Groq + DeepSeek Free API Stack: How WordPress Developers Can Ship Plugins With $0 Budget

I manage over 100 WordPress plugins. For two years, AI API costs were a real line item. Then I rebuilt my dev workflow around two free tiers that most developers overlook. My monthly AI spend dropped to $0 for 80% of the work. This is the exact stack I use.

The Free Tier Reality Check

Most developers assume “free” means toy-tier rate limits with models that can’t handle real code. That assumption is wrong as of 2026. Both Groq and DeepSeek run production-grade models on genuinely free tiers, no credit card required to get started, no arbitrary trial limits that expire after 7 days.

Here is what you actually get:

The gap between free and paid is significant when you’re iterating on plugin code all day. For context: a typical plugin refactor session runs 50-150 API calls. On Groq’s free tier, that’s well within the daily limit before you’ve spent a cent.

Groq: What You Get and How to Set It Up

Groq runs inference on custom LPU hardware. The result is response speeds that feel instant compared to GPU-based providers. For plugin development, this matters more than you’d think: fast responses mean you stay in flow instead of waiting for code suggestions.

Getting Your API Key

1. Go to console.groq.com
2. Sign up with email only (no credit card)
3. Navigate to API Keys and create a new key
4. Copy and store it securely

Making Your First API Call

The Groq API is OpenAI-compatible. Any code you’ve written for OpenAI works here with two changes: the base URL and the model name.

# Groq API - fast inference, free tier
curl https://api.groq.com/openai/v1/chat/completions \
  -H "Authorization: Bearer YOUR_GROQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.3-70b-versatile",
    "messages": [
      {
        "role": "user",
        "content": "Write a WordPress function that sanitizes and validates an admin settings array with keys: api_url (URL), max_results (int 1-100), enable_cache (bool)"
      }
    ],
    "temperature": 0.2,
    "max_tokens": 1024
  }'

For WordPress plugin work, I use llama-3.3-70b-versatile as the default. It handles PHP well, understands WordPress hooks and filters, and responds in under 2 seconds. For anything requiring multi-step reasoning (like analyzing a complex plugin conflict), I switch to deepseek-r1-distill-llama-70b on the same Groq endpoint.

Groq Free Tier Limits in Practice

The 30 req/min limit sounds tight. In practice, it isn’t. Plugin development doesn’t generate continuous API traffic. You write some code, ask a question, review the response, write more code. The burst limit is what matters, and 30 req/min is plenty for a single developer workflow.

Where you’ll hit the limit: automated test generation loops or bulk code review scripts. For those cases, add a simple sleep between calls or route to DeepSeek instead.

DeepSeek: The Coding-Focused Free Tier

DeepSeek trained purpose-built coding models. deepseek-coder outperforms GPT-4o on most coding benchmarks and is available free via platform.deepseek.com. New accounts get 5 million tokens on signup. That’s not a trial. It’s actual capacity you can use immediately.

DeepSeek API Setup

# DeepSeek API - coding-focused model
curl https://api.deepseek.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_DEEPSEEK_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-coder",
    "messages": [
      {
        "role": "system",
        "content": "You are a WordPress backend developer. Write clean, WPCS-compliant PHP."
      },
      {
        "role": "user",
        "content": "Refactor this plugin class to use lazy loading for the database queries and add proper caching with transients: [paste your code]"
      }
    ],
    "temperature": 0.1
  }'

For reasoning tasks, switch to deepseek-r1. It uses chain-of-thought internally and gives you much better results on anything involving “why is this code wrong” or architectural decisions.

Which DeepSeek Model for Which Task

• deepseek-chat: Documentation, code comments, readme writing, admin copy
• deepseek-coder: PHP refactoring, SQL query optimization, REST API endpoint writing, unit test generation
• deepseek-r1: Debugging complex plugin conflicts, architecture decisions, security review of sensitive code paths

The Fall-Through Routing Pattern

The pattern I actually use in my development environment is a simple fall-through router. When Groq’s rate limit is hit, the request falls through to DeepSeek. When DeepSeek is slow (rare), it falls through to a paid provider. Most requests never leave the free tier.

import anthropic
import groq
import openai
import time

def wp_dev_query(prompt: str, task_type: str = "code") -> str:
    """
    Fall-through router: Groq first, DeepSeek fallback, paid last resort.
    task_type: 'code' | 'reason' | 'docs'
    """
    
    # Route by task type
    if task_type == "code":
        primary_provider = "groq"
        primary_model = "llama-3.3-70b-versatile"
        fallback_provider = "deepseek"
        fallback_model = "deepseek-coder"
    elif task_type == "reason":
        primary_provider = "groq"
        primary_model = "deepseek-r1-distill-llama-70b"
        fallback_provider = "deepseek"
        fallback_model = "deepseek-r1"
    else:  # docs
        primary_provider = "deepseek"
        primary_model = "deepseek-chat"
        fallback_provider = "groq"
        fallback_model = "llama-3.3-70b-versatile"
    
    # Try primary
    try:
        if primary_provider == "groq":
            client = groq.Groq(api_key="YOUR_GROQ_KEY")
            response = client.chat.completions.create(
                model=primary_model,
                messages=[{"role": "user", "content": prompt}],
                temperature=0.1
            )
            return response.choices[0].message.content
        elif primary_provider == "deepseek":
            client = openai.OpenAI(
                api_key="YOUR_DEEPSEEK_KEY",
                base_url="https://api.deepseek.com/v1"
            )
            response = client.chat.completions.create(
                model=primary_model,
                messages=[{"role": "user", "content": prompt}]
            )
            return response.choices[0].message.content
    except Exception as e:
        print(f"Primary failed ({primary_provider}): {e}")
    
    # Fallback
    try:
        if fallback_provider == "deepseek":
            client = openai.OpenAI(
                api_key="YOUR_DEEPSEEK_KEY",
                base_url="https://api.deepseek.com/v1"
            )
            response = client.chat.completions.create(
                model=fallback_model,
                messages=[{"role": "user", "content": prompt}]
            )
            return response.choices[0].message.content
    except Exception as e:
        print(f"Fallback failed: {e}")
        raise

# Usage examples
php_code = wp_dev_query(
    "Write a WordPress REST API endpoint that returns paginated BuddyPress member data",
    task_type="code"
)

architecture = wp_dev_query(
    "Should I store plugin settings in wp_options or a custom table? My plugin has 50+ settings and needs per-user overrides.",
    task_type="reason"
)

This pattern keeps your code abstracted from any single provider. You can swap Groq for a different fast provider without touching the calling code.

Real WordPress Plugin Dev Workflow

Here is how this plays out when I’m building a new plugin from scratch. Say I need a plugin that adds a simple REST API to expose BuddyPress group membership data to an external app.

Phase 1: Rapid Prototyping with Groq

I use Groq for everything that needs fast iteration. First-pass code generation, boilerplate scaffolding, function signature drafts. The speed means I can generate 10 variations and pick the best one without breaking flow.

Prompt pattern that works well:

Generate the PHP class structure for a WordPress REST API plugin with:
- Namespace: WbcomDesigns\MembershipAPI
- Endpoint: /wp-json/wbcom/v1/bp-groups/{group_id}/members
- Authentication: Application passwords + nonce
- Response: paginated member list with avatar_url, display_name, joined_date
- Error handling: standard WP_Error responses
- WPCS compliance required
Do NOT include the full implementation, just the class skeleton with method stubs and DocBlocks.

Groq returns the full skeleton in under 2 seconds. I review it, adjust the structure, then move to implementation.

Phase 2: Refactoring and Optimization with DeepSeek-Coder

Once I have working code, I paste it into DeepSeek-Coder for refactoring. This is where the coding-specialized model earns its place. It catches things general models miss: N+1 query patterns in WordPress, transient misuse, missing nonce verification on admin-only endpoints.

My refactor prompt template:

Review this WordPress plugin code for:
1. N+1 database queries (suggest batch queries or transients)
2. Missing sanitization/escaping (WPCS standard)
3. Memory efficiency for sites with 10,000+ users
4. Any security gaps in the REST endpoint authentication

Return only the specific changes needed, not the full rewritten file.

[PASTE CODE BELOW]

# OpenRouter - single API for multiple providers
curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer YOUR_OPENROUTER_KEY" \
  -H "HTTP-Referer: https://yoursite.com" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "groq/llama-3.3-70b-versatile",
    "messages": [
      {
        "role": "user",
        "content": "Write WPCS-compliant PHP for a settings page using Settings API"
      }
    ]
  }'