Quick Verdict

For teams with a budget over $10,000 per month and requiring advanced edge computing capabilities, Fastly Compute is the better choice. However, for smaller teams or those with limited JavaScript expertise, @edge offers a more accessible and cost-effective solution. Ultimately, the decision depends on your specific use case and the trade-offs between cost, complexity, and functionality.

Feature Comparison Table

When to Choose Fastly Compute

• If you’re a 50-person SaaS company needing to handle over 100,000 requests per second, Fastly Compute’s scalability and advanced edge computing features make it the better choice.
• For teams with extensive JavaScript expertise, Fastly Compute’s customizability and support for serverless functions are major advantages.
• If your application requires complex A/B testing and real-time analytics, Fastly Compute’s edge computing features provide more flexibility and control.
• For large enterprises with a budget over $50,000 per month, Fastly Compute’s custom quotes and priority support ensure a high level of service and reliability.

When to Choose @edge

• If you’re a 10-person startup with limited JavaScript expertise, @edge’s more accessible pricing and moderate learning curve make it a better fit.
• For teams with smaller budgets (under $5,000 per month), @edge’s tiered pricing and cost-effective solution are more attractive.
• If your application requires simple edge-side includes and A/B testing, @edge’s features are sufficient and easier to implement.
• For small to medium-sized teams with limited edge computing requirements, @edge’s community support and paid support options provide adequate assistance.

Real-World Use Case: Edge Computing

Let’s consider a real-world scenario where a 20-person e-commerce company needs to handle 10,000 requests per second and requires edge-side includes and A/B testing.

• Setup complexity: Fastly Compute requires 2-3 days of setup, while @edge can be set up in 1-2 days.
• Ongoing maintenance burden: Fastly Compute requires more maintenance due to its customizability and complexity, while @edge is relatively low-maintenance.
• Cost breakdown for 100 users/actions: Fastly Compute would cost around $550 per month (based on $0.0055 per request), while @edge would cost around $100 per month (based on $0.01 per request).
• Common gotchas: With Fastly Compute, the steep learning curve and customizability can lead to errors and increased maintenance. With @edge, the limited scalability and lack of advanced edge computing features may become bottlenecks as the application grows.

Migration Considerations

If switching between these tools:

• Data export/import limitations: Fastly Compute has more restrictive data export policies, while @edge allows for easier data migration.
• Training time needed: Fastly Compute requires more extensive training due to its complexity and customizability, while @edge requires moderate training.
• Hidden costs: Fastly Compute may have additional costs for custom support and priority features, while @edge has more transparent pricing.

FAQ

Q: Which tool is better for real-time analytics? A: Fastly Compute offers more advanced real-time analytics features, including support for serverless functions and edge-side includes.

Q: Can I use both Fastly Compute and @edge together? A: Yes, you can use both tools together, but it may require custom integration and additional maintenance. It’s essential to evaluate the benefits and costs of using both tools before deciding.

Q: Which has better ROI for Edge Computing? A: Based on a 12-month projection, @edge offers a better ROI for small to medium-sized teams with limited edge computing requirements, while Fastly Compute provides a better ROI for large enterprises with complex edge computing needs and high traffic volumes.

Bottom Line: Fastly Compute is the better choice for large enterprises with complex edge computing requirements and high traffic volumes, while @edge is more suitable for small to medium-sized teams with limited edge computing needs and budgets.

🔍 More Fastly Compute Comparisons

Explore all Fastly Compute alternatives or check out @edge reviews.