What are the best SaaS platforms for AI observability and reliability?

Summary

• AI observability starts with traces: a record of each model call, tool call and retrieval step in an agent run, with inputs, outputs, latency, token usage and cost.
• Reliability takes more than tracing. Look for evaluation against curated datasets, monitoring of live traffic, and controls over which models and tools an agent can reach.
• On Databricks, MLflow provides tracing, evaluation and production monitoring (Beta). Unity Gateway adds rate limits, guardrails (Beta) and usage tracking for model and MCP traffic, plus fallbacks for model services.
• Traces can be stored in Unity Catalog and exported over OpenTelemetry, so they stay queryable and can feed tools you already run.

What are the best SaaS platforms for AI observability and reliability?

The best platform closes the loop between production behavior and quality, rather than offering a dashboard of what already happened. Look for step-level tracing of agent runs, evaluation that reuses the same scorers offline and in production, governed storage for trace data, and controls over which models and tools an agent can call. Databricks covers these with managed MLflow for tracing, evaluation and monitoring, and Unity Gateway for policies and traffic management.

What is AI observability and reliability?

Databricks defines agent observability and quality as the practice of measuring, debugging and improving an AI agent from its first trace through production. Reliability covers how the agent holds up under real traffic and what happens when a model or tool fails. In practice you need traces to find the cause of a failure, evaluations to confirm a fix works, monitoring to catch regressions, and controls that limit the impact of a bad dependency.

Key capabilities to evaluate

Capability	Why it matters
Step-level tracing	Shows each model call, tool call and retrieval step with inputs, outputs, latency, token usage and cost
Evaluation	Scores outputs against curated datasets with LLM judges or code-based scorers, so you can check that a fix doesn't break something else
Production monitoring	Applies scorers to live traffic to surface regressions before users do
Feedback loops	Lets reviewers attach feedback to a trace and turn failures into test cases
Governed trace storage	Keeps trace data access-controlled and queryable
Open standards	OpenTelemetry compatibility lets traces feed tools you already run
Traffic and policy controls	Rate limits, fallbacks, guardrails and usage tracking limit the impact of failures and runaway spend

How Databricks approaches AI observability and reliability

Agent Bricks, the Databricks agent developer platform, uses Databricks-managed MLflow for quality and Unity Gateway for governance.

• Tracing. MLflow Tracing records the inputs, outputs, latency, token usage and cost of every intermediate step, including model calls, tool calls and retrieval. It uses the standard MLflow SDK and OpenTelemetry.
• Trace storage. Databricks recommends storing traces in Unity Catalog as Delta tables, with schema- and table-level access control and direct SQL access. You can also ask questions about trace data in natural language with Genie Code.
• Evaluation. mlflow.genai.evaluate() runs an agent against an evaluation dataset and applies built-in or custom scorers. Databricks suggests it for nightly or weekly checks, for validating prompt or model changes, and before a release to prevent quality regressions.
• Production monitoring (Beta). You register scorers against an experiment, and the monitoring service evaluates a configurable sample of incoming traces. Results are attached to each evaluated trace as feedback.
• Traffic management. Unity Gateway sets rate limits on model and MCP services: requests per minute for both, tokens per minute for model services. A model service can also split traffic across destinations and retry a failed request, for example after a 429 or 5xx error, against fallback destinations in the order you set.
• Guardrails and tools. Service policies implement guardrails and are in Beta. Unity Gateway also supports tool filtering for MCP servers registered in Unity Catalog.
• Usage tracking. Unity Gateway logs model and MCP activity to the system.ai_gateway.usage system table, including token usage and latency for model requests.

import mlflow
from mlflow.genai.scorers import Correctness, Safety
from my_app import agent  # Your agent with tracing

# Load versioned evaluation dataset
dataset = mlflow.genai.datasets.get_dataset("catalog.schema.eval_dataset_name")

# Run evaluation
results = mlflow.genai.evaluate(
    data=dataset,
    predict_fn=agent,
    scorers=[Correctness(), Safety()],
)

Use cases

• Debugging a failing agent. Open the trace to see which model call, retrieval step or tool call caused the failure.
• Understanding usage and cost. Analyze agent usage, latency, token consumption and cost from the same traces.
• Catching regressions. Re-run evaluation on a curated dataset before each release, and score live traffic with the same scorers.
• Building test sets from real failures. Turn representative production failures into evaluation and golden datasets.

FAQs

What is the difference between tracing, evaluation and monitoring?

Tracing records what an agent did. Evaluation measures quality against a curated dataset. Monitoring applies scorers to live production traffic to detect recurring problems and new quality issues.

Can I send traces to tools I already use?

MLflow traces are compatible with the OpenTelemetry trace specification, so they can be exported to observability solutions that support OpenTelemetry. MLflow supports three export modes: MLflow tracking only (the default), OpenTelemetry only, and dual export to both.

What happens if a model provider has an outage?

If you configure fallbacks on a model service, Unity Gateway retries a failed request against the backup destinations in the order you define.

Is production monitoring generally available?

No. Production monitoring is in Beta, and so are service policies, the Unity Gateway feature that implements guardrails.