LLMOps 2026: Running AI Applications in Production

SynfraCore·March 2026·10 min read

What is LLMOps?

LLMOps is MLOps for Large Language Models — practices, tools, and processes for deploying and maintaining LLM-powered applications at production scale.

The Stack in 2026

•Application framework: LangChain / LlamaIndex

•Tracing: LangSmith, Phoenix/Arize

•Inference: Anthropic/OpenAI APIs, or vLLM/Ollama for self-hosted

•Vector stores: Chroma, Qdrant, pgvector

•Caching: Redis for response caching

•Monitoring: Grafana for cost/latency dashboards

Cost Monitoring

python

response = client.messages.create(
    model='claude-haiku-4-5',  # cheaper for simple tasks
    max_tokens=500,
    messages=[{'role': 'user', 'content': prompt}]
)
cost = (response.usage.input_tokens * 0.0000008) + \
       (response.usage.output_tokens * 0.000004)
print(f'Cost: ${cost:.6f}')
# Cache identical responses in Redis — major cost reduction

Prompt Version Control and Evaluation

Prompts are code. Store in Git or LangSmith. Test before deploying — use Claude-as-judge automated evaluation. Track accuracy on a test dataset before every prompt change. You cannot improve what you cannot measure. See LLMOps Academy.

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