The Problem With Paid AI APIs
If you're building AI agents, you're probably spending $50-500/month on:
- OpenAI: $0.01-0.06 per 1K tokens
- Anthropic: $0.003-0.015 per 1K tokens
- Together AI: $0.0002-0.001 per 1K tokens
For a single agent doing 1000 calls/day, that's $300-18,000/month.
But there are 5 free APIs that handle 90% of agent workloads.
1. Google Gemini 3.1 Flash Lite
What: Google's fastest model, free tier with generous limits.
Specs:
- 15 requests/minute (free)
- 1M tokens/minute (free)
- 1500 requests/day (free)
- Context: 1M tokens
- Speed: ~300 tokens/second
Best for: Content generation, code review, analysis, summarization
import google.generativeai as genai
genai.configure(api_key="YOUR_KEY")
model = genai.GenerativeModel("gemini-3.1-flash-lite")
response = model.generate_content("Write a Dev.to article about AI agents")
print(response.text)
Gotcha: Model names change. gemini-2.0-flash is DEAD (shutdown June 2026). Always use gemini-3.1-flash-lite or newer.
2. Groq (Llama 3.3 70B)
What: Groq runs Llama on custom LPU chips — insanely fast.
Specs:
- 30 requests/minute (free)
- 6000 tokens/minute (free)
- Speed: 320 tokens/second (fastest free API)
- Model:
llama-3.3-70b-versatile
Best for: Fast agent loops, real-time responses, batch processing
from groq import Groq
client = Groq(api_key="YOUR_KEY")
response = client.chat.completions.create(
model="llama-3.3-70b-versatile",
messages=[{"role": "user", "content": "Analyze this code for bugs: ..."}],
max_tokens=500
)
Gotcha: Rate limits hit fast. Use a 4-second delay between calls. For reasoning models, content can be null — check reasoning field too.
3. OpenRouter Free Models
What: OpenRouter aggregates 100+ models. Several are completely free.
Free models available:
-
nvidia/nemotron-3.5-lightning:free— fast, capable -
z-ai/glm-5.2:free— Chinese model, good for multilingual -
google/gemma-4-31b-it:free— Google's open model -
meta-llama/llama-3.3-70b:free— Llama 70B, free
Best for: Fallback when Gemini/Groq hit rate limits
import openai
client = openai.OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="YOUR_OPENROUTER_KEY"
)
response = client.chat.completions.create(
model="nvidia/nemotron-3.5-lightning:free",
messages=[{"role": "user", "content": "Generate a Python web scraper"}],
max_tokens=1000
)
4. HuggingFace Inference API
What: Free inference for 100,000+ models on HuggingFace Hub.
Specs:
- Free tier: limited but usable
- Models: text generation, summarization, translation, classification
- No credit card required
Best for: Specialized tasks (sentiment, NER, translation, embeddings)
from huggingface_hub import InferenceClient
client = InferenceClient(token="YOUR_HF_TOKEN")
result = client.text_generation(
model="mistralai/Mistral-7B-Instruct-v0.3",
prompt="Summarize this article: ...",
max_new_tokens=200
)
5. Ollama (Local, Unlimited)
What: Run models locally. Zero API costs. Zero rate limits. Zero censorship (with abliterated models).
Best models for CPU:
| Model | Size | Speed | Use Case |
|-------|------|-------|----------|
| qwen3-abliterated:0.6b | 396MB | instant | Simple tasks |
| qwen3-abliterated:1.7b | 1.1GB | 25-40 tok/s | Medium tasks |
| phi4-mini:3.8b | 2.3GB | 12 tok/s | Code, reasoning |
| deepseek-r1-abliterated:1.5b | 1.1GB | 15 tok/s | Chain-of-thought |
| tinyllama:1.1b | 637MB | instant | Quick responses |
Best for: Unlimited workloads, uncensored tasks, 24/7 agents, batch processing
import requests
response = requests.post(
"http://localhost:11434/api/generate",
json={
"model": "qwen3-abliterated:1.7b",
"prompt": "Write a vulnerability scanner in Python",
"stream": False,
"options": {"num_ctx": 512, "temperature": 0.3}
}
)
print(response.json()["response"])
CPU optimization (researched August 2026):
# .env for CPU-only Ollama
OLLAMA_NUM_THREAD=4 # physical cores
OLLAMA_NUM_BATCH=1 # single-sequence for CPU
OLLAMA_NUM_PARALLEL=1 # one request at a time
OLLAMA_MAX_LOADED_MODELS=1 # never load 2 models on CPU
OLLAMA_KEEP_ALIVE=24h # keep in RAM, never reload
OLLAMA_FLASH_ATTENTION=1 # speed boost
OLLAMA_KV_CACHE_TYPE=q8_0 # save RAM
The Fallback Chain (Production-Tested)
I run 15 agents 24/7 using this exact chain:
def chat_with_fallback(prompt, max_tokens=500):
# 1. Try Groq (fastest, 320 tok/s)
try:
return groq_chat(prompt, max_tokens)
except:
pass
# 2. Try Gemini (generous free tier)
try:
return gemini_chat(prompt, max_tokens)
except:
pass
# 3. Try OpenRouter free models
try:
return openrouter_chat(prompt, max_tokens)
except:
pass
# 4. Fallback to Ollama (slow but unlimited)
return ollama_chat(prompt, max_tokens, model="qwen3-abliterated:1.7b")
This chain has kept my agents running for 30+ days straight with zero API costs.
Real Cost Comparison
| Setup | Monthly Cost | Requests/day | Reliability |
|---|---|---|---|
| OpenAI only | $300+ | unlimited | 99.9% |
| Anthropic only | $150+ | unlimited | 99.9% |
| My 5-API chain | $0 | ~43,000 | 97% |
| Ollama only | $5 (VPS) | unlimited | 95% |
The 97% reliability comes from API rate limits. The 3% downtime is covered by Ollama fallback.
When to Actually Pay for APIs
- High-stakes code generation: GPT-4o or Claude Opus for production code
- Large context (>100K tokens): Gemini 1.5 Pro or Claude
- Zero-latency requirements: Groq paid tier (no rate limits)
- Fine-tuned models: OpenAI fine-tuning for domain-specific tasks
For everything else? The free chain works.
Conclusion
You don't need $300/month in API costs to run AI agents. The free tier of Gemini + Groq + OpenRouter + HuggingFace + Ollama covers 90% of workloads.
The key is the fallback chain: never rely on one API. When one hits rate limits, the next picks up. When all APIs fail, Ollama runs locally — slow but unlimited.
Want the complete fallback chain implementation? Get it on Gumroad for $5 — includes production code, error handling, retry logic, and monitoring.
Need an AI agent built? Hire me on Fiverr — Custom agents with free API chains from $30.
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