Troubleshoot software performance issues using Claude

Performance issues are rarely polite. They manifest as unexplained slowdowns, sudden error spikes, or vague bug reports, and they always arrive at the worst possible moment.

Claude brings structure, clarity, and speed to troubleshooting through two powerful tools:

• Claude.ai: Anthropic’s AI coding tool, available in your browser, that helps reason through system symptoms, diagnose root causes, and plan remediation steps using natural language.
  
• Claude Code: Anthropic’s agentic coding tool that works directly in your development environment and inspects, edits, and optimizes code to reduce mean-time-to-resolution (MTTR) for issues like memory leaks and latency regressions.

Below you'll find a full, end-to-end workflow, plus a real-world look at how Ramp uses Claude to ship fixes faster.

Diagnose performance issues with Claude.ai 

The first step in any performance investigation is narrowing the scope. Claude.ai excels at turning high-level symptoms into targeted hypotheses using structured reasoning.

Start with a prompt like:

We're seeing a CPU spike on the checkout service after yesterday's deploy. Think through the top three likely causes and how we'd confirm each one. Return an outline of investigative steps.

Claude.ai converts that natural-language symptom into targeted hypotheses, for example:

1. Recently merged code touching /checkout → diff & feature-flag rollback
2. GC pressure → compare heap profiles & container limits
3. N + 1 database queries → explain plan & missing index check
4. Traffic skew → segment traces by peak vs. off-peak

You can iterate further: "Focus only on database queries," or "Zoom in on GC activity during that timeframe." Claude.ai's reasoning stays directional and specific, leveraging Anthropic's large context window (up to 200K tokens or 1M in preview) so that even long dashboards, traces, or playbook excerpts stay in scope.

Interpret debugging artifacts with Claude.ai 

Once you've narrowed in, Claude.ai helps interpret messy debugging artifacts:

Stack-trace analysis

Paste an exception report and prompt:

TimeoutException in OrderService. Parse the trace below, pinpoint root cause, and propose a fix.

Claude.ai will:

• Parse and cluster similar frames
• Flag blocking calls in async handlers
• Suggest safe code changes (often with diff blocks)
• Recommend log or metric instrumentation to verify

Profile & FlameGraph review

Upload profiler output—e.g., py-spy --flame SVG, perf script text, or folded-stack call trees, and ask:

Analyze this flamegraph and highlight the three most expensive functions.

Claude.ai identifies hotspots, explains why they're expensive, and proposes optimizations (memoization, batching, vectorizing, etc.).

By leveraging Claude.ai for structured reasoning and artifact analysis, developers can quickly zero in on the root cause of performance issues without getting lost in the noise. This targeted approach reduces MTTR and helps teams focus their efforts where it matters most.

Run targeted static analysis with Claude Code

For more advanced debugging workflows, Claude Code can work directly with your repository, making it easy to identify bottlenecks inside complex codebases.

Inside the CLI-based Claude Code (requires Node.js ≥ 18), run:

find inefficient loops or redundant calculations in src/utils/

Claude Code will:

• Traverse the codebase (read-only by default)
• Flag nested iterations over large datasets, duplicate computations, or sub-optimal data structures
• Recommend performance-friendly rewrites—e.g., replacing list look-ups with sets or leveraging NumPy/pandas vectorization

Since Claude Code operates inside your terminal, all file reads and edits require explicit approval or pre-configured allow-lists to ensure security and predictability.

Apply safe, auditable edits with Claude Code 

For surgical fixes, invoke the text-editor tool in a Claude Code session:

{
  "type": "tool_use",
  "name": "str_replace_editor",
  "input": {
    "command": "str_replace",
    "path": "app/helpers/list_utils.py",
    "old_str": "for i in range(len(items)): result.append(items[i])",
    "new_str": "for item in items: result.append(item)"
  }
}

Edits execute only after Claude Code presents the diff, and they must match the target string exactly. Every change remains traceable, reversible (using undo_edit), and bound by your project's permission rules.

Verify improvements & prevent regressions 

Once edits are made, Claude Code helps confirm that performance has improved. Ask it to sanity-check complexity:

Compare the time & space complexity of the old vs. new normalize_items implementation.

Claude Code returns a concise before/after Big-O table, suggests micro-benchmarks (pytest-benchmark, timeit, hyperfine), and can auto-generate regression tests to guard the fix.

How Ramp uses Claude to optimize critical paths 

Ramp, a leading spend management platform, uses Claude to troubleshoot and optimize performance across their web application. By prompting Claude.ai with "We're seeing increased latency on the /receipts endpoint, walk through likely causes," Ramp's engineers receive structured debugging plans that help them identify bottlenecks faster.

When it's time to implement fixes, Ramp turns to Claude Code. Prompts like claude "find N+1 queries in the Receipt model and propose eager-loading fixes" generate targeted optimizations that match Ramp's stack and coding conventions. As a result, Ramp has been able to:

• Reduce mean time to resolution (MTTR) for performance issues by 35%
• Identify and fix 20+ performance bottlenecks before they impact production
• Ship optimizations 2x faster compared to manual debugging and profiling

Choosing the right Claude tool for performance debugging

Each of these two tools fits into different stages of performance debugging.

Claude.ai uses structured reasoning to narrow the debugging scope and interprets stack traces, logs, and profiles. It works on web, desktop, and mobile and is free to sign up.

Claude Code runs targeted static analysis on the codebase and applies safe, auditable edits while suggesting regression tests to verify improvements. You can run it from the terminal if you have API access or a Max plan subscription.

In general, use Claude.ai for initial problem scoping, hypothesis generation, and artifact analysis. It's the fastest way to go from vague symptoms to targeted debugging plans.

Switch to Claude Code when you need to analyze code, implement fixes, and verify optimizations. Its deep integration with your local development environment makes it a powerful tool for hands-on performance work.

Get started 

Ready to streamline your performance debugging process? Here's how to begin:

1. Visit Claude.ai and sign up for free. Use it to triage issues, generate debugging hypotheses, and analyze artifacts.
   
2. When you're ready to dive into code, install Claude Code and run it in your terminal:

npm install -g @anthropic-ai/claude-code
cd your-project
claude

With Claude, you can finally make performance debugging an integral part of your development process. The result? Faster issue resolution, fewer production incidents, and more proactive optimization.