.
- Outcome metadata: tenant, release, environment, feature flag, user or session identifier in a privacy-safe form, and the final user-visible result.
Measure operations, economics, quality, and safety together
Latency should be visible at both the end-to-end and span level, including time to first token and time to last token when streaming. Token counts must distinguish input, output, cached, and evaluator tokens where the provider exposes them. Cost needs the model, region, currency, and rate card used for calculation so a price change does not silently rewrite historical spend.
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Why are traces alone not enough?
Tracing tells you what happened; an evaluation system helps you decide whether the behavior was acceptable and whether a change made it better. Plan for both offline and online work before choosing a vendor.
Offline datasets and experiments
Create versioned examples that represent normal traffic, edge cases, known failures, and safety-sensitive prompts. Run candidate prompts, models, retrievers, or agent policies against the same dataset, store scores and evaluator versions, and compare results before release. A useful platform lets you retain the input, expected or reference answer when available, evaluator rationale, and the software or prompt revision used for each run.
Online scoring and sampling
After deployment, apply deterministic checks (schema validity, citation presence, latency thresholds) and model-based or custom evaluators to a controlled sample of live traces. Sampling protects budget and privacy; the sampling rule itself should be recorded so a score trend is interpretable.
Human review and regression handling
Give reviewers a queue with the trace, retrieved context, tool activity, output, and a clear rubric. Store labels, adjudication, and reviewer identity or role. When a regression appears, the platform should let you promote failing traces into a dataset, rerun them, and attach the resulting decision to a release or incident.
How should hosting and data governance affect the decision?
Observability data can contain prompts, retrieved documents, tool arguments, conversation history, and customer identifiers. Hosting is therefore a data-governance choice, not merely an infrastructure preference.
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Hosted, self-hosted, and hybrid models
- Vendor-hosted: fastest start and less operational work, but you must review processing locations, subprocessors, retention, deletion, encryption, and access controls.
- Self-hosted: keeps traces in your environment and can support stricter residency rules, but your team owns upgrades, scaling, backups, authentication, and on-call response.
- Hybrid: sends metadata or selected spans to a service while retaining sensitive payloads internally. Confirm that redaction occurs before export and that links remain useful when payloads are withheld.
Questions for a security review
- Can administrators set retention by environment, project, tenant, or trace type?
- Are prompts, outputs, and retrieved content encrypted in transit and at rest?
- Does the provider use customer data to train models, and can that use be contractually disabled?
- Are single sign-on, role-based access, audit logs, export, and deletion available at the required plan?
- Can regulated data remain in a specified region, and are evaluator calls sent to additional processors?
Redaction should happen as close to instrumentation as practical. Hash or tokenize user identifiers, remove secrets from tool arguments, and avoid recording full documents when a document ID and hash are sufficient for diagnosis.
Do we need OpenTelemetry for LLM tracing?
OpenTelemetry-compatible instrumentation improves portability and lets traces correlate with existing services, logs, and infrastructure. It is valuable, but it does not guarantee identical LLM features across backends.
What OpenTelemetry gives you
Standard trace context can connect an LLM span to the HTTP request, database query, queue, and downstream service that surround it. You can change exporters or send the same telemetry to more than one destination, reducing lock-in and making incident analysis familiar to an APM team.
What still requires validation
LLM semantic conventions and vendor interpretations may differ in how they represent token usage, model identifiers, prompts, tool calls, evaluations, and privacy attributes. During a proof of concept, inspect raw spans and verify that nested relationships, streaming timing, cost fields, and redaction survive export. A product can advertise OpenTelemetry support yet still require proprietary SDK calls for prompt versioning, annotation, or evaluator workflows.
Which platform fits which production operating model?
There is no universal winner. The following profiles describe where each product’s published emphasis aligns with a team’s existing workflow; confirm current capabilities and plan limits before purchase.
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| Platform | Best fit | Distinctive strengths | Trade-offs to test |
|---|---|---|---|
| LangSmith | Teams using LangChain or LangGraph, or teams wanting an agent-focused workflow | Framework-agnostic tracing, evaluations, monitoring, annotation and review, and automatic failure categorization | Check how well non-LangChain components, private hosting requirements, retention, and usage-based charges fit your stack |
| Braintrust | Organizations that manage quality through datasets, experiments, scores, and eval runs | Evaluation-first workflow and CI/CD quality gates | Validate deep nested traces, online operations, data residency, and the cost of processed data, scores, and evaluator calls |
| Datadog Agent Observability | Companies already operating Datadog APM, logs, infrastructure, security, and cost tooling | Correlates AI telemetry with the rest of the service map and operational stack | Model LLM-specific workflow depth, retention, span volume, and whether existing contracts cover the required features |
| Langfuse | Teams prioritizing open-source software, self-hosting, and vendor independence | Self-hostable traces, prompts, datasets, and evaluations | Budget for operating the service and verify scaling, upgrades, support, and enterprise controls |
| Arize Phoenix | OpenTelemetry-first teams wanting self-hosted tracing and evaluation | OpenTelemetry-native tracing and an evaluation workbench, with Arize AX as a commercial path | Check which capabilities require the commercial service and how your existing collectors and semantic conventions map |
| Helicone | Teams seeking gateway-level request visibility and controls | API gateway positioning, routing, fallbacks, caching, and request logs | A gateway may not see internal retrieval, tool, or context-assembly relationships unless you add application instrumentation |
| Portkey | Teams that want a managed gateway for provider access and resilience | Routing, fallbacks, caching, and API-level logs | Test whether gateway placement provides the trace depth and evaluation workflow your agents require |
How do the published prices compare?
Headline prices are not comparable until you translate them into the meter your workload generates. The figures below are examples published in 2026, not procurement quotes; plans, included volumes, retention, overages, regions, and evaluator-token charges can change.
| Product or plan | Published example | Important qualification |
|---|---|---|
| LangSmith Plus | $39 per seat per month | Seat-based example reported by LangChain in 2026; verify included traces, retention, and usage limits |
| Braintrust Pro | $249 per month | Plan price reported by LangChain in 2026; inspect data, score, and evaluator allowances |
| Langfuse Core / Pro / Enterprise | $29 / $199 / $2,499 per month | Examples reported by LangChain in 2026; self-hosting changes infrastructure and support costs |
| Datadog Agent Observability | $160 per month starting price for the first 100,000 LLM spans | Starting allowance reported by LangChain in 2026; confirm span definitions and overage rates |
| Arize AX Pro | $50 per month | Published example reported by LangChain in 2026; verify commercial-feature and volume limits |
| Helicone Pro | $79 per month | Published example reported by LangChain in 2026; gateway usage and retention may add cost |
| Portkey Production | $49 per month | Published example reported by LangChain in 2026; check request volume and gateway features |
A separate 2026 comparison reported approximate usage rates of $6.00–$8.00 per 100,000 units for Langfuse, $3 per GB plus $1.50 per 1,000 scores for Braintrust, and about $8 per 10,000 spans for Datadog. Because the meters differ, those figures cannot be converted into a fair ranking without your traffic shape.
Build a workload cost model
- Estimate daily production requests and the percentage that generate retrieval, tools, retries, or streaming spans.
- Record average and high-percentile input and output tokens for each model, including cached tokens where applicable.
- Choose an observability sampling rate and estimate bytes retained per trace, evaluator calls, scores, replay runs, and human-review volume.
- Apply the vendor’s meter—seats, traces, spans, units, gigabytes, scores, or processed data—to the same workload.
- Add storage, self-hosting infrastructure, support, regional deployment, overages, and a reserve for traffic growth.
Ask for a written example invoice using your projected monthly volume. It is the fastest way to uncover whether evaluator traffic, replay, or retention is billed separately.
How do you choose a platform step by step?
Use a time-boxed proof of concept that mirrors production rather than a feature checklist filled with toy prompts.
- Map one representative request. Draw the path from user input through retrieval, context assembly, model calls, tools, retries, and final output. Include asynchronous jobs and failure branches.
- Instrument without changing behavior. Use the same framework, model providers, retrieval system, and deployment boundaries as production. Capture trace IDs across services and redact sensitive fields before export.
- Inspect the trace. Confirm that parent-child relationships are preserved, parallel tool calls are visible, streaming timing is accurate, and token and cost fields are populated.
- Define quality and safety datasets. Include ordinary successes, known failures, adversarial inputs, retrieval misses, and structured-output edge cases. Version prompts, evaluators, and expected answers.
- Run offline and online evaluation. Compare two prompt or model revisions offline, then score a sampled stream of live traces. Test human annotation, adjudication, regression alerts, and promotion of failures into datasets.
- Apply governance controls. Test SSO, roles, audit logs, deletion, export, retention, regional processing, and redaction with realistic customer data classifications.
- Test portability. Export OpenTelemetry or compatible spans and send them to an existing collector or APM destination. Check which features disappear outside the vendor’s SDK.
- Model the bill. Use measured trace sizes, span counts, score volume, evaluator tokens, and retention—not a vendor’s smallest advertised tier.
- Run an operational exercise. Inject a slow retrieval dependency, a provider error, and a quality regression. Time how long the on-call engineer takes to identify the cause and produce a corrected dataset or release decision.
What common mistakes make an observability rollout fail?
Instrumenting only the model API
A provider log can show a prompt and completion but not which retrieved passage, tool result, retry, or context truncation caused the outcome. Instrument the application boundary and propagate trace context through every dependency.
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Recording everything forever
Unlimited payload retention increases privacy exposure and cost. Use environment-specific retention, sampling, redaction, and a deliberate policy for promoting a small set of failures into durable evaluation data.
Choosing by framework branding
A framework-native product can accelerate setup, but production systems often include custom routers, queues, databases, and tools. Recreate those components in the proof of concept and test framework-agnostic spans.
Ignoring evaluator economics
Quality checks invoke models, consume tokens, and create additional traces or scores. Budget evaluator traffic and replay separately from user traffic, and verify whether those operations count toward the paid meter.
Confusing gateway logs with causal traces
Gateways are useful for provider routing, fallbacks, caching, and API-level policy. They cannot automatically explain an internal retriever or tool chain that never passes through the gateway; retain application instrumentation when those relationships matter.
Leaving ownership undefined
Decide who maintains instrumentation, who owns evaluator definitions, who responds to cost alerts, and who can approve a release when quality and latency move in opposite directions. Without named owners, dashboards become passive logs rather than an operating system for the product.
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How should a team make the final decision?
Score each finalist against the same weighted criteria: trace completeness, offline and online evaluation, review workflow, prompt and dataset versioning, OpenTelemetry interoperability, hosting and residency, framework and APM integrations, retention and access control, enterprise support, and total cost under your measured workload.
Give the highest weight to non-negotiables such as residency, deletion, or trace depth. For the remaining criteria, use evidence from the proof of concept: a working trace, a completed regression run, a successful access-control test, and a workload-based invoice. The platform that best supports your team’s actual operating model—not the one with the most impressive demo—will produce the most durable value.
Frequently Asked Questions
Can a small team start with open-source LLM observability?
Yes, if it can operate the deployment, storage, upgrades, authentication, and backups. Start with a narrow retention window and a small representative dataset, then document the on-call burden before expanding to more services or longer retention.
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How long should an LLM observability proof of concept run?
Run it across enough traffic to include normal requests, retries, provider errors, retrieval misses, and at least one release change. A calendar duration matters less than exercising those failure modes with the same access controls and data classes planned for production.
Who should own LLM evaluation rules after launch?
A cross-functional owner is usually safest: engineering maintains instrumentation, product or domain experts define task rubrics, and security or compliance approves sensitive-data handling. Record evaluator versions and rubric changes so score movements are attributable to a deliberate change.
What should we export if we leave a vendor?
Preserve raw or normalized traces, prompt and dataset versions, evaluator definitions, scores, human labels, and release identifiers. Test an export before signing a long contract; an export that omits relationships or evaluator context may not support incident reconstruction later.
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