Buyer’s guide · 2026
Best MLOps and Agent Ops Platforms in 2026
Written by
Amber Jain
Published
Reading time
3 min read
Getting a model or an AI agent into production is one problem; keeping it healthy there is another. Here is an honest look at the best MLOps and Agent Ops platforms in 2026, from experiment tracking to production model and agent operations.
Why 'MLOps' has split into two very different categories
For a few years 'MLOps' covered the whole model lifecycle: experimentation, training, deployment, monitoring, retraining. In 2026 that broad label is genuinely two categories with different vendors, different buyers and different technical concerns. Build-side MLOps (experiment tracking, feature stores, model registries, training infrastructure, packaging) is a data-science team problem, and the vendors that lead it are extensions of the data platform. Operations-side MLOps (drift detection, quality monitoring, cost and latency observability, and increasingly agent operations for LLM-based systems) is an operations team problem, and the vendors leading it come from observability. Confusing the two produces bad procurement decisions. The list below is organised around this split.
Five criteria for evaluating production model and agent ops
- Drift detection that catches the drift that matters. Statistical drift on inputs is easy; performance drift on outputs (against ground truth or a business metric) is what actually degrades a product.
- Cost and latency observability with per-request granularity. LLM costs and latency vary wildly per prompt; aggregate metrics miss the outliers that generate customer complaints.
- Agent trace fidelity. For LLM agents doing multi-step work, the trace of intermediate decisions is what makes the failure debuggable. Log-only visibility is not enough.
- Quality evaluation that keeps up with change. Prompt updates, model version bumps, tool additions all change agent behaviour; evaluation has to be automatic, not annual.
- Governed action when something degrades. Detection without response is a dashboard; production model ops needs the ability to fall back, roll back, or route around the degraded model or agent under policy.
The shortlist
This category splits into two jobs. Building and training, where the big platforms and experiment trackers lead, and operating models and agents in production, where drift, quality, cost and governed action matter. Most tools focus on the first; the list makes the split clear.
Opstral
Best for: Production model & agent opsModel and agent operations as one of ten pillars: live telemetry for models, agents, prompts and executions, drift and quality checks, cost and latency, and governed action when something degrades in production.
Explore the platform →Databricks
Best for: Data-and-ML togetherThe strongest all-around MLOps platform when data engineering and ML overlap, with native MLflow, Unity Catalog governance and serving on the lakehouse.
Weights & Biases
Best for: Experiment trackingThe leading experiment-tracking and AI developer platform, extending into LLMOps with Weave.
Amazon SageMaker
Best for: AWS end-to-endEnd-to-end build, train and deploy for classical ML and foundation models across the AWS ecosystem.
Google Vertex AI
Best for: Google Cloud ML and GenAIGoogle Cloud's unified ML and generative-AI platform, increasingly absorbing LLMOps features.
MLflow
Best for: Open-source standardThe open-source standard for experiment tracking, model registry and packaging.
Arize
Best for: ML & LLM observabilityProduction ML and LLM observability with drift, quality and RAG tracing (Phoenix, open-source).
LangSmith
Best for: LLM & agent tracingLLM and agent observability, prompt versioning and evaluation for LLM applications.
Frequently asked questions
What is Agent Ops?
Agent Ops is the operations discipline for AI agents in production: monitoring their executions, cost, latency, quality and failures, and acting when they degrade, much as MLOps does for models.
Does Opstral train models?
No. It operates them. Opstral's AI Ops pillar keeps models and agents healthy in production and takes governed action on drift or failure, complementing the platforms that build and train them.
What is the relationship between LLM observability and Agent Ops?
LLM observability is a subset. Agent Ops adds the surrounding operational concerns: cost across agent runs, latency across tool calls, quality across multi-step reasoning, and governed action when an agent starts degrading in production. Tools that started in LLM observability are extending into Agent Ops; tools that started in AIOps are extending down into Agent Ops. The category is still being defined.
How does model or agent drift feed back into build-side MLOps?
Through the retraining trigger. Production observability that detects meaningful drift should be able to signal the training pipeline to schedule a retrain or a prompt refresh. In a well-organised MLOps practice, this loop is closed automatically; in most organisations it is still manual. The direction the category is heading is toward closing it under governance, which is the same pattern autonomous resolution follows for infrastructure incidents.