🚀 LLMOps is no longer a luxury — it's the backbone of modern AI workflows.
If you're building anything with GPT, Claude, or LLaMA in 2025, you need tooling that scales. From prompt orchestration to observability and deployment, this space is growing fast — and messy.
In this post, you'll get a curated shortlist of the top LLMOps platforms, based on real-world use cases and practical criteria.
✅ What to Look for in an LLMOps Platform
- Prompt versioning and testing support
- Observability (latency, drift, hallucinations, cost tracking)
- Seamless deployment workflows (GPU support, serverless, containerization)
- Integration with OpenAI, Hugging Face, Anthropic
- Enterprise readiness (compliance, auditing, scalability)
🧰 Top LLMOps Tools (Shortlist)
LangChain — Best for chaining workflows and agent logic
W&B — Best for experiment tracking and observability
LlamaIndex — Ideal for RAG and external data integration
Arize AI — LLM observability with hallucination detection
Fiddler AI — Focused on fairness, bias, and explainability
PromptLayer — GitHub-style versioning and A/B testing for prompts
BentoML — Production-ready model deployment (GPU + API support)
📊 Bonus: Download the LLMOps Toolkit (PDF)
🧠 Final Thoughts
The best LLMs still need great infrastructure. Whether you’re documenting internal copilots or launching a production-grade chatbot, the tools you use will make or break scalability and performance.
Need help documenting or selecting your stack?
👉 Let’s work together
Originally published on learn-dev-tools.blog