Debug LLM Calls With Full Request Context

Track tokens, costs, prompts, and tool calls - inside the traces you already send.
OpenTelemetry native. 10x more affordable than standalone tools.

LLM trace transcript view showing multi-turn conversation with token stats and cost estimation

Why Monitor LLMs in Your Observability Platform

Your LLM call is just a span in a trace. It talks to your API, your database, your cache. Monitor it in the same place.

LLM + Traditional Observability, Unified

When a model call is slow, see if it's the prompt, the network, or a downstream database query - without switching tools.

OpenTelemetry Native

Built on open-source conventions for LLMs - use the OpenTelemetry instrumentation libraries you already have.

Affordable at Scale

No sampling required - retain 100% of your traces with full prompt and response content at production scale.

Everything You Need for Production LLM Debugging

Deep LLM insights extracted from your OpenTelemetry traces.

Token Usage Tracking

Input, output, and reasoning tokens broken down per span. Spot runaway prompts and budget overruns before they hit your bill.

Cost Estimation with Configurable Pricing

Estimated cost per trace, per model, per service. 50+ model pricing definitions shipped by default - OpenAI, Anthropic, Google, Mistral, Cohere, and more. Add custom or fine-tuned models in the UI.

Full Prompt/Response Transcript Replay

Read the exact multi-turn conversation: system, user, assistant, and tool messages. Debug hallucinations and prompt issues in context.

Tool Call Analysis

See every tool invocation with its schema, arguments, and response. Identify which tools fail, timeout, or return unexpected results.

AI-Powered Trace Quality Scoring

Automated quality scores on LLM traces flag low-confidence, potentially harmful, or off-topic responses without manual review.

Trace Waterfall with LLM Span Highlighting

LLM spans stand out in your trace waterfall with provider icons and GenAI attributes. See how model calls fit into the full request lifecycle.

How It Works

Three steps from zero to full LLM visibility.

Instrument with OTel GenAI Conventions

Use any OpenTelemetry-compatible instrumentation library that emits Generative AI semantic conventions - LangChain, Vercel AI SDK, OpenLLMetry, or roll your own.

Send Traces to Oodle

Point your OTLP exporter at your Oodle instance. Same endpoint you already use for backend traces - one config line, no new collector.

See LLM Data Alongside Everything Else

Oodle automatically extracts tokens, cost, transcripts, and quality scores from your GenAI spans. No configuration - it just works.

10x

Lower LLM Trace Costs

“This is cheaper in terms of storage and ingestion everything.”

- Vinay Krishna, Senior DevOps Engineer, Fello

Compare LLM Pricing →

See It In Your Environment

Book a call to see how Oodle handles your LLM traces - with your data, your models, your scale.

Schedule Demo Try in Playground

Frequently Asked Questions

Do I need a separate SDK to instrument my LLM calls?

No. Oodle consumes standard OpenTelemetry traces using the Generative AI semantic conventions. If your instrumentation library already emits these attributes, you just point your OTLP exporter at Oodle.

Which LLM providers and models are supported?

Any provider that your code calls - OpenAI, Anthropic, Google Gemini, Mistral, Cohere, Azure OpenAI, AWS Bedrock, and self-hosted models. Cost estimation ships with 50+ model pricing definitions, and you can add custom models.

How is this different from standalone LLM observability tools?

Standalone tools create another data silo. Oodle shows your LLM spans inside the same traces as your API, database, and cache calls. When something breaks, you see the full picture - not just the prompt and response in isolation.

Does Oodle do prompt versioning or eval datasets?

As of now, no. We built Oodle for production observability first - debugging latency, cost, errors, and quality in live traffic. For prompt versioning, A/B testing, or eval dataset management, use a dedicated ML experimentation tool alongside Oodle.

What does '10x more affordable' mean?

Customers consolidating LLM trace data from standalone tools into Oodle have seen ingestion costs drop by over 10x. Oodle's S3-based architecture stores trace data at a fraction of the cost of tools built on hot-storage databases.

Do you sample LLM traces?

No. We don't need to.