OpenTelemetry LLM observability stack alternatives

If you’re looking for alternatives to an OpenTelemetry-based LLM observability stack, the best choice depends on what you want to observe:

• prompts / completions / tool calls
• latency / errors / token usage
• traces across app + model + vector DB
• evaluation / prompt debugging
• PII / compliance controls

Here are the main alternatives, grouped by style.

1) Vendor-native LLM observability platforms

These are the most direct alternatives if you want less instrumentation work.

LangSmith

• Strong for LangChain/LangGraph workflows
• Tracing, prompt/version management, datasets, evals
• Good developer UX
• Best if your stack is already LangChain-centric

Helicone

• Proxy-based LLM observability
• Captures requests/responses, costs, latency, caching
• Easy to drop in for OpenAI-compatible APIs
• Good if you want minimal code changes

Arize Phoenix

• Open-source observability + evals for LLMs
• Strong for tracing, experiments, RAG analysis
• Good local/dev workflow
• Often used alongside OpenTelemetry, but can stand on its own

Weave by Weights & Biases

• Tracing, evaluations, prompt tracking
• Good if you already use W&B for ML experiments
• Useful for agentic workflows and experiment comparison

Datadog LLM Observability

• Fits teams already using Datadog
• Good infra/app observability + LLM tracking in one place
• Better for enterprise monitoring than prompt engineering workflows

New Relic / Splunk / Dynatrace / Honeycomb

• Traditional observability vendors increasingly support LLM traces
• Best if you want unified infra + app + AI observability
• Usually less LLM-specific than dedicated tools

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2) Proxy / gateway-based observability

These focus on capturing model traffic without heavy SDK integration.

Helicone

• Most popular in this category
• OpenAI-compatible proxy
• Good cost tracking, prompt logs, caching, rate controls

LiteLLM Proxy

• Useful if you want a model gateway plus logging
• Supports many providers through one API
• Good for routing, fallback, budgeting, and visibility

OpenRouter-style gateways

• More about routing than observability
• Some logging/usage visibility, but less deep tracing

This approach is great if your main need is:

• centralized access control
• model routing
• token/cost tracking
• request/response logs

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3) Experiment/evaluation-first platforms

If you care more about quality than runtime observability:

LangSmith

• Also fits here
• Strong evals and datasets

Phoenix

• Strong for RAG evaluation and debugging

W&B Weave

• Great for comparing prompt/model iterations

Humanloop

• Prompt management, evals, feedback loops
• Useful for teams iterating on prompts and workflows

Braintrust

• Evaluation and prompt testing focused
• Good for regression testing and quality workflows

These are good when you need:

• prompt versioning
• test suites
• offline evaluations
• human feedback loops

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4) Build-your-own stack without OpenTelemetry

If you don’t want OTel, you can still build observability using:

• structured application logs with request IDs
• database/event streams for prompt records
• custom middleware around model calls
• metrics in Prometheus/StatsD
• traces via vendor SDKs instead of OTel

Typical stack:

• Logs: ELK / Loki / CloudWatch
• Metrics: Prometheus + Grafana
• Tracing: vendor-specific SDK or custom span records
• LLM telemetry store: Postgres / ClickHouse / BigQuery
• Dashboarding: Grafana / Metabase / Superset

This is best if:

• you need full control
• compliance/security requires self-hosting
• you don’t want another abstraction layer

Downside: you’ll likely rebuild features that dedicated tools already provide.

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5) Open-source/self-hosted alternatives

If you want control and lower vendor lock-in:

• Arize Phoenix
• Langfuse
• Helicone self-hosted
• LiteLLM Proxy
• Braintrust in some workflows
• OpenLIT for LLM observability instrumentation

Langfuse

Worth calling out separately:

• Open-source LLM observability platform
• Traces, prompt management, evaluation, sessions
• Good self-hosted option
• Strong fit for teams wanting an OTel-like experience without using OTel directly

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Quick recommendation by use case

Choose LangSmith if:

• you use LangChain/LangGraph
• you want dev-friendly tracing + evals

Choose Helicone if:

• you want a proxy
• you need fast setup for API logging/costs

Choose Langfuse if:

• you want self-hosted open-source observability
• you want tracing + prompt management + evals

Choose Phoenix if:

• your focus is RAG debugging and evaluation
• you want strong local/open-source tooling

Choose Datadog/New Relic/Honeycomb if:

• you already live in a general observability platform
• you want infra + app + LLM in one pane

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If you want to avoid OpenTelemetry specifically

The most common non-OTel stack looks like:

• Helicone or LiteLLM Proxy for capture/routing
• Langfuse or LangSmith for traces/evals
• Postgres/ClickHouse for storage
• Grafana/Metabase for dashboards

That gives you most of the value of an OTel stack, without needing OTel instrumentation across everything.

If you want, I can also give you:

1. a feature comparison table,
2. a self-hosted-only shortlist, or
3. the best alternative for your specific architecture.