GitHub Copilot vs Cursor vs Continue.dev for DevOps Engineers (2026 Comparison)

AI coding assistants have become genuinely useful for DevOps work. Writing Terraform modules, debugging YAML, generating GitHub Actions workflows — these tools save real time when configured correctly.

But which one is actually worth using for infrastructure work? Here's an honest comparison based on real DevOps use cases.

The Three Tools

GitHub Copilot — The Safe Enterprise Choice

Best for: Teams that live in VS Code or JetBrains, need admin controls, and want a supported product.

What it's genuinely good at for DevOps:

Inline completions while writing Terraform:

# Type this comment → Copilot autocompletes the block
# Create an S3 bucket with versioning enabled and server-side encryption
 
resource "aws_s3_bucket" "main" {
  bucket = var.bucket_name
  # Copilot fills this in correctly most of the time
}

GitHub Actions workflow generation from a comment describing what you want.

Copilot Chat (the chat panel) is useful for:

• "Explain this Helm chart"
• "What's wrong with this Dockerfile?"
• "Write a Kubernetes NetworkPolicy that allows only port 8080 from namespace monitoring"

Where Copilot falls short:

• No workspace-aware codebase understanding — it doesn't read your whole Terraform repo to understand your module structure
• Inline completions for YAML can be hit-or-miss with indentation
• No built-in way to connect your local tools or terminal

Best model for DevOps: Enable Claude 3.5 Sonnet in Copilot settings — it's significantly better at Terraform and YAML than the default GPT-4o.

Cursor — Best Overall for DevOps Individuals

Best for: Individual DevOps engineers or small teams who want the most powerful AI-augmented editor available.

Cursor is a fork of VS Code with AI built deeply into the editor. The key differentiator: Cursor reads your entire codebase before answering.

@Codebase feature for Terraform:

@Codebase How are we managing IAM roles across environments? 
What naming convention are we using?

Cursor scans all your .tf files and answers based on your actual code — not generic Terraform patterns.

Composer (multi-file edits): Ask Cursor to "Create a new Terraform module for RDS with the same structure as our existing VPC module" — it will read the VPC module and create a consistent RDS module with matching variable names, output patterns, and documentation style.

Terminal integration: Ctrl+K in the terminal lets you describe what you want to do in English:

explain what's happening when kubectl get pods shows CrashLoopBackOff

It generates the diagnostic commands, you run them, paste output, it diagnoses.

Where Cursor falls short:

• Privacy: Your code is sent to Cursor's servers. Check your employer's data policy.
• Cost: $20/mo for the fast model tier. Some teams can't expense it.
• Not JetBrains: If you're a GoLand or IntelliJ user, Cursor isn't an option.

Continue.dev — The Self-Hosted Power Tool

Best for: Teams with data residency requirements, engineers who want to run local models, or anyone who wants a free open-source option.

Continue is a VS Code / JetBrains extension that connects to any LLM — including Ollama running locally on your machine.

Setting up Continue with Ollama for DevOps:

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh
 
# Pull a model good at code
ollama pull qwen2.5-coder:7b

// ~/.continue/config.json
{
  "models": [
    {
      "title": "Qwen2.5 Coder (Local)",
      "provider": "ollama",
      "model": "qwen2.5-coder:7b",
      "contextLength": 32768
    },
    {
      "title": "Claude 3.5 Sonnet",
      "provider": "anthropic",
      "model": "claude-3-5-sonnet-20241022",
      "apiKey": "YOUR_API_KEY"
    }
  ]
}

No data leaves your machine when using Ollama. For regulated environments, this matters.

What Continue does well:

• Custom slash commands for DevOps tasks:

  /explain — explain selected code
  /edit — edit with instruction
  /comment — add inline comments
  

  Copy code
• You can add your own prompt templates for your team's patterns
• Works with any model — use Claude for complex Terraform, local Ollama for quick completions

Where Continue falls short:

• Local models (7B-13B) are noticeably worse at complex Terraform and multi-file reasoning compared to GPT-4o or Claude
• No multi-file composer like Cursor
• Setup requires more configuration than Copilot

Head-to-Head: DevOps Use Cases

Writing Terraform

Kubernetes YAML

All three handle straightforward YAML well. For complex Helm charts with nested conditionals, Cursor + Claude is clearly the best.

GitHub Actions / CI/CD

Copilot's inline completion is best here since it understands the GitHub ecosystem deeply. Cursor is close. Continue is fine.

Bash / Shell Scripting

All three are similar for simple scripts. For complex scripts with error handling, Cursor's codebase context helps.

Recommendation

Use Cursor if you're an individual DevOps engineer or work in a startup. The codebase-aware context and Composer make it the best tool for infrastructure-heavy work. Use Claude 3.5 Sonnet as the default model.

Use GitHub Copilot if you're in enterprise with data governance requirements, need JetBrains support, or your company provides it for free (it often comes with GitHub Enterprise).

Use Continue.dev if you have strict data policies, want to run local models, or work on sensitive infrastructure where you can't send code to third-party servers.

Getting Started

• Cursor: cursor.com — free tier is generous, upgrade to Pro for serious work
• GitHub Copilot: Available through GitHub — GitHub Student Pack gives it free for students
• Continue.dev: continue.dev + Ollama for local models

The best AI assistant is the one you'll actually use consistently. Start with Cursor's free tier — if it saves you even 30 minutes a day on Terraform and YAML, it pays for itself.