LLM observability tools detect , spikes, and cost overruns before they impact users. Without proper monitoring, AI errors can damage user trust and drain budgets undetected.

Traditional application monitoring doesn’t capture LLM-specific issues:

• Hallucinations: Factually incorrect outputs that pass technical checks
• : Malicious inputs that manipulate model behavior
• Cost Spikes: Unexpected token usage from verbose responses
• Latency Drift: Gradual slowdowns that impact user experience

Open-source LLM observability with one-line integration. Tracks costs, latency, and provides request-level debugging.

• Best for: Startups needing quick, affordable monitoring
• Pricing: Free tier available, paid from $20/month

Open-source LLM engineering platform with tracing, prompt management, and evaluation capabilities.

• Best for: Teams wanting full control with self-hosting option
• Pricing: Free self-hosted, cloud from $59/month

ML observability platform with strong LLM support, including embedding drift detection and trace visualization.

• Best for: Enterprise teams with existing ML infrastructure
• Pricing: Enterprise pricing

Experiment tracking platform with LLM-specific features for prompt versioning and response evaluation.

• Best for: Teams already using W&B for ML experiments
• Pricing: Free for individuals, team plans from $50/user/month

LangChain’s official observability platform with deep integration for chain debugging and testing.

• Best for: LangChain users needing native debugging
• Pricing: Free tier, Plus from $39/month

Enterprise APM platform with dedicated LLM monitoring features integrated into existing dashboards.

• Best for: Organizations already using Datadog
• Pricing: Part of Datadog subscription

AI gateway with built-in observability, caching, and fallback routing for production LLM apps.

• Best for: Teams needing gateway + observability combined
• Pricing: Free tier, Pro from $49/month

OpenTelemetry-based observability for LLMs, enabling integration with existing observability stacks.

• Best for: Teams with OpenTelemetry infrastructure
• Pricing: Open source

End-to-end LLM development platform with evaluation, logging, and prompt playground features.

• Best for: Teams needing eval + observability in one tool
• Pricing: Free tier, Pro from $50/month

1. Start Early: Add observability from day one, not after production issues
2. Track Costs: Set up token usage alerts before they become problems
3. Evaluate Quality: Implement automated quality checks for hallucination detection
4. Create Baselines: Establish latency and quality benchmarks for comparison

LLM observability is not optional for production AI systems. The cost of undetected errors—both financial and reputational—far exceeds the investment in proper monitoring tools.