# Awesome LLMOps - Deep Dive

> **TL;DR**: Awesome-LLMOps is the definitive curated list of tools for operationalizing Large Language Models (5,500+ stars). It covers the entire LLM lifecycle: model selection, serving, fine-tuning, vector search, monitoring, security, and deployment. If you're building production LLM applications, this is your go-to resource for discovering the right tools. Created by TensorChord, the team behind pgvecto.rs.

---

## What Is LLMOps?

**LLMOps** (Large Language Model Operations) is the specialized discipline for deploying, managing, and maintaining LLMs in production. It's the evolution of MLOps specifically adapted for the unique challenges of language models.

### Why LLMOps Is Different from MLOps

```
┌─────────────────────────────────────────────────────────────────────┐
│                    MLOPS vs LLMOPS                                   │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│   MLOPS                              LLMOPS                         │
│   ├── Training costs dominate        ├── Inference costs dominate   │
│   ├── Build models from scratch      ├── Fine-tune foundation models│
│   ├── Accuracy/F1 metrics            ├── Output quality + UX        │
│   ├── Batch processing               ├── Real-time generation       │
│   ├── Standard infrastructure        ├── Massive GPU requirements   │
│   └── Periodic retraining            └── Continuous evaluation      │
│                                                                     │
│   UNIQUE LLMOPS CONCERNS                                            │
│   ├── Prompt engineering & management                               │
│   ├── Context retrieval (RAG)                                       │
│   ├── LLM chaining and composition                                  │
│   ├── Hallucination detection                                       │
│   ├── Token cost optimization                                       │
│   └── Safety and content moderation                                 │
│                                                                     │
└─────────────────────────────────────────────────────────────────────┘
```

---

## Quick Stats

| Metric | Value |
|--------|-------|
| **GitHub Stars** | 5,500+ |
| **Forks** | 546 |
| **License** | CC0-1.0 (Public Domain) |
| **Created** | April 2022 |
| **Creator** | TensorChord |
| **Tool Categories** | 13 |

---

## The Categories

Awesome-LLMOps organizes tools into 13 major categories covering the complete LLM lifecycle:

```
┌─────────────────────────────────────────────────────────────────────┐
│                    LLMOPS TOOL CATEGORIES                            │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│   1. MODEL              Foundation models (LLMs, Vision, Audio)     │
│   2. SERVING            Inference engines and deployment            │
│   3. SECURITY           Safety, observability, monitoring           │
│   4. LLMOPS             Core operational platforms                  │
│   5. SEARCH             Vector databases and retrieval              │
│   6. CODE AI            AI-powered development tools                │
│   7. TRAINING           Fine-tuning, frameworks, tracking           │
│   8. DATA               Storage, versioning, feature engineering    │
│   9. DEPLOYMENT         ML platforms, workflows, scheduling         │
│  10. PERFORMANCE        Compilers, profiling, optimization          │
│  11. AUTOML             Automated machine learning                  │
│  12. OPTIMIZATIONS      Model compression, quantization             │
│  13. FEDERATED ML       Distributed learning                        │
│                                                                     │
└─────────────────────────────────────────────────────────────────────┘
```

---

## Key Tools by Category

### 🧠 Models

| Category | Notable Tools |
|----------|---------------|
| **LLMs** | Falcon, Mixtral, ChatGLM, Llama, StableLM |
| **Vision** | Stable Diffusion, Segment Anything (SAM), Midjourney |
| **Audio** | Whisper, Bark |

### ⚡ Serving & Inference

The most critical category for production:

| Tool | What It Does |
|------|--------------|
| **vLLM** | High-throughput, memory-efficient LLM serving |
| **text-generation-inference** | Hugging Face's production inference |
| **TensorRT-LLM** | NVIDIA GPU-optimized inference |
| **Ollama** | Simple local LLM serving |
| **llama.cpp** | C++ LLaMA implementation (runs anywhere) |
| **BentoML** | Unified model serving framework |
| **Triton Server** | Cloud and edge optimization |

### 🔗 LLMOps Platforms

| Tool | What It Does |
|------|--------------|
| **LangChain** | Composable LLM application building |
| **LlamaIndex** | Data integration for LLMs (RAG) |
| **Haystack** | Semantic search and QA systems |
| **Dify** | Visual LLM application development |
| **MLflow** | End-to-end ML lifecycle management |

### 🔍 Vector Search

| Tool | What It Does |
|------|--------------|
| **Milvus** | Scalable vector database |
| **Weaviate** | Vector search with filtering |
| **Qdrant** | Vector search engine |
| **Chroma** | Open-source embedding database |
| **Pinecone** | Managed vector database |
| **pgvector** | PostgreSQL vector extension |

### 🔒 Security & Observability

| Tool | What It Does |
|------|--------------|
| **Helicone** | Open-source LLM observability |
| **Langfuse** | LLM engineering platform with traces |
| **Evidently** | ML/LLM evaluation and monitoring |
| **Fiddler AI** | Hallucination detection, PII |

### 🔧 Training & Fine-tuning

| Tool | What It Does |
|------|--------------|
| **PEFT** | Parameter-Efficient Fine-Tuning |
| **QLoRA** | Memory-efficient quantized fine-tuning |
| **TRL** | Transformer Reinforcement Learning |
| **DeepSpeed** | Distributed training optimization |
| **Weights & Biases** | Experiment tracking |

### 💻 Code AI

| Tool | What It Does |
|------|--------------|
| **Continue** | Open-source AI coding assistant |
| **Tabby** | Self-hosted GitHub Copilot alternative |
| **GitHub Copilot** | AI code generation |

---

## The LLM Lifecycle

Here's how the tools map to each stage:

```
┌─────────────────────────────────────────────────────────────────────┐
│                    LLM PRODUCTION LIFECYCLE                          │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│   1. DATA PREPARATION                                               │
│      └── Hugging Face Datasets, DVC, Delta Lake, Feast              │
│                                                                     │
│   2. MODEL SELECTION / DEVELOPMENT                                  │
│      └── Falcon, Llama, Mixtral, ChatGLM                           │
│                                                                     │
│   3. FINE-TUNING                                                    │
│      └── PEFT, QLoRA, TRL, LMFlow, DeepSpeed                       │
│                                                                     │
│   4. EXPERIMENT TRACKING                                            │
│      └── Weights & Biases, MLflow, Comet ML, Neptune AI            │
│                                                                     │
│   5. SERVING & DEPLOYMENT                                           │
│      └── vLLM, TensorRT-LLM, Ollama, BentoML, Triton               │
│                                                                     │
│   6. VECTOR SEARCH (RAG)                                            │
│      └── Milvus, Weaviate, Qdrant, Chroma, pgvector                │
│                                                                     │
│   7. ORCHESTRATION                                                  │
│      └── LangChain, LlamaIndex, Haystack, Dify                     │
│                                                                     │
│   8. MONITORING & EVALUATION                                        │
│      └── Helicone, Langfuse, Evidently, promptfoo                  │
│                                                                     │
│   9. OPTIMIZATION                                                   │
│      └── Neural Compressor, ONNX, TVM                              │
│                                                                     │
└─────────────────────────────────────────────────────────────────────┘
```

---

## Most Popular Tools (by Stars)

Based on the repository's star tracking:

1. **vLLM** - Production inference at scale
2. **LangChain** - LLM application composition
3. **Whisper** - Speech recognition baseline
4. **Stable Diffusion** - Image generation
5. **llama.cpp** - Local inference
6. **text-generation-inference** - HuggingFace serving
7. **Ollama** - User-friendly local LLMs
8. **BentoML** - Unified serving
9. **Milvus** - Vector database
10. **Langfuse** - Observability

---

## Who Created This?

### TensorChord

TensorChord is a company focused on **cloud-native AI infrastructure**:

| Project | Stars | Description |
|---------|-------|-------------|
| **Awesome-LLMOps** | 5,500+ | This curated list |
| **pgvecto.rs** | 2,100+ | Postgres vector search (Rust) |
| **VectorChord** | 1,500+ | Scalable Postgres vector search |
| **envd** | 2,200+ | Reproducible dev environments |

Their focus on infrastructure tools gives them unique insight into the LLMOps ecosystem.

---

## How to Use This Resource

### For Tool Selection

```
1. Identify your stage in the LLM lifecycle
2. Find the relevant category
3. Compare tools by stars and features
4. Evaluate based on your requirements
```

### For Learning

```
1. Start with the LLMOps category for platforms
2. Explore Serving for deployment options
3. Study Security for production readiness
4. Review Search for RAG implementations
```

### For Architecture Planning

```
1. Map your pipeline stages to categories
2. Select tools for each stage
3. Consider integration patterns
4. Plan for observability from the start
```

---

## Recent Additions (December 2025)

| Category | New Tools |
|----------|-----------|
| **Observability** | Helicone, Langfuse, LangWatch, Maxim AI |
| **LLMOps** | PraisonAI, PromptSite, PromptDX |
| **Deployment** | KubeAI, Kaito, ParadeDB |
| **Evaluation** | TensorZero, ai-evaluation |

---

## Gaps & Opportunities

Areas with less coverage:

| Area | Status |
|------|--------|
| **Prompt Engineering** | Limited dedicated tools |
| **Ethical AI / Bias** | Sparse coverage |
| **Model Interpretability** | Few entries |
| **Cost Optimization** | Limited tools |
| **Regulatory Compliance** | Minimal coverage |
| **Edge Deployment** | Could be expanded |

---

## How This Connects to Our Other Deep Dives

| Deep Dive | Connection |
|-----------|------------|
| **MindsDB** | Listed in LLMOps platforms |
| **LLM Guard** | Security category |
| **NeMo Guardrails** | Security category |
| **Context Engineering** | The "why" behind LLMOps |
| **Awesome MCP Servers** | Complementary resource |
| **OpenHands** | Uses many of these tools |

---

## Key Links

| Resource | URL |
|----------|-----|
| GitHub Repository | https://github.com/tensorchord/Awesome-LLMOps |
| TensorChord | https://github.com/tensorchord |
| pgvecto.rs | https://github.com/tensorchord/pgvecto.rs |

---

## Bottom Line

Awesome-LLMOps is the **definitive roadmap for production LLM tooling**.

**Why it matters**:
- Curated by infrastructure experts
- Covers the complete lifecycle
- Star counts help compare popularity
- Regularly updated

**The key insight**: LLMOps isn't just MLOps with bigger models. It requires specialized tools for inference costs, real-time generation, hallucination detection, and prompt management. This repository maps that landscape.

For our repository focused on AI-assisted development, Awesome-LLMOps is the meta-resource - it's where you go to find the individual tools we've been exploring.

---

*Document created: January 2026*
*Sources: [GitHub - tensorchord/Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps), [TensorChord](https://github.com/tensorchord), [LLMOps vs MLOps - TrueFoundry](https://www.truefoundry.com/blog/llmops-vs-mlops)*
