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:

1. Current: What it costs now
2. Optimized: What it could cost
3. Savings: Monthly/annual savings
4. Trade-off: What changes in behavior
5. 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.