Grafana vs Datadog vs New Relic: Which Observability Platform for Your AI Stack?

Your Redis cluster, MongoDB Atlas deployment, and Kubernetes infrastructure are all emitting Prometheus-compatible metrics. The question isn't how to collect them — it's where to send them, how to visualize them, and what you'll pay at scale. Grafana, Datadog, and New Relic are the three dominant choices, and each makes fundamentally different trade-offs.

All three can visualize the same Prometheus metrics. The difference is who owns the infrastructure, what it costs at scale, and how fast your team can debug a production incident.

The Common Foundation: Prometheus as the Data Source

All three platforms can consume Prometheus metrics — they just do it differently. Grafana reads directly from Prometheus as a native data source using PromQL. Datadog's agent scrapes Prometheus endpoints via its openmetrics check and pushes to Datadog's cloud. New Relic ingests via Prometheus remote_write or the OpenTelemetry collector, storing in its NRDB.

The underlying data is identical. The same redis_commands_duration_seconds metric from port 8070 flows into all three. The divergence is in storage, query language, bundled features, and — most importantly — pricing.

Prometheus is the lingua franca of metrics. Grafana, Datadog, and New Relic are three different ways to read the same language.

Grafana + Prometheus (Self-Hosted)

You run Prometheus (collector + time series database) and Grafana (visualization + alerting). Both are open-source. Both run on your infrastructure — typically as containers in your EKS cluster. Prometheus scrapes your Redis and MongoDB endpoints, stores the time series locally, and Grafana queries it with PromQL.

The cost model is infrastructure only — compute and storage for the Prometheus server. There are zero per-metric fees. At 10M time series, this is dramatically cheaper than any SaaS alternative. The trade-off: you own uptime, scaling, and retention. You'll need Thanos or Cortex for long-term storage, Alertmanager for alerting, and Loki or Tempo if you want logs and traces alongside metrics.

• Strengths — Full PromQL, unlimited dashboards, no metric limits, complete control over retention, open-source ecosystem, cheapest at scale.
• Weaknesses — You manage the infrastructure. No built-in APM, logs, or distributed tracing (need separate tools). Alerting requires Alertmanager configuration.
• Best for — Teams on Kubernetes/EKS, cost-sensitive at scale, engineers comfortable with PromQL and self-hosted infrastructure.

Datadog (Managed SaaS)

The Datadog agent runs on your hosts, scrapes Prometheus endpoints via its openmetrics check, and pushes metrics to Datadog's cloud backend. Metrics, APM traces, logs, and infrastructure monitoring live in one platform. The setup is fast — install the agent, point it at your Redis port 8070 and MongoDB, and dashboards appear within minutes.

The cost model is per custom metric per month, plus per host for infrastructure monitoring, plus per GB for logs. This is where discipline matters most. Redis Enterprise exposes 200+ metrics per node. If you scrape everything, you're paying to store and index hundreds of metrics you'll never look at.

Datadog is the fastest path to a production dashboard. It's also the fastest path to a $50k/year monitoring bill if you don't filter your metrics.

The critical optimization: in the Datadog agent's openmetrics.d/conf.yaml, use the metrics parameter to explicitly list only the 15-20 Redis and MongoDB metrics you need — the Golden Signals plus AI-specific metrics. Every unfiltered metric is a line item on your invoice.

• Strengths — Unified platform (metrics + APM + logs + traces), excellent anomaly detection, zero infrastructure to manage, great out-of-box dashboards, strong Kubernetes integration.
• Weaknesses — Expensive at scale, vendor lock-in on query language (not PromQL), per-metric billing punishes careless metric collection.
• Best for — Teams that want managed observability, need APM correlation with infrastructure metrics, and have budget for SaaS tooling.

New Relic (Managed SaaS, Different Pricing)

New Relic ingests Prometheus metrics via remote_write (Prometheus pushes to New Relic's endpoint) or through the OpenTelemetry collector. Metrics are stored in NRDB and queried with NRQL — a SQL-like language that's easier to learn than PromQL for teams coming from a database background.

The pricing model is fundamentally different from Datadog: per-user pricing plus data ingest per GB. More metrics don't cost more — more data volume and more team members do. This makes New Relic attractive for high-cardinality workloads where Redis has many shards or MongoDB has many replica sets, because the metric count doesn't directly drive cost.

• Strengths — Predictable pricing for high-cardinality data, strong APM with distributed tracing, NRQL is SQL-like (lower learning curve), good Kubernetes integration, generous free tier.
• Weaknesses — NRQL is not PromQL (migration friction if you're used to Prometheus queries), dashboarding less flexible than Grafana, some features gated behind higher pricing tiers.
• Best for — Teams with many metrics but few users, organizations already using New Relic for APM, high-cardinality environments.

Head-to-Head Comparison

Our Recommendation for AI Data Layers

If you're running EKS or Kubernetes and your team knows PromQL, go with Grafana + Prometheus. It's the cheapest option at scale, the most flexible for custom dashboards, and you get unlimited metrics without worrying about billing. Add Thanos for long-term retention.

If you want one platform for metrics, APM, logs, and traces — and your budget can handle SaaS pricing — Datadog gets you to production dashboards fastest. But be disciplined: specify exactly which Redis and MongoDB metrics to collect. Don't scrape all 200+.

If you have high-cardinality metrics (many shards, many tenants, many databases) and a small engineering team, New Relic's per-user pricing won't punish you for metric volume. The SQL-like NRQL also lowers the learning curve for teams new to observability.

For any choice: start with the Golden Signals (latency, traffic, errors, saturation), add 5-10 AI-specific metrics (cache hit ratio, vector search latency, evictions), and resist the urge to collect everything. The platform matters less than the discipline.

The platform matters less than the discipline. Pick one, monitor the right 15 metrics, and build alerts that wake you up before your users notice.