⬡ API & SDK/2026-03-27Advanced

Building AI Application Observability with Claude API and OpenTelemetry

Learn how to integrate OpenTelemetry with your Claude API applications for unified tracing, metrics, and logging. Covers token usage visualization, latency monitoring, cost alerting, and distributed tracing for agent workflows.

opentelemetry³ observability⁹ monitoring⁶ production¹¹⁰ tracing² metrics

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Why AI Applications Need Dedicated Observability

As production applications powered by Claude API continue to grow, engineering teams face operational challenges that differ significantly from traditional web services. "Why did this request take 3 seconds?" "What caused our API costs to double this month?" "Where exactly did the agent's tool call chain fail?" Without the ability to answer these questions instantly, running AI applications reliably in production becomes a constant struggle.

OpenTelemetry is the CNCF-backed standard framework for observability. It provides a unified approach to collecting and exporting three core signals — traces, metrics, and logs — and supports all major monitoring backends including Grafana, Datadog, and New Relic.

Prerequisites and Required Packages

The code examples in this article use Node.js with TypeScript. The design patterns apply equally to Python SDK implementations.

# OpenTelemetry core packages
npm install @opentelemetry/api \
  @opentelemetry/sdk-node \
  @opentelemetry/sdk-trace-node \
  @opentelemetry/sdk-metrics \
  @opentelemetry/exporter-trace-otlp-http \
  @opentelemetry/exporter-metrics-otlp-http \
  @opentelemetry/resources \
  @opentelemetry/semantic-conventions
 
# Claude API SDK
npm install @anthropic-ai/sdk

We recommend routing telemetry through the OpenTelemetry Collector to your backend (Grafana Tempo + Prometheus, Datadog, etc.). For local development, direct export without a Collector is also supported.

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WHAT YOU'LL LEARN

✦Master design patterns for unified tracing, metrics, and logging of Claude API calls using OpenTelemetry

✦Build real-time dashboards to visualize token usage, latency, and error rates across your AI application

✦Implement cost anomaly detection alerts and distributed tracing for agent workflows in production

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Architecture Design — Three Signals for AI Workloads

Observability for AI applications requires tracking metrics that go beyond what traditional web applications need.

┌─────────────────────────────────────────────────┐
│  AI Application                                  │
│  ┌───────────┐  ┌───────────┐  ┌───────────┐    │
│  │ Traces    │  │ Metrics   │  │ Logs      │    │
│  │ ・API call│  │ ・Tokens  │  │ ・Errors  │    │
│  │ ・Tools   │  │ ・Latency │  │ ・Warnings│    │
│  │ ・Thinking│  │ ・Cost    │  │ ・Audit   │    │
│  └─────┬─────┘  └─────┬─────┘  └─────┬─────┘    │
│        └──────────┬───┘───────────────┘          │
│              OTLP Protocol                       │
└──────────────┬───────────────────────────────────┘
               ▼
┌──────────────────────────────┐
│  OpenTelemetry Collector     │
│  ・Filtering                 │
│  ・Sampling                  │
│  ・Enrichment                │
└──────┬───────────┬───────────┘
       ▼           ▼
┌────────────┐ ┌────────────┐
│ Grafana    │ │ Datadog /  │
│ Tempo +    │ │ New Relic  │
│ Prometheus │ │            │
└────────────┘ └────────────┘

Traces visualize the execution path of individual API requests and agent workflows. Metrics provide aggregated data on token consumption and latency, forming the foundation for cost management. Logs capture detailed error information and prompt audit trails.

OpenTelemetry SDK Initialization

Start by setting up the OpenTelemetry SDK that the entire application will use.

// src/telemetry/setup.ts
import { NodeSDK } from "@opentelemetry/sdk-node";
import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-http";
import { OTLPMetricExporter } from "@opentelemetry/exporter-metrics-otlp-http";
import { PeriodicExportingMetricReader } from "@opentelemetry/sdk-metrics";
import { Resource } from "@opentelemetry/resources";
import {
  ATTR_SERVICE_NAME,
  ATTR_SERVICE_VERSION,
} from "@opentelemetry/semantic-conventions";
 
// Define application resource information
const resource = new Resource({
  [ATTR_SERVICE_NAME]: "claude-ai-app",
  [ATTR_SERVICE_VERSION]: "1.0.0",
  "ai.provider": "anthropic",
  "deployment.environment": process.env.NODE_ENV || "development",
});
 
// Trace exporter (OTLP over HTTP)
const traceExporter = new OTLPTraceExporter({
  url:
    process.env.OTEL_EXPORTER_OTLP_ENDPOINT + "/v1/traces" ||
    "http://localhost:4318/v1/traces",
});
 
// Metrics exporter
const metricExporter = new OTLPMetricExporter({
  url:
    process.env.OTEL_EXPORTER_OTLP_ENDPOINT + "/v1/metrics" ||
    "http://localhost:4318/v1/metrics",
});
 
// Initialize the SDK
const sdk = new NodeSDK({
  resource,
  traceExporter,
  metricReader: new PeriodicExportingMetricReader({
    exporter: metricExporter,
    exportIntervalMillis: 30000, // Export metrics every 30 seconds
  }),
});
 
export function initTelemetry(): void {
  sdk.start();
  console.log("OpenTelemetry initialized for Claude AI application");
 
  // Graceful shutdown handler
  process.on("SIGTERM", () => {
    sdk
      .shutdown()
      .then(() => console.log("Telemetry shut down"))
      .catch((err) => console.error("Telemetry shutdown error", err))
      .finally(() => process.exit(0));
  });
}

Call this setup at the very beginning of your application entry point.

// src/index.ts
import { initTelemetry } from "./telemetry/setup";
initTelemetry(); // Must run before other imports
 
import { startServer } from "./server";
startServer();

Instrumenting the Claude API Client

Build a wrapper around the Claude SDK that automatically traces every API request using the OpenTelemetry API.

// src/telemetry/claude-instrumentation.ts
import { trace, metrics, SpanStatusCode, context } from "@opentelemetry/api";
import Anthropic from "@anthropic-ai/sdk";
 
// Get tracer and meter instances
const tracer = trace.getTracer("claude-api", "1.0.0");
const meter = metrics.getMeter("claude-api", "1.0.0");
 
// Define custom metrics
const tokenCounter = meter.createCounter("claude.tokens.total", {
  description: "Total tokens consumed by Claude API calls",
  unit: "tokens",
});
 
const inputTokenCounter = meter.createCounter("claude.tokens.input", {
  description: "Input tokens sent to Claude API",
  unit: "tokens",
});
 
const outputTokenCounter = meter.createCounter("claude.tokens.output", {
  description: "Output tokens received from Claude API",
  unit: "tokens",
});
 
const requestDuration = meter.createHistogram("claude.request.duration", {
  description: "Duration of Claude API requests",
  unit: "ms",
});
 
const requestCounter = meter.createCounter("claude.requests.total", {
  description: "Total number of Claude API requests",
});
 
const errorCounter = meter.createCounter("claude.errors.total", {
  description: "Total number of Claude API errors",
});
 
// Cost rates (as of March 2026)
const COST_PER_1K_INPUT: Record<string, number> = {
  "claude-opus-4-6": 0.015,
  "claude-sonnet-4-6": 0.003,
  "claude-haiku-4-5-20251001": 0.0008,
};
 
const COST_PER_1K_OUTPUT: Record<string, number> = {
  "claude-opus-4-6": 0.075,
  "claude-sonnet-4-6": 0.015,
  "claude-haiku-4-5-20251001": 0.004,
};
 
const costCounter = meter.createCounter("claude.cost.usd", {
  description: "Estimated cost of Claude API calls in USD",
  unit: "usd",
});
 
// Instrumented Claude API client
export class InstrumentedClaudeClient {
  private client: Anthropic;
 
  constructor(apiKey?: string) {
    this.client = new Anthropic({ apiKey });
  }
 
  async createMessage(
    params: Anthropic.MessageCreateParamsNonStreaming
  ): Promise<Anthropic.Message> {
    // Create a span to trace the API call
    return tracer.startActiveSpan(
      "claude.messages.create",
      {
        attributes: {
          "ai.model": params.model,
          "ai.max_tokens": params.max_tokens,
          "ai.message_count": params.messages.length,
          "ai.has_tools": params.tools ? "true" : "false",
          "ai.has_system": params.system ? "true" : "false",
        },
      },
      async (span) => {
        const startTime = Date.now();
 
        try {
          // Make the Claude API call
          const response = await this.client.messages.create(params);
          const duration = Date.now() - startTime;
 
          // Record response metadata on the span
          span.setAttributes({
            "ai.response.id": response.id,
            "ai.response.model": response.model,
            "ai.response.stop_reason": response.stop_reason || "unknown",
            "ai.tokens.input": response.usage.input_tokens,
            "ai.tokens.output": response.usage.output_tokens,
            "ai.tokens.total":
              response.usage.input_tokens + response.usage.output_tokens,
            "ai.duration_ms": duration,
          });
 
          // Record metrics
          const labels = { model: response.model };
          inputTokenCounter.add(response.usage.input_tokens, labels);
          outputTokenCounter.add(response.usage.output_tokens, labels);
          tokenCounter.add(
            response.usage.input_tokens + response.usage.output_tokens,
            labels
          );
          requestDuration.record(duration, labels);
          requestCounter.add(1, { ...labels, status: "success" });
 
          // Estimate cost
          const model = response.model;
          const inputCost =
            (response.usage.input_tokens / 1000) *
            (COST_PER_1K_INPUT[model] || 0.003);
          const outputCost =
            (response.usage.output_tokens / 1000) *
            (COST_PER_1K_OUTPUT[model] || 0.015);
          costCounter.add(inputCost + outputCost, labels);
 
          span.setStatus({ code: SpanStatusCode.OK });
          return response;
        } catch (error) {
          const duration = Date.now() - startTime;
 
          // Record error on span and metrics
          span.setStatus({
            code: SpanStatusCode.ERROR,
            message: error instanceof Error ? error.message : "Unknown error",
          });
          span.recordException(error as Error);
 
          errorCounter.add(1, {
            model: params.model,
            error_type:
              error instanceof Anthropic.APIError
                ? `${error.status}`
                : "unknown",
          });
          requestDuration.record(duration, { model: params.model });
          requestCounter.add(1, {
            model: params.model,
            status: "error",
          });
 
          throw error;
        } finally {
          span.end();
        }
      }
    );
  }
}

Simply using this client ensures that every Claude API call is automatically traced, with token consumption and latency recorded as metrics.

Distributed Tracing for Agent Workflows

When building agent workflows with tool use, tracking the execution path of each step becomes critical for debugging and optimization.

// src/telemetry/agent-tracing.ts
import { trace, SpanStatusCode, SpanKind } from "@opentelemetry/api";
import { InstrumentedClaudeClient } from "./claude-instrumentation";
import Anthropic from "@anthropic-ai/sdk";
 
const tracer = trace.getTracer("claude-agent", "1.0.0");
 
interface ToolResult {
  tool_use_id: string;
  content: string;
}
 
// Instrumented tool execution
async function executeToolWithTracing(
  toolName: string,
  toolInput: Record<string, unknown>,
  toolHandler: (input: Record<string, unknown>) => Promise<string>
): Promise<string> {
  return tracer.startActiveSpan(
    `tool.${toolName}`,
    {
      kind: SpanKind.INTERNAL,
      attributes: {
        "tool.name": toolName,
        "tool.input_keys": Object.keys(toolInput).join(","),
      },
    },
    async (span) => {
      try {
        const result = await toolHandler(toolInput);
        span.setAttributes({
          "tool.result_length": result.length,
          "tool.success": true,
        });
        span.setStatus({ code: SpanStatusCode.OK });
        return result;
      } catch (error) {
        span.setStatus({
          code: SpanStatusCode.ERROR,
          message: error instanceof Error ? error.message : "Tool execution failed",
        });
        span.recordException(error as Error);
        throw error;
      } finally {
        span.end();
      }
    }
  );
}
 
// Instrumented agent loop
export async function runAgentWithTracing(
  client: InstrumentedClaudeClient,
  systemPrompt: string,
  userMessage: string,
  tools: Anthropic.Tool[],
  toolHandlers: Record<
    string,
    (input: Record<string, unknown>) => Promise<string>
  >,
  maxIterations: number = 10
): Promise<string> {
  return tracer.startActiveSpan(
    "agent.workflow",
    {
      attributes: {
        "agent.max_iterations": maxIterations,
        "agent.tools_available": tools.map((t) => t.name).join(","),
        "agent.system_prompt_length": systemPrompt.length,
      },
    },
    async (rootSpan) => {
      const messages: Anthropic.MessageParam[] = [
        { role: "user", content: userMessage },
      ];
      let iteration = 0;
      let totalInputTokens = 0;
      let totalOutputTokens = 0;
 
      try {
        while (iteration < maxIterations) {
          iteration++;
 
          // Record each iteration as a child span
          const response = await tracer.startActiveSpan(
            `agent.iteration.${iteration}`,
            { attributes: { "agent.iteration": iteration } },
            async (iterSpan) => {
              const resp = await client.createMessage({
                model: "claude-sonnet-4-6",
                max_tokens: 4096,
                system: systemPrompt,
                tools,
                messages,
              });
 
              totalInputTokens += resp.usage.input_tokens;
              totalOutputTokens += resp.usage.output_tokens;
 
              iterSpan.setAttributes({
                "agent.stop_reason": resp.stop_reason || "unknown",
                "agent.content_blocks": resp.content.length,
              });
              iterSpan.end();
              return resp;
            }
          );
 
          // Exit condition: no tool calls means we're done
          if (response.stop_reason === "end_turn") {
            const textBlock = response.content.find(
              (b) => b.type === "text"
            );
            const finalAnswer =
              textBlock && "text" in textBlock ? textBlock.text : "";
 
            rootSpan.setAttributes({
              "agent.total_iterations": iteration,
              "agent.total_input_tokens": totalInputTokens,
              "agent.total_output_tokens": totalOutputTokens,
              "agent.final_answer_length": finalAnswer.length,
            });
            rootSpan.setStatus({ code: SpanStatusCode.OK });
            return finalAnswer;
          }
 
          // Process tool calls
          const toolUseBlocks = response.content.filter(
            (b) => b.type === "tool_use"
          );
 
          const toolResults: ToolResult[] = [];
          for (const block of toolUseBlocks) {
            if (block.type === "tool_use") {
              const handler = toolHandlers[block.name];
              if (handler) {
                const result = await executeToolWithTracing(
                  block.name,
                  block.input as Record<string, unknown>,
                  handler
                );
                toolResults.push({
                  tool_use_id: block.id,
                  content: result,
                });
              }
            }
          }
 
          // Add assistant response and tool results to conversation
          messages.push({ role: "assistant", content: response.content });
          messages.push({
            role: "user",
            content: toolResults.map((r) => ({
              type: "tool_result" as const,
              tool_use_id: r.tool_use_id,
              content: r.content,
            })),
          });
        }
 
        rootSpan.setAttributes({
          "agent.total_iterations": iteration,
          "agent.max_iterations_reached": true,
        });
        rootSpan.setStatus({ code: SpanStatusCode.OK });
        return "Maximum iterations reached";
      } catch (error) {
        rootSpan.setStatus({
          code: SpanStatusCode.ERROR,
          message: error instanceof Error ? error.message : "Agent workflow failed",
        });
        rootSpan.recordException(error as Error);
        throw error;
      } finally {
        rootSpan.end();
      }
    }
  );
}

With this instrumentation, you can visualize traces in Grafana Tempo or Jaeger like this:

agent.workflow (2.4s)
  ├── agent.iteration.1 (800ms)
  │   └── claude.messages.create (780ms)
  ├── agent.iteration.2 (1.2s)
  │   ├── claude.messages.create (600ms)
  │   ├── tool.search_database (450ms)
  │   └── tool.format_report (120ms)
  └── agent.iteration.3 (400ms)
      └── claude.messages.create (390ms)

Each span carries token usage and error information, making it straightforward to identify bottlenecks.

Building a Cost Monitoring Dashboard

Here's how to design metrics for real-time cost visibility.

// src/telemetry/cost-dashboard-metrics.ts
import { metrics } from "@opentelemetry/api";
 
const meter = metrics.getMeter("claude-cost-dashboard", "1.0.0");
 
// Daily cost tracking via ObservableGauge
const dailyCostGauge = meter.createObservableGauge(
  "claude.cost.daily_estimate_usd",
  {
    description: "Estimated daily cost based on current usage rate",
    unit: "usd",
  }
);
 
// Budget utilization percentage
const budgetUtilization = meter.createObservableGauge(
  "claude.budget.utilization_percent",
  {
    description: "Percentage of monthly budget consumed",
    unit: "percent",
  }
);
 
// Cost tracking state
interface CostTracker {
  dailyTotal: number;
  monthlyTotal: number;
  monthlyBudget: number;
  lastResetDate: string;
}
 
const costTracker: CostTracker = {
  dailyTotal: 0,
  monthlyTotal: 0,
  monthlyBudget: parseFloat(process.env.MONTHLY_BUDGET_USD || "500"),
  lastResetDate: new Date().toISOString().split("T")[0],
};
 
// Register observable callbacks
dailyCostGauge.addCallback((result) => {
  result.observe(costTracker.dailyTotal);
});
 
budgetUtilization.addCallback((result) => {
  const utilization =
    (costTracker.monthlyTotal / costTracker.monthlyBudget) * 100;
  result.observe(utilization);
});
 
// Record cost (called from InstrumentedClaudeClient)
export function recordCost(
  model: string,
  inputTokens: number,
  outputTokens: number
): void {
  const today = new Date().toISOString().split("T")[0];
  if (today !== costTracker.lastResetDate) {
    costTracker.dailyTotal = 0;
    costTracker.lastResetDate = today;
  }
 
  const costRates: Record<string, { input: number; output: number }> = {
    "claude-opus-4-6": { input: 0.015, output: 0.075 },
    "claude-sonnet-4-6": { input: 0.003, output: 0.015 },
    "claude-haiku-4-5-20251001": { input: 0.0008, output: 0.004 },
  };
 
  const rates = costRates[model] || { input: 0.003, output: 0.015 };
  const cost =
    (inputTokens / 1000) * rates.input +
    (outputTokens / 1000) * rates.output;
 
  costTracker.dailyTotal += cost;
  costTracker.monthlyTotal += cost;
 
  // Budget alert threshold
  const utilization =
    (costTracker.monthlyTotal / costTracker.monthlyBudget) * 100;
  if (utilization > 80) {
    console.warn(
      `⚠️ Budget alert: ${utilization.toFixed(1)}% of monthly budget consumed`
    );
  }
}

For building dashboards in Grafana, use these PromQL queries:

# Token consumption over the last hour (by model)
sum(rate(claude_tokens_total[1h])) by (model)
 
# Estimated hourly cost
sum(rate(claude_cost_usd[1h])) * 3600
 
# Error rate (5-minute window)
sum(rate(claude_errors_total[5m])) / sum(rate(claude_requests_total[5m])) * 100
 
# P95 latency
histogram_quantile(0.95, rate(claude_request_duration_bucket[5m]))
 
# Budget utilization trend
claude_budget_utilization_percent

OpenTelemetry Collector Configuration

In production, route telemetry through the OpenTelemetry Collector. Here's a configuration optimized for AI workloads:

# otel-collector-config.yaml
receivers:
  otlp:
    protocols:
      http:
        endpoint: 0.0.0.0:4318
      grpc:
        endpoint: 0.0.0.0:4317
 
processors:
  # Batch for network efficiency
  batch:
    timeout: 10s
    send_batch_size: 1024
 
  # Enrich AI-related spans with metadata
  attributes:
    actions:
      - key: "service.layer"
        value: "ai-inference"
        action: upsert
 
  # Trace sampling (cost reduction in production)
  probabilistic_sampler:
    sampling_percentage: 25  # Keep 25% of traces
 
  # Always retain high-value traces
  tail_sampling:
    decision_wait: 10s
    policies:
      # Keep 100% of errors
      - name: errors
        type: status_code
        status_code:
          status_codes: [ERROR]
      # Keep high-latency requests (>3s)
      - name: slow-requests
        type: latency
        latency:
          threshold_ms: 3000
      # Keep all Opus model calls (expensive)
      - name: expensive-models
        type: string_attribute
        string_attribute:
          key: ai.model
          values: ["claude-opus-4-6"]
 
exporters:
  otlphttp/tempo:
    endpoint: http://tempo:4318
  prometheus:
    endpoint: 0.0.0.0:8889
    namespace: claude
 
service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch, attributes, tail_sampling]
      exporters: [otlphttp/tempo]
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [prometheus]

The key here is the tail_sampling configuration. Retaining all traces would incur massive storage costs, but by always keeping errors, slow requests, and expensive model calls, you ensure the data you need for troubleshooting is always available while keeping costs manageable.

Monitoring Prompt Caching Hit Rates

If you're using Claude API's prompt caching, tracking cache hit rates helps you measure the effectiveness of your cost optimization strategy.

// src/telemetry/cache-metrics.ts
import { metrics } from "@opentelemetry/api";
import Anthropic from "@anthropic-ai/sdk";
 
const meter = metrics.getMeter("claude-cache", "1.0.0");
 
const cacheHitCounter = meter.createCounter("claude.cache.hits", {
  description: "Number of prompt cache hits",
});
 
const cacheMissCounter = meter.createCounter("claude.cache.misses", {
  description: "Number of prompt cache misses",
});
 
const cacheSavingsCounter = meter.createCounter(
  "claude.cache.savings_tokens",
  {
    description: "Tokens saved by prompt caching",
    unit: "tokens",
  }
);
 
// Monitor cache effectiveness
export function recordCacheMetrics(
  response: Anthropic.Message,
  model: string
): void {
  const usage = response.usage as Record<string, number>;
 
  // Get cache_creation_input_tokens and cache_read_input_tokens
  const cacheCreation = usage.cache_creation_input_tokens || 0;
  const cacheRead = usage.cache_read_input_tokens || 0;
 
  if (cacheRead > 0) {
    cacheHitCounter.add(1, { model });
    // Cache reads are charged at 10% of normal rate, saving 90%
    cacheSavingsCounter.add(Math.floor(cacheRead * 0.9), { model });
  } else if (cacheCreation > 0) {
    cacheMissCounter.add(1, { model });
  }
}

# Prompt caching hit rate
sum(rate(claude_cache_hits[1h])) /
(sum(rate(claude_cache_hits[1h])) + sum(rate(claude_cache_misses[1h]))) * 100
 
# Estimated tokens saved by caching (per day)
sum(increase(claude_cache_savings_tokens[24h]))

Local Development Environment with Docker Compose

Get observability running locally with this Docker Compose setup:

# docker-compose.observability.yml
version: "3.9"
services:
  otel-collector:
    image: otel/opentelemetry-collector-contrib:0.96.0
    command: ["--config=/etc/otel-collector-config.yaml"]
    volumes:
      - ./otel-collector-config.yaml:/etc/otel-collector-config.yaml
    ports:
      - "4317:4317"   # gRPC
      - "4318:4318"   # HTTP
      - "8889:8889"   # Prometheus metrics
 
  tempo:
    image: grafana/tempo:2.4.0
    command: ["-config.file=/etc/tempo.yaml"]
    volumes:
      - ./tempo.yaml:/etc/tempo.yaml
    ports:
      - "3200:3200"
 
  prometheus:
    image: prom/prometheus:v2.50.0
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
    ports:
      - "9090:9090"
 
  grafana:
    image: grafana/grafana:10.3.0
    environment:
      - GF_AUTH_ANONYMOUS_ENABLED=true
      - GF_AUTH_ANONYMOUS_ORG_ROLE=Admin
    volumes:
      - ./grafana/provisioning:/etc/grafana/provisioning
    ports:
      - "3001:3000"
    depends_on:
      - tempo
      - prometheus

Run docker compose -f docker-compose.observability.yml up -d and access Grafana at http://localhost:3001.

A Note from an Indie Developer

Key Takeaways

Integrating OpenTelemetry into your Claude API applications gives you the tools to tackle the unique operational challenges of AI workloads: token consumption and cost visibility, latency optimization, error tracking, and distributed tracing of agent workflows. Tail sampling and prompt caching hit rate monitoring are particularly impactful in production, directly reducing costs while maintaining the observability you need.

For more on cost optimization strategies, see the Claude API Cost Optimization Production Guide. For streaming and tool use production patterns, check out the Claude API Streaming and Tool Use Production Guide. For security considerations, see the Claude API Production Security Complete Guide.

To go deeper into the concepts covered here,

Thank You for Reading

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