What This Template Is For
Edge computing moves processing closer to the source of data. Instead of sending every request to a centralized cloud region, computation happens at locations geographically near the user or device. The result is lower latency, reduced bandwidth costs, and the ability to function when connectivity to the central cloud is unreliable.
Not every workload benefits from edge computing. A CRUD API serving users in a single region gains nothing from edge deployment. But real-time applications (video processing, IoT telemetry, game state, financial trading), latency-sensitive user experiences (personalization, A/B testing at the CDN layer), and bandwidth-heavy workloads (media transcoding, sensor data aggregation) can see order-of-magnitude improvements.
This template helps teams evaluate whether edge computing is the right fit, choose the right edge platform, design the data synchronization strategy, and plan the deployment architecture. For teams planning broader infrastructure changes, the Technical PM Handbook covers distributed systems patterns. If edge computing is part of a multi-region strategy, the multi-region deployment template provides the complementary regional architecture plan. For documenting the decision to move to edge, use the architecture decision record template.
How to Use This Template
- Start with the Use Case Evaluation. Not every feature belongs at the edge. Identify which workloads have latency, bandwidth, or availability requirements that justify edge deployment.
- Define Latency Budgets for each use case. Quantify how much latency improvement edge computing must deliver to justify the added complexity.
- Select your Edge Platform. The options range from CDN-based compute (Cloudflare Workers, Lambda@Edge) to full edge nodes (bare metal or VMs in colo facilities).
- Design the Data Synchronization strategy. This is the hardest part. Edge nodes need data to make decisions, but synchronizing state across distributed locations introduces consistency challenges.
- Plan the Compute Placement. Decide what logic runs at the edge versus what stays centralized.
- Configure Failover and Degradation. Edge nodes fail. Define what happens when they do.
The Template
Architecture Overview
| Field | Details |
|---|---|
| Project Name | [Name of the edge computing initiative] |
| Architecture Owner | [Name, title] |
| Edge Platform | [e.g., Cloudflare Workers, Lambda@Edge, Fastly Compute, custom edge nodes] |
| Number of Edge Locations | [e.g., 15 PoPs, 200+ CDN nodes, 5 colo sites] |
| Central Cloud Region | [e.g., us-east-1, origin region for non-edge workloads] |
| Target Latency Improvement | [e.g., P99 from 180ms to 40ms for US users] |
| Timeline | [e.g., Q2-Q3 2026] |
Use Case Evaluation
| Use Case | Current Latency | Target Latency | Bandwidth | Availability Req | Edge Candidate? |
|---|---|---|---|---|---|
| [e.g., API authentication] | [120ms P99] | [< 30ms] | [Low] | [99.99%] | [Yes / No] |
| [e.g., Image optimization] | [N/A, done server-side] | [< 50ms added] | [High, 2TB/day] | [99.9%] | [Yes / No] |
| [e.g., Personalization] | [200ms P99] | [< 50ms] | [Low] | [99.9%] | [Yes / No] |
| [e.g., IoT data ingestion] | [300ms P99] | [< 100ms] | [High, 1M events/min] | [99.95%] | [Yes / No] |
| [e.g., User dashboard CRUD] | [150ms P99] | [No change needed] | [Low] | [99.9%] | [No, stays centralized] |
Edge suitability criteria:
A workload is a good edge candidate if it meets 2+ of the following:
- ☐ Latency-sensitive: users or devices need sub-50ms response times
- ☐ Bandwidth-heavy: processing data at the edge reduces egress costs significantly
- ☐ Read-heavy: mostly reads, infrequent writes (simplifies consistency)
- ☐ Stateless or eventually consistent: can tolerate stale data for seconds or minutes
- ☐ Geographically distributed: users or devices are spread across many regions
- ☐ Availability-critical: must function during cloud region outages
Latency Budget
| Component | Budget (ms) | Notes |
|---|---|---|
| DNS resolution | [5] | [CDN DNS, anycast] |
| TLS handshake | [10] | [TLS 1.3, session resumption, edge-terminated] |
| Edge compute | [15] | [Function execution time at edge PoP] |
| Edge cache lookup | [2] | [Local cache hit at edge node] |
| Origin fetch (cache miss) | [80-150] | [Only on cache miss, async revalidation preferred] |
| Data sync overhead | [0 (async)] | [Background sync, does not add to request latency] |
| Total (cache hit) | [32ms] | Target met |
| Total (cache miss) | [112-182ms] | Acceptable, cache hit rate target: > 90% |
Edge Platform Selection
| Platform | Type | Runtime | Locations | Cold Start | Max Execution | Decision |
|---|---|---|---|---|---|---|
| Cloudflare Workers | CDN compute | V8 isolates (JS/Wasm) | 300+ PoPs | < 5ms | 30s (free) / 15min (paid) | [Selected / Rejected] |
| AWS Lambda@Edge | CDN compute | Node.js, Python | 400+ CloudFront PoPs | 50-100ms | 30s (viewer) / 30s (origin) | [Selected / Rejected] |
| Fastly Compute | CDN compute | Wasm (Rust, Go, JS) | 90+ PoPs | < 1ms | No limit (billing-based) | [Selected / Rejected] |
| AWS Wavelength | Telco edge | Full EC2 | 30+ carrier zones | N/A (always-on) | Unlimited | [Selected / Rejected] |
| Custom edge nodes | Bare metal / colo | Any | [N custom locations] | N/A (always-on) | Unlimited | [Selected / Rejected] |
| Fly.io | App platform | Containers (Firecracker) | 35+ regions | < 500ms | Unlimited | [Selected / Rejected] |
Selected platform: [Platform name]
Rationale: [2-3 sentences explaining why this platform was chosen over alternatives]
Compute Placement
| Function | Runs At | Rationale |
|---|---|---|
| [TLS termination] | [Edge] | [Reduces round-trip latency for TLS handshake] |
| [Request routing / load balancing] | [Edge] | [Route to nearest healthy origin] |
| [Authentication / token validation] | [Edge] | [Reject unauthorized requests before hitting origin] |
| [Static asset serving] | [Edge (CDN cache)] | [Serve from cache, avoid origin fetch] |
| [Image/video transformation] | [Edge] | [Reduce bandwidth, serve optimized assets] |
| [A/B test assignment] | [Edge] | [Assign variant at CDN, no origin round-trip] |
| [Rate limiting] | [Edge] | [Block abuse before it reaches origin] |
| [Business logic (CRUD)] | [Central cloud] | [Requires strong consistency, database access] |
| [Database writes] | [Central cloud] | [Single source of truth, ACID transactions] |
| [Batch processing] | [Central cloud] | [Not latency-sensitive, needs large compute] |
| [ML model inference] | [Edge or cloud] | [Depends on model size and latency requirement] |
Data Synchronization Strategy
Data Classification
| Data Type | Consistency Requirement | Sync Strategy | Staleness Tolerance |
|---|---|---|---|
| [User session / auth tokens] | [Strong] | [Edge-validated JWT, no sync needed] | [0 (stateless validation)] |
| [Feature flags / config] | [Eventual] | [Push from central, poll every 30s] | [30 seconds] |
| [Product catalog / prices] | [Eventual] | [CDN cache + cache invalidation on change] | [5 minutes] |
| [User preferences] | [Eventual] | [Read from edge cache, write to central, async propagate] | [1 minute] |
| [Inventory / stock levels] | [Strong-ish] | [Read from central on each request, no edge caching] | [0 (always fresh)] |
| [Analytics / telemetry] | [Best effort] | [Buffer at edge, batch upload every 60s] | [N/A (write path)] |
Conflict Resolution
| Scenario | Resolution Strategy |
|---|---|
| [Two edge nodes update same record simultaneously] | [Last-write-wins with vector clock / timestamp] |
| [Edge node writes while offline, central state changed] | [Central state wins, edge changes queued for retry] |
| [Cache invalidation during write] | [Write-through: write to central, invalidate all edge caches] |
Edge Caching Strategy
| Content Type | Cache Location | TTL | Invalidation Method |
|---|---|---|---|
| [Static assets (JS, CSS, images)] | [Edge CDN] | [1 year (fingerprinted URLs)] | [New URL on deploy] |
| [API responses (GET, cacheable)] | [Edge CDN] | [60 seconds] | [Purge API on data change] |
| [HTML pages (SSR/SSG)] | [Edge CDN] | [300 seconds] | [Stale-while-revalidate] |
| [User-specific responses] | [Not cached / Vary header] | [N/A] | [N/A] |
| [Configuration / feature flags] | [Edge KV store] | [30 seconds] | [Push update from central] |
Monitoring and Observability
| Metric | Source | Alert Threshold |
|---|---|---|
| Edge function execution time (P50, P95, P99) | [Edge platform metrics] | [P99 > 100ms] |
| Cache hit rate (per edge location) | [CDN analytics] | [< 80%] |
| Origin fetch latency | [Edge-to-origin timing] | [P99 > 300ms] |
| Error rate at edge | [Edge function logs] | [> 0.1% 5xx] |
| Data sync lag (edge to central) | [Custom metric] | [> 5 minutes] |
| Edge node availability | [Health checks] | [Any node down > 2 minutes] |
Failover Design
| Failure Scenario | Detection | Response | Recovery Time |
|---|---|---|---|
| [Single edge node failure] | [Health check failure] | [DNS/anycast routes to next nearest node] | [< 30 seconds] |
| [Edge platform outage (all nodes)] | [Synthetic monitoring] | [Fail open: route directly to origin] | [1-5 minutes] |
| [Central origin outage] | [Origin health checks] | [Edge serves stale cache + degraded mode] | [Depends on origin recovery] |
| [Data sync failure] | [Sync lag metric] | [Edge continues with stale data, alert ops] | [Async, no user impact] |
| [Edge function error] | [Error rate spike] | [Roll back to previous function version] | [< 2 minutes] |
Filled Example: IoT Fleet Analytics Platform
Architecture Overview
| Field | Details |
|---|---|
| Project Name | FleetVision Edge Analytics |
| Architecture Owner | Nina Rodriguez, Principal Engineer |
| Edge Platform | Custom edge nodes (12 colo facilities) + Cloudflare Workers (CDN layer) |
| Number of Edge Locations | 12 colo edge nodes + 300 Cloudflare PoPs |
| Central Cloud Region | us-east-1 (AWS) |
| Target Latency Improvement | Telemetry ingestion P99 from 280ms to 45ms |
Use Case Analysis
FleetVision processes telemetry from 50,000 connected vehicles. Each vehicle sends GPS, engine diagnostics, and driver behavior data every 2 seconds. At peak, the system ingests 500,000 events per minute. Sending all data to a central cloud region created three problems:
- Latency. Vehicles in rural areas experienced 300ms+ round-trip times to us-east-1. Real-time alerts (harsh braking, route deviation) arrived too late to be actionable.
- Bandwidth cost. Raw telemetry at 500K events/min generated $22,000/month in data transfer costs alone.
- Reliability. Cellular connectivity drops for 10-15% of fleet time. Events were lost during outages.
Compute Placement Decision
| Function | Location | Rationale |
|---|---|---|
| Telemetry ingestion and buffering | Edge node | Accept data locally, buffer during connectivity gaps |
| Real-time anomaly detection | Edge node | ML inference on-device or at nearest edge node, sub-50ms alerts |
| Data aggregation (5-min rollups) | Edge node | Reduce 500K events/min to 50K aggregated records, 90% bandwidth reduction |
| Dashboard API (fleet overview) | Central cloud | Reads from central data warehouse, not latency-critical |
| Historical reporting | Central cloud | Batch processing against full dataset |
| Driver-facing mobile API | Cloudflare Workers | Auth, personalization, push notification targeting at CDN edge |
Results
After deploying edge nodes, telemetry ingestion latency dropped from 280ms P99 to 38ms P99. Data transfer costs dropped by $18,000/month (82% reduction) because aggregation at the edge reduced the volume of data sent to the central cloud. Alert delivery time for safety events improved from 2.1 seconds to 340 milliseconds. The SLA definition template was updated to reflect the new latency guarantees.
Common Mistakes to Avoid
- Moving everything to the edge. Edge computing adds operational complexity: distributed state, eventual consistency, multi-location deployments, and harder debugging. Only move workloads where latency, bandwidth, or availability requirements justify the complexity. Keep CRUD operations, database writes, and batch jobs centralized.
- Ignoring data consistency. When the same data exists at the edge and in the central cloud, it will get out of sync. Design for this explicitly. Decide which data can be eventually consistent (most read-path data) and which must be strongly consistent (financial transactions, inventory).
- Underestimating cold start latency. CDN compute platforms (Lambda@Edge, Cloudflare Workers) have cold starts when a function has not been invoked recently at a particular PoP. For latency-sensitive workloads, factor in cold start time and use keep-alive strategies.
- Not testing from actual edge locations. Testing from your office (which may be near a cloud region) does not reveal the latency improvement edge computing provides to users in distant locations. Use synthetic monitoring from multiple geographies.
- Forgetting about observability. Debugging a distributed edge system is significantly harder than debugging a centralized one. Invest in distributed tracing, centralized log aggregation, and per-location metric dashboards before deploying to production.
Key Takeaways
- Evaluate each workload independently. Only move to the edge when latency, bandwidth, or availability requirements justify the added complexity
- Define explicit latency budgets and cache hit rate targets before building. These are your success metrics
- Design data synchronization carefully. Classify each data type by its consistency requirement and choose the appropriate sync strategy
- Plan for failure. Edge nodes will go down. Design failover so the system degrades gracefully rather than failing completely
- Invest in observability early. Distributed systems are harder to debug. Centralized logging, distributed tracing, and per-location dashboards are not optional
About This Template
Created by: Tim Adair
Last Updated: 3/5/2026
Version: 1.0.0
License: Free for personal and commercial use