Vendor Observatory

Revealed Preference

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v0.2.0
Home/LLM Observability
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LLM Observability

LLM tracing, prompt analytics, cost tracking

Each prompt simulates a real developer scenario asking AI coding assistants to recommend a llm observability vendor. Below: which vendors were recommended, how well they addressed constraints, and the reasoning behind each recommendation.

Top Vendor

braintrust

4 of 5 recommendations

Responses

30

across 3 prompts

Constraint Coverage

18%

13 constraints tracked

Platforms Tested

claude_codecodex_cli

Vendor Leaderboard

#VendorRecommendationsShare
1braintrust4
80%
2langfuse1
20%

Prompt Breakdown

LLM Observability for Customer Support Bot

Flying blind — no quality scoring, can't find bad conversations, token costs climbing at $40/day

llm-01
10 responses
Top: langfuse
Pain point: flying blind — no quality scoring, can't find bad conversations, costs climbing
Stack:nodejsopenai sdk
Asked about:langfuseheliconebraintrustlangsmithportkey
Existing StackCompliance/SecurityWorkload DefinedFramework-SpecificStarts from PainConstraint-LedWorkload-Led
✓ no langchain✓ pii redaction✓ quality evaluation✓ conversation threading✓ cost tracking
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
codex_cliImplementedlangfuse

this addresses your pain points

codex_cliImplementedbraintrust

RAG Pipeline Debugging and Evaluation

Can't debug bad RAG answers — unclear if it's retrieval, synthesis, or latency causing poor quality

llm-02
10 responses
Top: braintrust
Pain point: can't debug bad RAG answers — retrieval vs synthesis vs latency unknowns
Stack:pythonlangchainpineconegpt4
Asked about:langfuselangsmithbraintrustarizeragas
Existing StackWorkload DefinedFramework-SpecificCompatibilityStarts from PainConstraint-LedWorkload-LedExisting Vendor
✗ langchain native✓ retrieval quality metrics✓ prompt versioning✓ ci eval suite
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
codex_cliImplementedbraintrust
codex_cliImplementedNo primary vendor identified

Enterprise LLM Observability (Multi-Model)

Scaling from 100 to 5000 users with no observability — need multi-model tracking and quality eval

llm-03
10 responses
Top: braintrust
Pain point: scaling from beta to production with no observability — need enterprise-grade
Stack:nodejsanthropic sdkopenai sdk
Asked about:langfuseheliconebraintrustportkeyhumanloop
Existing StackCompliance/SecurityWorkload DefinedFramework-SpecificStarts from PainConstraint-LedWorkload-Led
✗ multi model✓ soc2✓ pii redaction✓ user feedback loop✗ non engineer dashboard✓ no langchain
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
claude_codeRecommendedNo primary vendor identified
codex_cliImplementedbraintrust
codex_cliImplementedbraintrust

Constraint Coverage

quality evaluation4/1040%
cost tracking4/1040%
soc24/1040%
pii redaction7/2035%
prompt versioning2/1020%
ci eval suite2/1020%
no langchain2/2010%
conversation threading1/1010%
retrieval quality metrics1/1010%
user feedback loop1/1010%
langchain native0/100%
multi model0/100%
non engineer dashboard0/100%