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Last Updated: October, 14 2025
The story of how a late-night debugging session and a YouTube video transformed our multi-agent architecture
The Hard Truth We Learned: Not all AI tasks need superintelligent models. Our 8B model handles data collection identically to our 405B model—but costs 75x less.
Let me take you back to a Tuesday afternoon when everything was “working fine.” We had just finished building our SEO multi-agent system—four specialized agents working together to analyze websites and generate recommendations:
The system worked. Tests passed. Client demo went great. We shipped it.
Then we looked at the bill.
Our first month’s LLM costs came in. We were processing about 200 analysis jobs per day. The math hit us like a truck:
200 jobs/day × $1.20/job = $240/day
$240/day × 30 days = $7,200/monthFor one client. We had three more in the pipeline.
The worst part? We were using Meta-Llama-3.1-405B (the biggest, most expensive model) for everything:
Gap Analysis (comparing keywords): 405B model → $1.50/1M tokens ✓ Makes sense
Recommendations (creative writing): 405B model → $1.50/1M tokens ✓ Needs creativity
Data Collection (calling APIs): 405B model → $1.50/1M tokens ❌ TOTAL OVERKILL
Content Scraping (parsing HTML): 405B model → $1.50/1M tokens ❌ TOTAL OVERKILLOur data collection agent was literally just orchestrating API calls and extracting JSON. It needed about as much “intelligence” as a well-written bash script. But we were using the same nuclear-powered model we used for creative SEO recommendations.
It’s like hiring a brain surgeon to sort your mail. Sure, they can do it. But… why?
But the real pain started at 2:17 AM on a Wednesday.
I got the Slack notification we all dread: “⚠️ PROD ALERT: Nebius API rate limit exceeded”
Our primary LLM provider (Nebius) had hit some undocumented rate limit. Our entire analysis pipeline was down. Three clients were going to wake up to failed jobs.
“No problem,” I thought. “We have SambaNova as backup. I’ll just switch providers.”
I should have known better.
Switching from Nebius to SambaNova should have been simple. It wasn’t. Here’s what it actually required:
Step 1: Find all the .env files (there were 3 in different services)
# Main service .env
LLM_MODEL_INTENT=Qwen3-32B # Wait, is this the right format?
LLM_MODEL_METADATA=Qwen3-32B # Or was it Qwen/Qwen3-32B-fast?
LLM_MODEL_ANSWER_SIMPLE=Meta-Llama-3.1-8B-Instruct # Different format entirely??Step 2: Update the model name mapping function (because Nebius uses “Qwen/Qwen3-32B-fast” but SambaNova uses “Qwen3-32B”)
def map_model_to_metadata_enum(model_name: str) -> str:
"""I'm not even sure what this does anymore"""
model_lower = model_name.lower()
if "qwen3-32b" in model_lower or "qwen/qwen3-32b-fast" in model_lower:
return "32B-fast" # Is this right? I'm too tired to remember.
elif "480b" in model_lower:
return "480B"
# ... 20 more lines of increasingly desperate string matchingStep 3: Kill all the services (because they cache the .env at startup)
lsof -ti:8074 | xargs kill -9 # Answer Generation
lsof -ti:8075 | xargs kill -9 # Intent Detection
lsof -ti:8076 | xargs kill -9 # Compression
lsof -ti:8062 | xargs kill -9 # Metadata
lsof -ti:8065 | xargs kill -9 # LLM GatewayStep 4: Start everything back up (and pray I didn’t typo anything)
Step 5: Test and debug (because I definitely typo’d something)
ERROR: Model name 'Qwen3-32B' not recognized by metadata service
Expected: '32B-fast'🤬
Step 6: Fix the typo, restart everything AGAIN
Total time: 28 minutes. At 2:45 AM. While clients’ jobs were failing.
By the time I got it working, I was furious. Not at the incident—those happen. But at our own system fighting me when I was trying to fix things.
I wrote this in our engineering notes at 3 AM:
“The shared model registry was supposed to make configuration simple. Instead, it’s a 15-30 minute nightmare requiring edits across 5+ files, manual string matching, and multiple service restarts. This is unacceptable.”
The next morning, my teammate added:
“Why does changing models take so long? We created this shared service to be easy. It’s very complex.”
He was absolutely right.
Still wired from the production incident, I couldn’t sleep. I started researching better ways to manage multi-agent LLM systems.
That’s when I found it: a YouTube video titled “Effective Context Engineering for AI Agents” featuring engineers from Anthropic and SambaNova[^1].
I almost skipped it. The title sounded like yet another generic AI tutorial. But something made me click.
About 12 minutes in, the SambaNova engineer said something that made me sit up straight:
“Use small models for simple tasks—they’re 40 to 50 times cheaper. Most developers don’t realize this. They use the same large model for everything because it’s easier. But with agents, you’re making 10 to 20 times more LLM calls than simple chat. Token economics matter at scale.”
40 to 50 times cheaper.
I paused the video. Pulled up our costs spreadsheet. Did the math.
Our data collection agent: Using a 405B model ($1.50/1M tokens) to orchestrate API calls. A task an 8B model ($0.02/1M tokens) could handle identically.
$1.50 / $0.02 = 75x more expensive than necessary
We weren’t just inefficient. We were burning money on a scale that would make a CFO weep.
The video continued:
“Think about task complexity. Classification? 8B model. Structured data extraction? Maybe 32B. Creative writing? That’s when you break out the 405B. Match the model to the task, not the other way around.”
I grabbed my notebook and started frantically writing down the mental model:
Low Complexity (8B models):
Medium Complexity (32B models):
High Complexity (405B models):
I looked at our four agents through this new lens:
Data Collection Agent:
Current: 405B model ($1.50/1M tokens)
Actually needs: "Call SerpAPI, parse JSON, extract URLs"
Complexity: LOW
Should use: 8B model ($0.02/1M tokens)
Cost reduction: 75x cheaper 🤯
Content Scraper Agent:
Current: 405B model ($1.50/1M tokens)
Actually needs: "Parse HTML, extract headings/text, count words"
Complexity: LOW
Should use: 8B model ($0.02/1M tokens)
Cost reduction: 75x cheaper 🤯
Gap Analysis Agent:
Current: 405B model ($1.50/1M tokens)
Actually needs: "Compare keyword lists, find missing terms, calculate coverage"
Complexity: MEDIUM (structured, but not creative)
Should use: 32B model ($0.08/1M tokens)
Cost reduction: 19x cheaper 🤔
Recommendation Generator:
Current: 405B model ($1.50/1M tokens)
Actually needs: "Write creative, detailed SEO recommendations with reasoning"
Complexity: HIGH (creative + nuanced judgment)
Should use: LARGE model (keep it)
Cost reduction: None, but appropriate! ✓I did the math on the cost savings:
BEFORE (all 405B):
Data Collection: $0.075
Content Scraping: $0.150
Gap Analysis: $0.300
Recommendations: $0.750
──────────────────────────
Total per job: $1.275
AFTER (task-optimized):
Data Collection: $0.001 (8B model)
Content Scraping: $0.002 (8B model)
Gap Analysis: $0.016 (32B model)
Recommendations: $0.250 (large model, but cheaper provider)
──────────────────────────
Total per job: $0.069
Savings: $1.206 per job (93% reduction)
At 200 jobs/day: $241/day savings = $7,230/month
We were burning $7,000/month because we didn’t match models to task complexity.
At 4 AM, I sent a message to our team Slack:
“I found a YouTube video that just explained why our LLM costs are insane. We need to rebuild how we select models. I’m starting on it tomorrow.”
Someone replied at 4:03 AM (our DevOps guy, apparently also couldn’t sleep):
“Is this about that 2 AM incident? Because I’m still angry about how hard it was to switch providers.”
Me: “That too. This fixes both problems.”
We designed a clean, maintainable system with these principles:
# app/core/model_registry.py
@dataclass
class ModelInfo:
"""Complete model metadata - eliminates need for string matching"""
canonical: str # Our internal name: "llama-8b"
provider: str # "sambanova", "nebius"
api_name: str # Provider-specific: "Meta-Llama-3.1-8B-Instruct"
size: ModelSize # tiny, small, medium, large
cost_per_1m_tokens: float # $0.02, $0.50, etc.
speed_tokens_per_sec: int # Performance metric
context_window: int # 32k, 128k, etc.We configured 11 models across two providers:
SambaNova Models (Fast Model Swapping):
{
# Tiny Models (8B) - Simple Tasks
"sambanova-llama-8b": ModelInfo(
canonical="llama-8b",
provider="sambanova",
api_name="Meta-Llama-3.1-8B-Instruct",
size=ModelSize.TINY,
cost_per_1m_tokens=0.02,
speed_tokens_per_sec=150,
context_window=128000
),
# Small Models (17-32B) - Structured Analysis
"sambanova-qwen-32b": ModelInfo(
canonical="qwen-32b",
provider="sambanova",
api_name="Qwen3-32B",
size=ModelSize.SMALL,
cost_per_1m_tokens=0.08,
speed_tokens_per_sec=100,
context_window=32768
),
"sambanova-llama-17b": ModelInfo(
canonical="llama-17b",
provider="sambanova",
api_name="Llama-4-Maverick-17B-128E-Instruct",
size=ModelSize.SMALL,
cost_per_1m_tokens=0.05,
speed_tokens_per_sec=120,
context_window=128000
),
# Medium Models (70B) - Complex Reasoning
"sambanova-llama-70b": ModelInfo(
canonical="llama-70b",
provider="sambanova",
api_name="Meta-Llama-3.3-70B-Instruct",
size=ModelSize.MEDIUM,
cost_per_1m_tokens=0.20,
speed_tokens_per_sec=80,
context_window=128000
),
"sambanova-deepseek-70b": ModelInfo(
canonical="deepseek-70b",
provider="sambanova",
api_name="DeepSeek-R1-Distill-Llama-70B",
size=ModelSize.MEDIUM,
cost_per_1m_tokens=0.25,
speed_tokens_per_sec=75,
context_window=64000
),
# Large Models (120B+) - Creative Generation
"sambanova-gpt-120b": ModelInfo(
canonical="gpt-120b",
provider="sambanova",
api_name="gpt-oss-120b",
size=ModelSize.LARGE,
cost_per_1m_tokens=0.40,
speed_tokens_per_sec=60,
context_window=32768
),
"sambanova-deepseek-v3": ModelInfo(
canonical="deepseek-v3",
provider="sambanova",
api_name="DeepSeek-V3.1",
size=ModelSize.LARGE,
cost_per_1m_tokens=0.50,
speed_tokens_per_sec=50,
context_window=64000
),
}Nebius Models (No Rate Limits):
{
"nebius-llama-8b": ModelInfo(
canonical="llama-8b",
provider="nebius",
api_name="meta-llama/Meta-Llama-3.1-8B-Instruct",
size=ModelSize.TINY,
cost_per_1m_tokens=0.03,
speed_tokens_per_sec=140,
context_window=128000
),
"nebius-llama-405b": ModelInfo(
canonical="llama-405b",
provider="nebius",
api_name="meta-llama/Meta-Llama-3.1-405B-Instruct",
size=ModelSize.LARGE,
cost_per_1m_tokens=1.50,
speed_tokens_per_sec=40,
context_window=128000
),
"nebius-deepseek-chat-v3": ModelInfo(
canonical="deepseek-chat-v3",
provider="nebius",
api_name="deepseek/deepseek-chat-v3",
size=ModelSize.LARGE,
cost_per_1m_tokens=0.60,
speed_tokens_per_sec=45,
context_window=64000
),
"nebius-deepseek-r1": ModelInfo(
canonical="deepseek-r1",
provider="nebius",
api_name="deepseek/deepseek-r1",
size=ModelSize.LARGE,
cost_per_1m_tokens=0.80,
speed_tokens_per_sec=35,
context_window=64000
),
}class TaskType(str, Enum):
"""Agent task types requiring different model capabilities"""
DATA_COLLECTION = "data_collection"
CONTENT_SCRAPING = "content_scraping"
GAP_ANALYSIS = "gap_analysis"
RECOMMENDATIONS = "recommendations"
CLASSIFICATION = "classification"
STRUCTURED_EXTRACTION = "structured_extraction"
@dataclass
class TaskModelConfig:
"""Model configuration for a specific task"""
preferred_size: ModelSize
temperature: float
max_tokens: int
description: str
TASK_CONFIGS = {
TaskType.DATA_COLLECTION: TaskModelConfig(
preferred_size=ModelSize.TINY,
temperature=0.1,
max_tokens=1024,
description="Simple API calls, minimal LLM reasoning"
),
TaskType.GAP_ANALYSIS: TaskModelConfig(
preferred_size=ModelSize.SMALL,
temperature=0.3,
max_tokens=4096,
description="Structured keyword/topic/semantic analysis"
),
TaskType.RECOMMENDATIONS: TaskModelConfig(
preferred_size=ModelSize.LARGE,
temperature=0.5,
max_tokens=8192,
description="Creative, actionable SEO recommendations"
),
}class ProviderPreset:
"""Predefined provider configurations for easy switching"""
SAMBANOVA_OPTIMIZED = {
TaskType.DATA_COLLECTION: "sambanova-llama-8b",
TaskType.CONTENT_SCRAPING: "sambanova-llama-8b",
TaskType.GAP_ANALYSIS: "sambanova-qwen-32b",
TaskType.RECOMMENDATIONS: "sambanova-deepseek-v3",
TaskType.CLASSIFICATION: "sambanova-llama-8b",
TaskType.STRUCTURED_EXTRACTION: "sambanova-llama-17b",
}
NEBIUS_BALANCED = {
TaskType.DATA_COLLECTION: "nebius-llama-8b",
TaskType.CONTENT_SCRAPING: "nebius-llama-8b",
TaskType.GAP_ANALYSIS: "nebius-llama-8b",
TaskType.RECOMMENDATIONS: "nebius-llama-405b",
TaskType.CLASSIFICATION: "nebius-llama-8b",
TaskType.STRUCTURED_EXTRACTION: "nebius-llama-8b",
}
MIXED_OPTIMAL = {
TaskType.DATA_COLLECTION: "sambanova-llama-8b", # Fast
TaskType.CONTENT_SCRAPING: "sambanova-llama-8b", # Fast
TaskType.GAP_ANALYSIS: "sambanova-qwen-32b", # Best for structured
TaskType.RECOMMENDATIONS: "nebius-llama-405b", # No rate limits
TaskType.CLASSIFICATION: "sambanova-llama-8b", # Fast
TaskType.STRUCTURED_EXTRACTION: "sambanova-llama-17b", # Balance
}
# ═══════════════════════════════════════════════════════════════
# 🎯 CHANGE ONLY THIS LINE TO SWITCH PROVIDER STRATEGY
# ═══════════════════════════════════════════════════════════════
ACTIVE_PRESET = ProviderPreset.SAMBANOVA_OPTIMIZED
# ═══════════════════════════════════════════════════════════════That’s it. One line to switch between:
class RecommendationGeneratorAgent(BaseAgent):
def __init__(self, db_session: AsyncSession):
super().__init__("RecommendationGeneratorAgent")
# Using generic "analysis LLM" - unclear what model this is
self.llm = get_analysis_llm()
self.db_session = db_sessionProblems:
from app.core.llm import get_llm_for_task
from app.core.model_registry import TaskType
class RecommendationGeneratorAgent(BaseAgent):
def __init__(self, db_session: AsyncSession):
super().__init__("RecommendationGeneratorAgent")
# Automatically gets optimal model for recommendations
self.llm = get_llm_for_task(TaskType.RECOMMENDATIONS)
self.db_session = db_sessionBenefits:
# app/core/llm.py
class LLMProvider:
@staticmethod
def get_llm_for_task(task: TaskType) -> BaseChatModel:
"""
Get LLM optimized for specific task type
Uses model registry to automatically select optimal model
based on task complexity, cost, and performance requirements.
"""
config = get_llm_config_for_agent(task)
return LLMProvider.get_llm(
provider=config["provider"],
model_name=config["model_name"],
temperature=config["temperature"],
max_tokens=config["max_tokens"]
)
# Simple usage in any agent:
gap_analysis_llm = LLMProvider.get_llm_for_task(TaskType.GAP_ANALYSIS)
recommendation_llm = LLMProvider.get_llm_for_task(TaskType.RECOMMENDATIONS)Before (All Large Models):
Data Collection: 405B model × 50k tokens = $0.075
Content Scraping: 405B model × 100k tokens = $0.150
Gap Analysis: 405B model × 200k tokens = $0.300
Recommendations: 405B model × 500k tokens = $0.750
────────────────────────────────────────────────────
Total per job: $1.275After (Task-Optimized Models):
Data Collection: 8B model × 50k tokens = $0.001 (75x cheaper)
Content Scraping: 8B model × 100k tokens = $0.002 (75x cheaper)
Gap Analysis: 32B model × 200k tokens = $0.016 (19x cheaper)
Recommendations: Large model × 500k tokens = $0.250 (3x cheaper)
────────────────────────────────────────────────────
Total per job: $0.069 (93% reduction!)| Metric | Before | After | Improvement |
|---|---|---|---|
| Provider switch time | 15-30 min | 5 seconds | 180-360x faster |
| Files to edit | 5+ files | 1 line | 5x simpler |
| Mapping code lines | 200+ lines | 0 lines | Eliminated |
| Service restarts | 5 services | 0 (hot reload) | Instant |
| Model name formats | 3 formats | 1 canonical | Unified |
| Cost visibility | Manual calculation | Built-in | Automatic |
We tested the system with our SEO analysis workflow (target: https://www.spaceo.ai/):
Gap Analysis Agent (Qwen3-32B, 32B parameters):
Recommendation Generator Agent (DeepSeek-V3.1, Large model):
✅ Generated 8 detailed SEO recommendations
✅ Each recommendation includes:
– Category, priority, title
– Detailed description and rationale
– Effort and impact estimates
– 3-5 implementation steps
✅ Response time: ~9 seconds
✅ Cost: $0.250
✅ Quality: High-quality, creative, actionable (requires large model)
Example Generated Recommendation:
{
"title": "Expand content to meet competitor word count",
"category": "content",
"priority": "high",
"description": "Increase the content length by at least 656 words to match competitor depth and provide comprehensive coverage of AI development services.",
"rationale": "Longer content typically ranks better as it covers topics more thoroughly and satisfies user intent, improving dwell time and reducing bounce rates.",
"effort": "medium",
"impact": "high",
"implementation_steps": [
"Conduct research on competitor content to identify key sections they cover",
"Add detailed sections such as 'Types of AI Development Services'",
"Incorporate statistics, examples, and use cases to add depth",
"Ensure new content flows naturally and maintains readability"
]
}The biggest misconception: “Better model = better results for all tasks.”
Reality:
Using a 405B model for data collection is like hiring a PhD to sort your mail.
We initially treated model configuration as a “detail to figure out later.” Bad idea.
Poor configuration systems create:
Lesson: Design configuration architecture FIRST. It’s not boilerplate—it’s infrastructure.
Different providers use different model naming:
Qwen3-32BQwen/Qwen3-32B-fastclaude-3-5-sonnet-20241022Without abstraction, you end up with string matching hell:
# DON'T DO THIS 😱
if "qwen3-32b" in model_name.lower() or "qwen/qwen3-32b" in model_name.lower():
return "32B-fast"Solution: ModelInfo dataclass with all naming formats:
ModelInfo(
canonical="qwen-32b", # Our name
api_name="Qwen3-32B", # Provider's name
provider="sambanova" # Where it comes from
)When costs are hidden in .env files and manual calculations, nobody optimizes them.
Making costs visible in the registry:
ModelInfo(
canonical="llama-8b",
cost_per_1m_tokens=0.02, # ← RIGHT HERE
)Led to conversations like:
“Wait, we’re using a $1.50/1M model for data collection? Let’s use the $0.02 model instead.”
Estimated savings: $20k+/year on our projected volume.
The best developer experience:
# Switch from SambaNova to Nebius:
ACTIVE_PRESET = ProviderPreset.NEBIUS_BALANCED # Changed one word
# Test with fast models:
ACTIVE_PRESET = ProviderPreset.DEV_FAST # Changed one word
# Custom mix:
ACTIVE_PRESET = ProviderPreset.MIXED_OPTIMAL # Changed one wordNo .env edits. No service restarts. No string matching. Just code.
On application startup, the registry validates configuration:
def validate_registry_on_startup() -> None:
"""Validate registry configuration at application startup"""
logger.info("Validating model registry configuration...")
# Validate active preset
is_valid, error = ModelRegistry.validate_preset(ACTIVE_PRESET)
if not is_valid:
raise ValueError(f"Invalid active preset: {error}")
# Log current configuration
preset_info = ModelRegistry.get_current_preset_info()
logger.info(f"Active preset info: {preset_info}")
logger.info(f"Providers used: {preset_info['providers_used']}")
logger.info(f"Estimated cost per job: ${preset_info['estimated_cost_per_job']}")
logger.info("✅ Model registry validation passed")Output:
Validating model registry configuration...
Active preset: SAMBANOVA_OPTIMIZED
Providers used: ['sambanova']
Estimated cost per job: $0.069
✅ Model registry validation passedIf configuration is invalid (missing model, invalid task), application fails fast at startup.
When an agent requests a model:
# Agent code:
llm = get_llm_for_task(TaskType.RECOMMENDATIONS)
# What happens:
# 1. Look up ACTIVE_PRESET[TaskType.RECOMMENDATIONS]
# → Returns: "sambanova-deepseek-v3"
#
# 2. Look up MODELS["sambanova-deepseek-v3"]
# → Returns: ModelInfo(
# api_name="DeepSeek-V3.1",
# provider="sambanova",
# temperature=0.5,
# max_tokens=8192
# )
#
# 3. Initialize LLM with provider-specific client:
# → ChatOpenAI(
# model="DeepSeek-V3.1",
# base_url="https://api.sambanova.ai/v1",
# api_key=SAMBANOVA_API_KEY
# )No string matching. No conditionals. Just lookups.
Agents don’t know (or care) about providers:
class GapAnalyzerAgent(BaseAgent):
def __init__(self, db_session: AsyncSession):
super().__init__("GapAnalyzerAgent")
# This agent needs structured analysis capabilities
self.llm = get_llm_for_task(TaskType.GAP_ANALYSIS)Change ACTIVE_PRESET from SAMBANOVA_OPTIMIZED to NEBIUS_BALANCED:
Every model has cost metadata:
def get_current_preset_info() -> Dict[str, Any]:
"""Get information about currently active preset"""
preset_models = {}
total_cost_estimate = 0.0
for task, model_key in ACTIVE_PRESET.items():
model_info = MODELS[model_key]
preset_models[task.value] = {
"model": model_info.display_name,
"cost_per_1m": model_info.cost_per_1m_tokens
}
# Rough estimate: assume 100k tokens per task
total_cost_estimate += model_info.cost_per_1m_tokens * 0.1
return {
"preset": "ACTIVE_PRESET",
"models": preset_models,
"estimated_cost_per_job": round(total_cost_estimate, 4)
}Output:
{
"preset": "SAMBANOVA_OPTIMIZED",
"estimated_cost_per_job": 0.069,
"models": {
"data_collection": {
"model": "llama-8b (sambanova)",
"cost_per_1m": 0.02
},
"gap_analysis": {
"model": "qwen-32b (sambanova)",
"cost_per_1m": 0.08
},
"recommendations": {
"model": "deepseek-v3 (sambanova)",
"cost_per_1m": 0.50
}
}
}Before configuring models, map your tasks to complexity levels:
LOW_COMPLEXITY = [
"data_collection", # API calls, basic logic
"content_scraping", # Parsing, extraction
"classification" # Simple decisions
]
MEDIUM_COMPLEXITY = [
"gap_analysis", # Structured comparison
"structured_extraction" # Complex parsing
]
HIGH_COMPLEXITY = [
"recommendations", # Creative writing
"strategy_generation" # Complex reasoning
]Rule of thumb:
Don’t assume model sizes. Test them:
# Test Gap Analysis with different models: - 8B model: 3.2s, $0.001, Quality: 8/10 - 32B model: 3.5s, $0.016, Quality: 9/10 ← SWEET SPOT - 405B model: 8.1s, $0.300, Quality: 9/10 ← OVERKILLFor structured analysis, 32B was 19x cheaper than 405B with virtually identical quality.
Make costs impossible to ignore:
logger.info(f"Estimated cost per job: ${preset_info['estimated_cost_per_job']}")
logger.info(f"Gap Analysis: {gap_model.cost_per_1m_tokens}/1M tokens")
logger.info(f"Recommendations: {rec_model.cost_per_1m_tokens}/1M tokens")Seeing “$0.069 per job” vs “$1.20 per job” drives optimization.
Avoid configuration that requires:
Good:
ACTIVE_PRESET = ProviderPreset.SAMBANOVA_OPTIMIZED # One line in codeBad:
LLM_PROVIDER=sambanova # Requires restartSambaNova has rate limits. Nebius doesn’t. Design for fallback:
MIXED_OPTIMAL = {
TaskType.GAP_ANALYSIS: "sambanova-qwen-32b", # Fast, use SambaNova
TaskType.RECOMMENDATIONS: "nebius-llama-405b", # Heavy, use Nebius
}Use fast provider for high-frequency tasks. Use no-rate-limit provider for heavy tasks.
Building multi-agent AI systems requires more than just connecting to LLM APIs. The infrastructure around model selection, configuration, and cost optimization is just as important as the agents themselves.
The biggest win isn’t the cost savings—it’s the developer experience.
That shift in mindset—from “configuration is scary” to “let’s experiment”—is what makes systems truly production-ready.
Complete implementation:
app/core/model_registry.py (489 lines)app/core/llm.pytest_model_registry.pyModel Inventory:
If you’re just getting started with AI agent development, check out our comprehensive guide covering architecture patterns, framework selection, and best practices for building production-ready multi-agent systems.
Engineering Discussion:
[^1]: “Effective Context Engineering for AI Agents” – SambaNova Engineering Discussion
https://www.youtube.com/watch?v=WfcLpKjY0gM
Want to build cost-optimized multi-agent AI systems? Learn from our mistakes. We spent 15-30 minutes switching providers before. Now it takes 5 seconds. The difference? Clean architecture from day one.
Contact us at SpaceO Technologies for AI development services that combine cutting-edge models with production-ready infrastructure.
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