What is LLMOps?
LLMOps applies the rigorous standards and processes of software development (known as DevOps discipline) to AI systems. It covers deployment, evaluation, monitoring, and governance so assistants and agents stay fast, accurate, and compliant as they grow.
How does it work?
Define performance targets (like how fast and reliable the AI must be, known as SLOs), monitor response time, and resource usage. Evaluate changes before release, and keep prompts/context under version control. Cost and access policies keep usage on track.
When does it matter? (Examples)
- Usage is growing and you need predictable cost and performance.
- Multiple teams ship prompts, tools, and data changes in parallel.
- You must show governance and auditability to leadership.
Benefits
- Stabilizes performance
- Controls spend
- Improves release safety
Risks
- Shadow prompts and silent changes
- Noisy logs without clear metrics
- Lack of approval gates
Antire and LLMOps
We set up observability, evaluations, versioning and approvals so AI remains reliable at scale and aligned with your policies.
Services
Data platforms and applied AI
Related words
LLM evals (evaluation)
Tokenization
Large Language Model (LLM)