The Free AI Stack: GLM-5.1 + Gemini + Claude Without Paying a Dollar

Let me cook: 🔥 I run a production SaaS backend (multi-tenant SaaS platform) with three AI providers running in parallel. GLM-5.1 for coding, Gemini for research, Claude for complex architecture. Monthly cost: $0 (using free tiers). Here's how I built the setup.

---

---

The Setup: One System, Multiple Personalities

Our stack at a mid-size software agency:

# OpenClaw agent configuration
agents:
  backend-dev:
    imageModel: zai/glm-5.1
    fallbacks:
      - google-gemini-cli/gemini-3.1-pro-preview
      - google-gemini-cli/gemini-3.1-flash-preview

  qa-agent:
    imageModel: zai/glm-4.5-flash

  content-writer:
    imageModel: google-gemini-cli/gemini-3.1-pro-preview

  general-ai:
    imageModel: anthropic/claude-sonnet-4-6

Why three different models?

The magic: OpenClaw handles automatic failover. If GLM-5.1 fails → Gemini takes over → Claude as backup.

---

Model 1: Z.AI GLM-5.1 — The Coding Workhorse

My go-to for:

• Laravel controller/service implementation
• Bug fixes and code refactoring
• API documentation

Why it wins:

• Speed: ~40 tokens/second (10x faster than local LLMs)
• Accuracy: Correct Laravel syntax, Eloquent relationships, JWT auth
• Free Tier: 100K tokens/month for individual use

Real example from an LMS platform:

// Backend needed to handle seat concurrency
public function assignSeat(int $licenceId, int $userId): Seat
{
    $licence = Licence::lockForUpdate()->find($licenceId);

    if ($licence->seats_used >= $licence->seats_allocated) {
        throw new \Exception('No seats available');
    }

    $seat = Seat::create([
        'licence_id' => $licenceId,
        'user_id' => $userId,
        'status' => 'active',
    ]);

    $licence->increment('seats_used');

    return $seat;
}

What GLM-5.1 gave me:

• SELECT FOR UPDATE lock to prevent race conditions
• Correct migration schema
• Audit logging integration
• Full test coverage (8 tests)

Cost: ~$0.50/month (using 10K tokens/month)

---

Model 2: Google Gemini — The Research Copilot

My go-to for:

• Reading technical documentation (a government API API docs)
• Multi-modal tasks (analyzing screenshots)
• Research and planning

Why it wins:

• Multi-modal: Can "see" PDFs and images
• Research tools: Built-in web search
• Generous free tier: 150K tokens/month for OAuth auth

Real example from the platform project:

We needed to integrate with a government API (a government registry). The API requires approval and costs money.

Solution with Gemini:

# Gemini analyzed the documentation
gemini "Analyze this PDF: /path/to/dfe-registry-api-docs.pdf"
# Result: Summary of endpoints, rate limits, approval process

Cost: $0 (using Google AI Pro subscription)

---

Model 3: Anthropic Claude — The Architecture Thinker

My go-to for:

• System design reviews
• Complex trade-off decisions
• Long-form reasoning

Why it wins:

• Reasoning depth: Thinks before answering
• Safety: Hard to jailbreak
• Context window: 200K tokens for detailed codebases

Real example from a Shopify app:

We're building a Shopify multi-platform auto-posting app. Claude helped decide:

Decision 1: Should we use one queue or separate queues per platform?

• Claude's analysis:

  • Single queue: Simpler deployment, higher contention
  • Separate queues: Complex but better isolation

• Decision: Single queue for simplicity (for now), add job batching

Decision 2: Should we use Stripe vs PayPal?

• Claude's analysis: Stripe API is more mature for subscription management

Cost: ~$5/month (pay-per-use)

---

Building the Setup Without Breaking the Bank

1. OAuth + Shared Subscriptions (Zero Cost)

Instead of individual API keys, use Google AI Pro account:

# OAuth login
gemini authenticate

# All OpenClaw agents use same subscription

Result: $0 monthly cost, no key management.

2. Smart Model Selection

# Don't use Claude for simple tasks
simple-fix:
  model: zai/glm-4.5-flash  # Cheap, fast

# Only use expensive models for complex work
architecture-decisions:
  model: anthropic/claude-sonnet-4-6  # Expensive, but worth it

3. Token Budgeting

// Track usage in daily memory
[
    'date' => '2026-04-12',
    'zai_tokens' => 85000,
    'gemini_tokens' => 120000,
    'claude_tokens' => 45000,
]

---

The Failover System (Magic)

OpenClaw automatically handles provider failures:

# Backend dev agent config
fallbacks:
  - google-gemini-cli/gemini-3.1-pro-preview  # Fallback 1
  - google-gemini-cli/gemini-3.1-flash-preview  # Fallback 2
  - zai/glm-4.7  # Fallback 3
  - zai/glm-4.7-flash  # Fallback 4
  - zai/glm-5  # Fallback 5

Example failover in action:

Attempt 1: GLM-5.1 ✅
Attempt 2: GLM-5.1 timeout ⏳
Attempt 3: Gemini 3.1 Pro ✅ (automatic switch)

Zero downtime. Zero manual intervention.

---

---

How We Use It at a mid-size software agency

Daily Workflow

Example: Building School LMS

Step 1: Define requirements

Gemini: "Research Laravel multi-tenancy patterns"

Step 2: Generate code

GLM-5.1: "Create SchoolService with CRUD + audit"

Step 3: Review architecture

Claude: "Review middleware stack for security"

Step 4: Test

QA Agent: "Run PHPStan + tests"

Cost per sprint: ~$3 (3 sprints/month)

---

Pro Tips

1. Use Light Context for Simple Tasks

# Don't use full context for simple edits
simple-fix:
  model: zai/glm-4.5-flash
  thinking: off  # Faster

2. Cache Expensive Model Responses

// Save Claude's architecture decision for future reference
if (cache()->has('architecture-decision')) {
    return cache()->get('architecture-decision');
}

$decision = claude->generate('...');
cache()->put('architecture-decision', $decision, now()->addDays(30));
return $decision;

3. Monitor Token Usage Daily

# Script to check costs
python3 << 'EOF'
import requests

# Check Z.AI usage
zai_tokens = requests.get('https://api.z.ai/usage')
print(f"Z.AI: {zai_tokens.json()['tokens']} tokens")

# Check Google usage
gemini_tokens = requests.get('https://oauth.googleapis.com/analytics')
print(f"Google: {gemini_tokens.json()['tokens']} tokens")

# Calculate cost
print(f"Total: ~${calculate_cost()}")
EOF

---

The Monthly Cost Breakdown

a mid-size software agency AI Stack:

Per project (an LMS platform): ~$1.20/month

Compared to local LLMs:

• 3x faster inference
• 5x better code quality
• $0 hardware cost (no GPU needed)

---

The Takeaway

You don't need to pay for AI to get great results:

1. Start with GLM-5.1 for coding — fastest and most accurate
2. Add Gemini for research and multi-modal tasks
3. Use Claude sparingly for architecture decisions
4. Use OAuth + free tiers to avoid individual subscriptions

The math:

• Local LLM: Free but slow + mediocre quality
• Single cloud API: $10-20/month
• Three-provider stack: $3.90/month

Better quality, same speed, cheaper.

My recommendation: Build this stack. Your code will thank you.

---

What's your AI stack? Single provider, multiple providers, or local LLMs? Drop a comment — I'm curious how others balance cost vs quality.