Observability is not one problem — it is three. Infrastructure observability watches your servers, containers, and network. Application observability watches your code, APIs, and user-facing behavior. Data observability watches your pipelines, schemas, volumes, and freshness. Datadog, Grafana, and Monte Carlo each claim to solve all three but were each built to solve one.
The choice between them is not which platform is most feature-rich. It is which kind of observability pain you feel most acutely, and whether you prefer a single vendor or a composed stack.
TL;DR — which one for your situation
- You want one managed platform and will pay for correlation across layers → Datadog (full-stack, battle-tested alerting, expensive).
- You want open-source, composable, low-cost, and can operate the stack → Grafana (Prometheus/Loki/Tempo + Grafana; best dashboards).
- Your primary pain is data quality (freshness, schema, volume, distribution) → Monte Carlo (specialist; pair with Datadog or Grafana).
- You run production AI/LLM workloads → add a dedicated LLM observability layer (Langfuse/Arize/Helicone) on top — see “The fourth layer” below. Most AI teams end up with Datadog or Grafana for infra/app plus a dedicated LLM-obs tool.
The rest of this post explains the tradeoffs, including the LLM-specific layer that production AI systems require.
Datadog: Full-Stack Platform
Datadog is the most complete single-vendor observability platform. Infrastructure metrics, APM (application performance monitoring), log management, security monitoring, synthetic monitoring, database monitoring, and (more recently) data observability — all in one platform with a unified UI.
The advantage of a single platform is correlation. When a data pipeline fails, you can trace the failure from the infrastructure (a Kubernetes pod ran out of memory) through the application (the Spark job hit an OOM error) to the data impact (the downstream table is stale). This cross-layer visibility is difficult to achieve with composed tools because the correlation logic lives in your head, not in the software.
Datadog’s dashboard and alerting capabilities are mature and flexible. Custom dashboards are easy to build, alerts support complex conditions (e.g., “alert if error rate increases by 50% compared to the same hour last week”), and the notification routing integrates with PagerDuty, Slack, email, and custom webhooks. The alerting is the most battle-tested of the three platforms.
The cost is the most common complaint. Datadog’s pricing is per-host, per-GB-of-logs, per-trace, per-synthetic-test — each dimension adds cost independently. Teams consistently report Datadog bills that grow faster than their infrastructure. A company with 200 hosts, moderate log volume, and APM enabled can easily spend $15,000-30,000 per month on Datadog.
Datadog’s data observability features (added through acquisitions and internal development) are the weakest of its capabilities. It can monitor data pipeline execution, track freshness, and detect schema changes, but the data-specific features are less mature than what Monte Carlo offers. If data observability is your primary need, Datadog may not satisfy it.
The vendor lock-in is real. Datadog’s proprietary query language, dashboard format, and alert configurations do not export cleanly to other platforms. Migrating away from Datadog means rebuilding your monitoring from scratch.
Grafana: Composable Open Source
Grafana takes the opposite approach. Instead of a monolithic platform, Grafana provides best-in-class visualization and alerting that connects to your choice of data sources. Prometheus for metrics, Loki for logs, Tempo for traces, and dozens of third-party integrations. You compose the stack from components that each do one thing well.
The cost advantage is significant. Grafana itself is open source. Prometheus, Loki, and Tempo are open source. The total cost is your infrastructure to run these services, plus Grafana Cloud if you want the managed option. Teams that migrate from Datadog to a Grafana-based stack typically report 50-70% cost reductions.
Grafana’s visualization capabilities are its strongest feature. The dashboard system is more flexible than Datadog’s, with a wider range of visualization types, more granular layout control, and better support for custom data sources. If your primary need is “beautiful, informative dashboards that combine data from multiple sources,” Grafana is the best option.
The trade-off is operational overhead. Running Prometheus, Loki, Tempo, and Grafana in production requires managing four services instead of one. Each service has its own configuration, its own storage backend, and its own scaling characteristics. The integration between components requires manual configuration — setting up Loki as a Grafana data source, configuring Tempo trace-to-log correlation, wiring Prometheus alert rules into Grafana’s alerting engine.
Grafana’s alerting has improved substantially (the unified alerting system introduced in Grafana 9 and refined since), but it is still less polished than Datadog’s. Complex alert conditions, alert grouping, and notification routing are possible but require more configuration effort.
Grafana does not have a native data observability offering. You can build data freshness monitoring, schema change detection, and volume anomaly detection on top of Prometheus and Grafana dashboards, but it requires custom work. This is where Monte Carlo fills a gap that Grafana does not attempt to address.
Monte Carlo: Data Observability Specialist
Monte Carlo was built for one purpose: monitoring the health of your data. It connects to your data warehouse (Snowflake, BigQuery, Redshift, Databricks), monitors your tables for freshness, schema changes, volume anomalies, and distribution shifts, and alerts you when something looks wrong.
The depth of data-specific monitoring is Monte Carlo’s differentiator. Where Datadog can tell you “your pipeline job failed,” Monte Carlo can tell you “your pipeline job succeeded but the output table has 15% fewer rows than expected and the revenue column has shifted to a different distribution.” This distinction matters because many data quality issues occur without pipeline failures — the job runs, but the output is wrong.
Monte Carlo’s automatic anomaly detection uses historical patterns to establish baselines for each table. Freshness baselines (this table typically updates every 4 hours), volume baselines (this table typically has 1-1.2 million rows), and distribution baselines (this column’s values are normally distributed around this range). When a new data point falls outside the baseline, Monte Carlo alerts you.
The lineage tracking maps dependencies between tables, showing you what upstream change caused a downstream data quality issue. When the orders table has a volume anomaly, Monte Carlo traces it back to the raw_orders ingestion job that dropped records. This root cause analysis saves hours of manual investigation.
Monte Carlo’s limitation is scope. It monitors data — not infrastructure, not application performance, not logs. If you need end-to-end observability (infrastructure through application to data), Monte Carlo alone is insufficient. You need it alongside Datadog or Grafana.
The pricing is per-table-monitored, which is more predictable than Datadog’s multi-dimensional pricing but can still add up for large data warehouses with thousands of tables.
The fourth layer: LLM observability
For production AI systems, there is a fourth observability domain the tools above were not built for: LLM observability — tracing LLM and agent calls, tracking token cost per request/tenant/user, monitoring quality (hallucinations, safety, eval regressions), and versioning prompts. Infrastructure and data observability tell you whether the system is healthy; LLM observability tells you whether the outputs are good.
Three tools lead here:
- Langfuse — open-source LLM observability (self-host or managed cloud). Tracing for LLM calls, chains, and agent steps; prompt management/versioning; eval integration; cost analytics. Best for teams that want OSS ownership and tight eval + tracing integration.
- Arize AI — commercial platform for LLM and ML observability. Deep tracing, evaluation, production quality monitoring, drift detection, and a prompt playground. Best for ML teams that want heavy eval and quality monitoring out of the box.
- Helicone — a lightweight LLM observability proxy. Request logging, cost and latency tracking, caching, rate limiting, and analytics with fast setup. Best for teams that want quick cost and usage visibility without a heavy platform.
Datadog and Grafana (via plugins/integrations) now offer some LLM-observability features, and they are fine for basic tracing and cost dashboards. But for the LLM-specific concerns — eval-gated releases, prompt versioning, quality drift, per-tenant cost attribution — a dedicated LLM-observability tool goes deeper. For a production AI system, this is a layer you add on top of your infra/app observability, not a replacement for it.
The Composed Stack vs Single Vendor Question
This diagram requires JavaScript.
Enable JavaScript in your browser to use this feature.
The most common production architecture in 2026 is not a single vendor. It is a combination: Datadog or Grafana for infrastructure and application observability, plus Monte Carlo for data observability. This combination covers the full stack without requiring a single vendor to be best-in-class at everything.
Decision Framework
Use Datadog when you need a single platform, your team does not want to manage observability infrastructure, and your budget can absorb the cost. Best for teams that value correlation across observability layers and are willing to pay the premium for a managed experience.
Use Grafana when cost is a constraint, your team can manage composed infrastructure, and visualization quality matters. Best for teams with strong infrastructure engineering that want maximum flexibility and minimum vendor lock-in.
Use Monte Carlo when data quality is the primary concern and you already have infrastructure monitoring covered. Best for data teams that need automated anomaly detection, lineage tracking, and root cause analysis for data issues. Pair with Datadog or Grafana for full-stack coverage.
Use Grafana plus Monte Carlo when you want the cost advantage of open source infrastructure monitoring with best-in-class data observability. This combination offers the widest coverage at a moderate total cost, assuming your team can operate the Grafana stack.
The wrong choice is using Datadog’s data observability features as a substitute for a dedicated data observability tool. Datadog’s infrastructure monitoring is best-in-class. Its data monitoring is not. Compose your stack accordingly.
What we’d actually pick for production, multi-user AI systems
For a customer-facing or multi-tenant AI product, observability is what lets you debug an incident at 3am and prove quality to a buyer. The deciding factors are different from a prototype:
- Trace every LLM and tool call end-to-end (L6). If a request isn’t traced, you can’t reconstruct what happened — and you can’t debug “why did the agent do that.” Make the gateway/app emit traces by default.
- Layer LLM observability on top of infra observability. Datadog/Grafana answer “is the system healthy?”; Langfuse/Arize answer “are the outputs good?” You usually need both — they are not substitutes.
- Treat token cost as a production metric (L4). Alert on cost spikes per tenant, model, and query type — not just at billing time. A fan-out loop should page someone before the invoice lands.
- Wire evals to alerts. A regression in eval scores should trigger like an infra alert, so quality drops are caught before users see them.
- Monitor output quality in production. Sample real outputs, score them (LLM-as-judge or rubric), and alert on quality drift — not just uptime.
Our default for a team shipping a multi-user AI product: Datadog or Grafana for infrastructure and application observability, plus a dedicated LLM observability tool (Langfuse if you want open-source, Arize for managed depth), plus Monte Carlo only if data quality is in scope. For ongoing oversight of incidents, traces, and evals without a full-time hire, our Fractional AI Reliability Partner covers it; for a quick self-assess, take the AI Production Scorecard (L6 is observability).
