Cloudflare Cost Optimization Skill

Analyze Cloudflare architectures and predict monthly costs with actionable optimization recommendations. This skill provides engineering-grade cost estimates based on 2026 Cloudflare pricing.

Pricing Reference (2026)

Workers

Requests: $0.30/million (after 10M free)
CPU Time: $0.02/million GB-seconds
Unbound: $12.50/million duration-ms (15M free)
Subrequests: Count against request limits

D1 (SQLite)

Reads: $0.25/billion rows
Writes: $1.00/million rows (4x more expensive than reads!)
Storage: $0.75/GB/month
Cost Trap: for(row){db.insert()} = N× writes. Always batch ≤1,000.

R2 (Object Storage)

Class A (writes): $4.50/million
Class B (reads): $0.36/million
Storage: $0.015/GB/month
Egress: FREE (major advantage)

KV (Key-Value)

Reads: $0.50/million
Writes: $5.00/million (10x more than reads!)
Storage: $0.50/GB/month
Rate Limit: 1 write/sec/key

Queues

Standard: $0.40/million messages
Batch: $0.40/million batches
Cost Trap: max_retries: 3 = up to 3× message cost

Vectorize

Queries: $0.01/million
Stored Vectors: $0.05/100M dimensions×vectors
Hard Limit: 5M vectors/index, 1,536 dimensions

Workers AI

Neurons: $0.011/1,000 (reset daily UTC)
Large Models (Llama 11B+): $0.68/M output tokens - expensive!
Recommendation: Use smaller models (1B-8B) or Gemini Flash for bulk

AI Gateway

Caching: Only caches IDENTICAL prompts (no semantic)
Logs: 10M free, then $0.10/million
Cost Trap: Forgetting cache = paying full LLM cost every time

Analytics Engine

Essentially FREE - no per-write charges
Note: Use SUM(_sample_interval) at scale (adaptive sampling)

Analysis Workflow

Step 1: Gather Architecture Data

Use MCP tools to collect current usage:

1. Read wrangler.toml/wrangler.jsonc for bindings
2. Query cloudflare-observability for Worker metrics
3. Query cloudflare-ai-gateway for AI costs
4. Check cloudflare-bindings for resource lists

Step 2: Calculate Per-Service Costs

For each service bound in wrangler config:

Workers:

monthly_cost = (requests - 10M) / 1M * $0.30
            + cpu_gb_seconds / 1M * $0.02

D1:

monthly_cost = reads / 1B * $0.25
            + writes / 1M * $1.00
            + storage_gb * $0.75

R2:

monthly_cost = class_a_ops / 1M * $4.50
            + class_b_ops / 1M * $0.36
            + storage_gb * $0.015

KV:

monthly_cost = reads / 1M * $0.50
            + writes / 1M * $5.00
            + storage_gb * $0.50

Queues:

monthly_cost = messages / 1M * $0.40 * (1 + avg_retries)

Step 3: Identify Cost Drivers

Flag any service that's >20% of total cost. Common patterns:

| Cost Driver | Typical Cause | Fix | |-------------|--------------|-----| | D1 writes dominating | Per-row inserts | Batch to ≤1,000 | | Queue costs high | Retries enabled | Set max_retries: 1 if idempotent | | AI Gateway expensive | No caching | Enable cache, deduplicate prompts | | Workers AI | Large model | Switch to smaller model or external LLM | | R2 Class A | Frequent writes | Buffer writes, use R2 presigned |

Step 4: Generate Recommendations

For each optimization opportunity, provide:

Current: What it costs now
Optimized: What it could cost
Savings: Monthly/annual savings
Trade-off: What changes in behavior
Implementation: Specific code/config change

Output Format

# Cloudflare Cost Analysis

## Monthly Cost Estimate: $X.XX

### Breakdown by Service

| Service | Cost | % of Total | Status |
|---------|------|------------|--------|
| D1 | $X.XX | X% | ⚠️ Cost driver |
| Workers | $X.XX | X% | ✅ Normal |
| R2 | $X.XX | X% | ✅ Normal |

### Cost Drivers Identified

1. **D1 Writes** (80% of total)
   - Current: 50M writes/month = $50
   - Pattern detected: Per-row inserts in cron job
   - Fix: Batch inserts to ≤1,000 rows

### Optimization Opportunities

| Opportunity | Current | Optimized | Savings | Effort |
|-------------|---------|-----------|---------|--------|
| Batch D1 writes | $50/mo | $5/mo | $45/mo ($540/yr) | Low |
| Reduce queue retries | $10/mo | $3/mo | $7/mo ($84/yr) | Trivial |

### Warnings

- ⚠️ D1 writes >50M/day is a red flag
- ⚠️ Workers AI Llama 11B is expensive for high-volume

### Action Items

1. [ ] Change `for(row){insert()}` to `db.batch()` in `processor.ts`
2. [ ] Set `max_retries: 1` for `layer2-queue` in wrangler.jsonc
3. [ ] Consider switching AI model from llama-3-11b to llama-3-8b

MCP Tools to Use

mcp__cloudflare-observability__query_worker_observability - Worker request/duration metrics
mcp__cloudflare-ai-gateway__list_logs - AI request costs
mcp__cloudflare-bindings__workers_get_worker - Worker details
mcp__cloudflare-bindings__d1_databases_list - D1 databases
mcp__cloudflare-bindings__r2_buckets_list - R2 buckets
mcp__cloudflare-bindings__kv_namespaces_list - KV namespaces

Tips

D1 is usually the culprit: Writes are 4× more expensive than reads
Queue retries multiply costs: Each retry = another message charge
Analytics Engine is nearly free: Use it heavily for metrics
R2 egress is free: Use R2 over S3 when possible
AI caching only works for identical prompts: Deduplicate inputs

Example Usage

When user asks:

"How much is this costing me?"
"Optimize my Cloudflare costs"
"Why is my D1 bill so high?"
"Estimate monthly costs for this architecture"

Invoke this skill to provide detailed cost analysis with actionable recommendations.