Arize Phoenix vs Weights & Biases

Detailed side-by-side comparison to help you choose the right tool

Arize Phoenix

🔴Developer

AI Observability

Arize Phoenix is an open-source AI observability and evaluation platform for tracing, testing, and improving LLM applications and agents.

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Starting Price

Free

Weights & Biases

🔴Developer

MLOps

End-to-end MLOps and AI developer platform — Models (experiment tracking, sweeps, model registry) plus Weave (LLM/agent observability and evals) — used by frontier labs and enterprise ML teams.

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Starting Price

Free

Feature Comparison

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FeatureArize PhoenixWeights & Biases
CategoryAI ObservabilityMLOps
Pricing Plans4 listed plans3 listed plans
Starting PriceFreeFree
Key Features
  • • LLM Tracing & Observability
  • • Evaluation Framework
  • • Experiment Management
  • • Workflow Runtime
  • • Tool and API Connectivity
  • • State and Context Handling

Arize Phoenix - Pros & Cons

Pros

  • ✓Open-source Phoenix can run locally or on controlled infrastructure without a software license fee.
  • ✓OpenTelemetry tracing reduces instrumentation lock-in.
  • ✓Datasets, experiments, evaluations, and Prompt IDE connect evidence to changes.
  • ✓AX Free and Pro include unlimited users and evaluations at published prices.

Cons

  • ✗Self-hosting shifts upgrades, storage, access control, backups, and availability to the user's team.
  • ✗Agent runs can generate many spans, consuming 25,000 or 50,000 allowances quickly.
  • ✗Managed retention of 15 or 30 days may be short for audits or long incident cycles.
  • ✗Fetched pages did not explicitly document native MCP support.

Weights & Biases - Pros & Cons

Pros

  • ✓Best-in-class experiment-tracking UI — researchers genuinely prefer it
  • ✓Weave bridges classical ML and LLM observability in one platform
  • ✓Mature integrations with virtually every major training framework
  • ✓Reports make collaboration and asynchronous review of experiments easy
  • ✓CoreWeave acquisition gives a clear long-term home and GPU compute story

Cons

  • ✗Paid tiers can get expensive at team scale relative to self-hosted MLflow
  • ✗SaaS-first posture; on-prem requires Enterprise tier
  • ✗Weave is newer and still catching up to LangSmith on some LangChain-specific niceties
  • ✗Storage of large artifacts (datasets, checkpoints) can become a hidden cost driver
  • ✗Some teams find the breadth (Models + Weave + Launch + Inference) overwhelming to adopt all at once

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🔒 Security & Compliance Comparison

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Security FeatureArize PhoenixWeights & Biases
SOC2✅ Yes✅ Yes
GDPR✅ Yes✅ Yes
HIPAA❌ No—
SSO❌ No✅ Yes
Self-Hosted✅ Yes🔀 Hybrid
On-Prem✅ Yes✅ Yes
RBAC❌ No✅ Yes
Audit Log❌ No✅ Yes
Open Source✅ Yes❌ No
API Key Auth✅ Yes✅ Yes
Encryption at Rest✅ Yes✅ Yes
Encryption in Transit✅ Yes✅ Yes
Data ResidencyAvailableUS, EU
Data Retentionconfigurableconfigurable
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