Fastly Compute
This page is a decision brief, not a review. It explains when Fastly Compute tends to fit, where it usually struggles, and how costs behave as your needs change. Side-by-side comparisons live on separate pages.
Quick signals
What this product actually is
Edge compute runtime for performance-sensitive request handling and programmable networking patterns close to users.
Pricing behavior (not a price list)
These points describe when users typically pay more, what actions trigger upgrades, and the mechanics of how costs escalate.
Actions that trigger upgrades
- You need more complex state patterns and operational ownership at the edge
- Runtime constraints block required dependencies or workloads
- You need clearer cost modeling for global traffic and networking
When costs usually spike
- Edge state/data locality decisions shape architecture early
- Debuggability requires distributed tracing and consistent logging practices
- Cost mechanics can shift with global distribution and egress
- Lock-in grows if edge-specific APIs are deeply embedded
Plans and variants (structural only)
Grouped by type to show structure, not to rank or recommend specific SKUs.
Plans
- Edge request handling - performance lane - Best for low-latency middleware, routing, and programmable edge behavior.
- State strategy - pick the pattern - Decide early how you’ll handle state and data locality (cache/KV/queues) without breaking latency goals.
- Operational ownership - tracing at the edge - Standardize logs/traces so tail latency and failures aren’t invisible.
- Official docs: https://developer.fastly.com/learning/compute/
Costs & limitations
Common limits
- Edge constraints can limit heavy dependencies and certain compute patterns
- Not a broad cloud-native event ecosystem baseline
- State and data locality require deliberate architectural choices
- Observability and debugging need strong discipline at the edge
- Edge-specific APIs can increase lock-in
What breaks first
- Architecture fit if you treat edge like regional cloud
- Debuggability without strong observability pipelines
- Portability as edge-specific patterns deepen
- State and data locality assumptions as features grow
Fit assessment
Good fit if…
- Latency-sensitive request paths that benefit from edge execution
- Programmable networking and edge middleware patterns
- Global products where tail latency affects UX
- Teams comfortable with edge constraints and architecture trade-offs
Poor fit if…
- You need deep cloud-native triggers and managed event ecosystems as the default
- You want maximum portability and minimal platform-specific edge patterns
- You need long-running or heavy compute per request
Trade-offs
Every design choice has a cost. Here are the explicit trade-offs:
- Edge latency wins → Tighter runtime constraints and architecture shifts
- Global distribution → More need to think about data locality and caching
- Great for request path → Not the default for broad event ecosystems
Common alternatives people evaluate next
These are common “next shortlists” — same tier, step-down, step-sideways, or step-up — with a quick reason why.
-
Cloudflare Workers — Same tier / edge runtimeDirect alternative for edge-first execution model decisions (constraints, workflow, platform fit).
-
AWS Lambda — Step-sideways / regional serverlessConsidered when event-driven integrations and regional cloud patterns matter more than edge latency.
-
Vercel Functions — Step-sideways / web platform functionsCompared by web teams deciding between edge compute and platform-integrated functions tied to deployment workflow.
Sources & verification
Pricing and behavioral information comes from public documentation and structured research. When information is incomplete or volatile, we prefer to say so rather than guess.