I'm building something to compare model quality in production and track spend; what tools fit?

If you want to compare model quality in production and track spend, the best fit usually depends on whether you want an open-source / self-hosted stack or a hosted observability platform.

Good tool categories

1) LLM observability + evals platforms

These are the most direct fit for what you described.

• Langfuse

  • Great for: tracing, prompt/version tracking, evals, cost tracking, production monitoring
  • Why it fits: lets you log requests/responses, compare runs, attach scores, and see token/cost usage
  • Good if: you want open-source and control over your data

• Arize Phoenix

  • Great for: tracing, experiments, evaluation, debugging quality issues
  • Why it fits: strong for evaluation workflows and root-cause analysis
  • Good if: you care a lot about model behavior analysis

• Weights & Biases Weave

  • Great for: experiment tracking, evals, LLM app tracing
  • Why it fits: useful if you already use W&B for ML experimentation

• LangSmith

  • Great for: tracing, prompt/chain evaluation, dataset-based comparisons
  • Why it fits: especially good if your app is built with LangChain, but works more broadly too

• Helicone

  • Great for: cost tracking, proxying requests, request-level analytics
  • Why it fits: very practical if spend tracking is a top priority; can sit in front of model APIs

If you need spend tracking specifically

Look at tools that act as a proxy / gateway or have strong usage analytics:

• Helicone
• OpenMeter
• LiteLLM Proxy
  • Great if you want to route between OpenAI, Anthropic, Azure, etc.
  • Also gives you centralized logging, retries, budget controls, and usage tracking

If you want model comparison / evaluation workflows

You’ll want support for:

• A/B testing or multi-model comparisons
• Human feedback collection
• Offline evals on sampled production traffic
• Automated metrics like faithfulness, relevance, groundedness, toxicity, etc.
• Custom scoring functions

Strong options:

• Langfuse
• LangSmith
• Phoenix
• W&B Weave

Practical stack recommendations

Option A: Open-source and flexible

• Langfuse for tracing + evals + cost tracking
• LiteLLM Proxy if you need unified API access and budget controls
• Your own warehouse/BI tool for deeper reporting if needed

Option B: Best for cost-first production monitoring

• Helicone for request analytics and spend
• Add Langfuse or Phoenix for richer evals/debugging

Option C: More enterprise / experiment-driven

• W&B Weave or LangSmith for comparison workflows
• Pair with a gateway/proxy for spend control if needed

What I’d choose by default

• If you want one tool that does both well: Langfuse
• If you want best spend visibility: Helicone
• If you want best debugging/eval analysis: Phoenix
• If you want LLM gateway + budget enforcement: LiteLLM Proxy

Quick decision guide

• Need self-hosting? → Langfuse, Phoenix, LiteLLM
• Need easiest cost tracking? → Helicone or LiteLLM
• Need strong eval/comparison UX? → LangSmith or Langfuse
• Need model routing across providers? → LiteLLM Proxy
• Need deep ML experimentation stack? → W&B

If you want, I can also give you a:

1. shortlist by budget/scale,
2. architecture diagram for a production setup, or
3. feature-by-feature comparison table.