Top Datadog Alternatives in 2026

Pricing, Features, and Deployment Compared

Datadog is one of the most widely used observability platforms for cloud-native teams. It brings infrastructure monitoring, APM, logs, RUM, synthetics, Kubernetes visibility, security, and 1,000+ integrations under one SaaS roof.

However, once usage increases, Datadog’s pricing becomes difficult to forecast across multiple products. The real bill often combines several separate billing dimensions:

• Log indexing costs: Teams pay for log ingestion, but searchable indexed logs are billed separately. At 30TB/month with 30% indexing, log indexing alone can reach ~$30,000/month – often the single largest line item.
• Coupled APM and infrastructure pricing: Datadog APM requires the same hosts to be licensed under Pro or Enterprise Infrastructure Monitoring, so APM adoption also increases the baseline infrastructure bill.
• Custom metrics creep: Kubernetes labels, high-cardinality tags, and application-level metrics increase custom metric volume over time. OTel metrics in Datadog are often billed as custom metrics, which adds cost for teams adopting open standards.
• SaaS-first architecture: Datadog’s CloudPrem is limited to logs and  is currently in preview with feature gaps. There is no self-hosted setup for traces. Teams with HIPAA, GDPR, or data residency requirements may find this a compliance blocker.

This guide compares seven Datadog alternatives – CubeAPM, New Relic, Dynatrace, Grafana Cloud, Elastic APM, Splunk Observability Cloud, and Sentry – across pricing, OpenTelemetry support, deployment model, and real-world migration feasibility.

Pricing Methodology – 30TB/Month Scenario

Volume: 30TB/month – ~20TB logs, 7TB traces, 3TB metrics

Retention: 30 days across all signal types

Indexing: 30% of logs indexed (70% ingested to archive)

Hosts: 100 hosts (used where vendors charge per-host)

Users: 20 full-platform users (used where vendors charge per-seat)

Metrics: 500,000 active series

Add-ons: Core observability only – no security, profiling, or synthetics

Note: orgs at 30TB/month typically run 200-500 hosts; per-host vendor costs scale linearly.

Estimates are directional, based on public rate cards as of early 2026.

Vendor discounts and EDP commitments can significantly reduce SaaS costs.

Why Teams Are Looking for Datadog Alternatives

SKU Sprawl: Multiple Billing Dimensions That Compound

Datadog charges separately for infrastructure hosts, APM hosts, log ingestion, log indexing, log retention, log forwarding, span ingestion, indexed spans, custom metrics, RUM sessions, synthetic tests, error tracking, and premium support. Each product has its own billing unit, default limits, and overage rules. As teams enable more features, costs compound across separate pricing dimensions instead of scaling through one predictable usage meter.

Log Indexing: The Dominant Cost Driver Most Teams Underestimate

Datadog’s log pricing has several separate parts: ingestion ($0.10/GB), indexing ($1.70/million events at 15-day retention), retention, and forwarding ($0.25/GB outbound). Because ingest, indexing, retention, and forwarding are billed separately, log-heavy teams need detailed modeling to forecast the real monthly cost. At 30TB/month with 30% indexing, log indexing alone can reach ~$30,000/month.

Coupled APM and Infrastructure Pricing

When APM is purchased using Datadog’s Infrastructure Monitoring pricing model, every host running APM must also be subscribed to Pro or Enterprise Infrastructure Monitoring. This means APM does not behave like a fully separate line item – infrastructure costs scale alongside APM adoption.

Custom Metrics and the OTel Tax

Custom metrics grow quietly as teams add Kubernetes labels, high-cardinality tags, and application-level metrics. Beyond included allotments (100 per host on Pro, 200 on Enterprise), custom metrics are billed at $5 per 100/month. For teams adopting OpenTelemetry, this creates an additional concern: OTel metrics in Datadog are often billed as custom metrics, adding cost for teams using open standards.

CloudPrem Limitations and Data Residency Gaps

Datadog’s CloudPrem is currently in preview and covers log management only – not a full self-hosted Datadog backend. There is no self-hosted setup for traces. Teams with strict data residency, compliance, or data sovereignty requirements may need a structurally different deployment model.

How We Evaluated the Best Datadog Alternatives

Most observability tools can collect logs, metrics, and traces. The biggest differences show up in pricing, OpenTelemetry support, deployment model, operational effort, and long-term fit.

• Pricing model and cost predictability: How pricing behaves as telemetry volume grows, more users need access, and more features are used. We model GB-based, host-based, and feature-based pricing at consistent data volumes.
• OpenTelemetry support – native vs bolt-on: OTel-native platforms ingest OTLP data without transformation. Platforms that added OTel as a layer on top of proprietary agents may bill OTel metrics as custom metrics or require workarounds.
• Deployment model: SaaS-only, self-hosted, or hybrid. For regulated industries, this is often the deciding factor before any other evaluation begins.
• Observability depth (MELT coverage): Metrics, Events, Logs, and Traces. We evaluate whether each platform covers all four and how well signals are correlated for investigation.
• Kubernetes and cloud-native support: Where observability complexity and cost grow fastest – especially with host-based pricing models.
• The hidden egress cost: When you send telemetry to any external SaaS platform, your cloud provider charges ~$0.10/GB for data leaving your VPC. At 30TB/month, that is $3,000/month, which does not appear on your observability invoice.
• Ease of migration from Datadog: Can existing instrumentation be reused? How much dashboard and alert rebuilding is required?

1. CubeAPM

Best for: DevOps and platform teams that want full-stack observability inside their own cloud without SaaS data egress, pricing sprawl, or DIY self-hosting overhead

Overview

CubeAPM is a self-hosted, OpenTelemetry-native, full-stack observability platform built for teams that want unified visibility across metrics, events, logs, and traces without the cost sprawl common in SaaS observability tools. It runs inside your own AWS, GCP, or Azure VPC – telemetry data stays inside your infrastructure boundary. CubeAPM monitors the setup remotely, handling upgrades, patches, and platform operations; you provide the infrastructure.

Ranked in the top 10 APM platforms in G2’s Spring 2026 APM Grid Report and #4 in easiest-to-use APM tools on G2. Used by Policybazaar (insurance), Delhivery ($3.5B logistics), Mamaearth ($1.2B), world’s largest bus aggregator – redBus (part of MakeMyTrip Limited (NASDAQ: MMYT), 8+ countries), Ola, and Practo (healthcare). SOC 2 Type II and ISO 27001 certified. Rated Capterra 5/5 and G2 5/5.

Key Features

• Full MELT observability: Metrics, events, logs, and traces in one platform with a single investigation workflow
• OpenTelemetry-native: Built from the ground up on OTel. Compatible with OpenTelemetry, Datadog, New Relic, Elastic, and Prometheus agents for incremental migration
• Self-hosted, vendor-managed: Deploys in your VPC. Zero cloud egress cost (saves ~$3,000/month at 30TB vs any external SaaS). Your monitoring stays up even if the internet doesn’t.
• AI-based Smart Sampling: Reduces low-value telemetry volume while preserving high-value traces
• Unlimited retention: Included in pricing – no separate retention charges
• MCP server: CubeAPM provides an MCP server that customers can use to query CubeAPM in natural language
• 800+ integrations: Kubernetes, synthetic monitoring, RUM, and error tracking included.

Pricing

Predictable pricing – $0.15/GB of data ingested. No per-user fees. No per-host charges. No custom metrics fees. Single billing dimension. Unlimited users and unlimited data retention included.

At 30TB/month: ~$5,100/month all-in ($4,500 license + ~$600 infra)

Delhivery: 75% cost reduction after replacing three separate monitoring tools. Mamaearth: ~70% savings, migrated in under an hour. redBus: 4x faster dashboards, 50% faster MTTR.

Pros

• 70-75% lower cost than enterprise APM at scale
• Complete data ownership – telemetry never leaves your VPC
• Predictable pricing with no hidden billing dimensions
• Zero cloud egress cost
• Direct engineering support via WhatsApp and Slack – responds in minutes, which is highly usefulduring incidents.

Cons

• Requires self-hosted deployment in customers’ cloud or on-prem infra; may not suit teams looking for a SaaS-only model
• AI/ML anomaly detection is growing, but not as mature as Dynatrace Davis AI

2. New Relic

Best for: Teams that want a mature SaaS observability platform with broad full-stack coverage and native OTLP ingest

Overview

New Relic is a full-stack SaaS observability platform covering APM, infrastructure, browser, mobile, synthetics, logs, Kubernetes, and incident workflows. It supports native OTLP ingest and recommends OTLP as the preferred method for sending OpenTelemetry data into the platform. Unlike Datadog’s host-based pricing, New Relic uses a data + user pricing model – though the user fees can add up quickly at scale.

Key Features

• Full-stack observability: APM, infrastructure, browser, mobile, synthetics, logs, Kubernetes, incident workflows
• Native OTLP ingest – recommends OTLP as preferred method for OpenTelemetry data
• 780+ quickstarts and integrations
• AI-powered: New Relic AI, alert-coverage analysis, automated instrumentation suggestions
• Free tier: 100GB/month + 1 full platform user

Pricing

Data + User model: $0.40/GB ingested (or $0.60/GB for Data Plus with 90-day retention). Full platform users at $99 to $349 per user per month for full platform access. Core Compute model (preview) replaces seats with Compute Capacity Units (CCUs).

At 30TB/month: ~$20,000-$25,000+/month

Pros

• Broad full-stack coverage in one SaaS platform
• Supports ingesting OpenTelemetry data via  OTLP 
• Free tier offering 100GB free per month is useful for small teams
• Mature synthetic monitoring with scripted browser/API tests

Cons

• Per-user fees compound quickly – a 20-person team pays $6,980+/month in seat fees alone before data ingestion
• NRQL (proprietary query language) creates migration lock-in for dashboards and alerts
• SaaS-only architecture – no self-hosted option for teams with strict data residency requirements
• CCU billing model (preview) introduces billing opacity that can spike during incidents

3. Dynatrace

Best for: Large enterprises that need AI-automated root cause analysis

Overview

Dynatrace differentiates with its Davis AI engine, which automatically maps service dependencies and performs causal root-cause analysis. Gartner ranks Dynatrace highest in “Ability to Execute” among observability vendors. The platform targets large enterprises with complex, fast-moving microservice estates. For teams evaluating Datadog alternatives, Dynatrace trades one form of pricing complexity (SKU sprawl) for another (memory-GiB-hour billing).

Key Features

• Davis AI: Automatic baselining, anomaly detection, and causal root-cause analysis
• Full-stack monitoring via OneAgent with automatic service discovery
• OpenTelemetry support via OTLP endpoints, OTel Collector, and Dynatrace Collector
• Dedicated Kubernetes observability with flexible deployment via Dynatrace Operator
• Log management with separate ingest, processing, and retention pricing

Pricing

Usage-based with separate rate-card units. Full-Stack Monitoring at $0.01/memory-GiB-hour (~$58/month per 8 GiB host). Log Management ingest/process at $0.20/GiB, retain at $0.0007/GiB-day.

At 30TB/month: ~$20,000-$35,000+/month

Breakdown: 100 hosts x $0.08/hr x 8 GiB x 730 hrs ~$4,700 + log ingest 20TB x $0.20/GiB ~$4,100 + log retention ~$430 + traces/metrics/APM + commitment overhead.

Pros

• Best automated root cause analysis in the market
• Automatic full-topology discovery – minimal manual configuration
• Managed deployment option for data residency (Dynatrace Managed)
• Strong compliance and enterprise security features

Cons

• Proprietary OneAgent creates its own vendor lock-in
• Memory-GiB-hour pricing is harder to estimate than per-GB models – teams looking for simpler billing may prefer ingestion-based alternatives
• Log retention billed separately from log ingestion
• Davis AI requires a baselining period – new deployments do not get full value immediately

4. Grafana Cloud (LGTM Stack)

Best for: OTel-first teams that want flexible dashboards and open-source foundations

Overview

Grafana Labs assembled the LGTM stack – Loki (logs), Grafana (dashboards), Tempo (traces), Mimir (metrics) – into a coherent observability platform. Grafana Cloud is the managed version. Paired with Grafana Alloy (an OTel Collector distribution), it provides dedicated OTLP endpoints that auto-route signals to the right backend. For teams leaving Datadog, Grafana offers strong dashboarding and OTel alignment – though usage-based pricing still grows with volume.

Key Features

• LGTM stack: Mimir for metrics, Loki for logs, Tempo for traces
• Grafana Alloy: OTel Collector distribution with built-in Prometheus pipelines
• Strongest dashboarding and visualization across multiple telemetry sources
• k6 performance testing integrated into the observability ecosystem
• Cost attribution features for metrics, logs, and traces

Pricing

Usage-based across telemetry types. Logs/traces/profiles: $0.50/GB ingested (Pro), 50GB included per signal. Metrics: $6.50/1k active series, 10K included. K8s: $0.015/host hour. Platform fee: $19/month.

At 30TB/month (managed cloud): ~$15,000-$20,000+/month

Breakdown: 20TB logs ~$11,000 + 7TB traces ~$3,500 + 500K metric series ~$4,000 + base. Adaptive Metrics/Logs features can reduce this materially.

Pros

• Fully OTel-native – no custom metrics penalty (unlike Datadog’s OTel-as-custom-metrics billing)
• Adaptive Metrics/Logs actively help reduce billing
• Strong open-source community; highly customizable
• Self-hosted path available for cost-driven teams with operational capacity

Cons

• No native APM out-of-the-box – requires significant configuration
• At scale, self-hosted Grafana is prone to performance degradation; query times increase and dashboard load slows as data volume and user count grow
• Usage-based pricing still grows with volume on managed cloud
• LGTM stack has a steep learning curve for teams new to Grafana

5. Elastic APM

Best for: Teams already on the Elastic Stack who want to add APM without a new vendor

Overview

Elastic APM is the distributed tracing and application monitoring component of the Elastic Stack. For teams already indexing logs in Elasticsearch and visualizing in Kibana, adding APM is natural. It provides distributed tracing, service maps, error tracking, and MELT correlation across serverless, hosted, and self-managed deployments. 

Note: Elastic APM’s OSS version reached end-of-service in September 2025.

Key Features

• Native Elasticsearch integration: APM data correlates directly with log indices
• OpenTelemetry support across serverless, self-managed, and hybrid deployments
• Machine learning-based anomaly detection via Elastic ML
• RUM via JavaScript agent for frontend experience monitoring
• Available self-hosted (SSPL license) or Elastic Cloud (Serverless/Hosted)

Pricing

Self-hosted is free; you cover infrastructure. Elastic Cloud Serverless: Logs Essentials from $0.07/GB ingested + $0.017/GB retained/month. Cloud Hosted from $99/month (Standard) to $184/month (Enterprise).

At 30TB/month (Elastic Cloud): ~$8,000-$15,000/month

Pros

• Zero incremental cost if already running Elastic for logs
• Strong log + trace correlation in the same query interface
• Self-hosted option keeps data on your infrastructure
• ML-based anomaly detection included

Cons

• Significant operational overhead to run self-hosted at scale – teams wanting self-hosted without the ops burden may prefer vendor-managed alternatives
• Kibana Query Language (KQL) is less developer-friendly than SQL
• 2021 SSPL licensing change – review for open-source compliance
• Pricing varies by deployment model, making cost comparison less straightforward

6. Splunk Observability Cloud

Best for: Enterprises with existing Splunk investments and deep SIEM integration needs

Overview

Splunk Observability Cloud is known for real-time, full-fidelity monitoring (no default sampling) across infrastructure, applications, and user interfaces. It uses the Splunk Distribution of the OTel Collector for telemetry collection. Strong for enterprises that want mature SaaS monitoring alongside Splunk’s security portfolio. However, it is the most expensive platform in this comparison.

Key Features

• Full-fidelity distributed tracing (no default sampling)
• Strong Kubernetes monitoring with Collector-based telemetry and entity views
• Deep Splunk SIEM and log analytics integration
• Real-time stream processing for telemetry
• Uses Splunk Distribution of the OpenTelemetry Collector

Pricing

Package-based with separate tiers for infrastructure, APM, and end-to-end. Infrastructure from $15/host/month. App & Infra from $60/host/month. End-to-End from $75/host/month.

At 30TB/month: ~$35,000-$60,000+/month

Estimated based on 100 hosts, APM per-host fees, log analytics volume, and enterprise minimums. Treat this as a floor, not a ceiling.

Pros

• Full-fidelity traces – no sampling means no blind spots
• Best-in-class integration with Splunk Security for unified IT and security observability
• Mature enterprise compliance story

Cons

• Most expensive platform in this comparison – teams prioritizing cost reduction may find better value with ingestion-based alternatives
• Modular pricing with separate packaging for each capability
• Best value only with existing Splunk investment – overkill otherwise
• SaaS-first – not suitable for teams requiring self-hosted backends

7. Sentry

Best for: Developer-first teams that debug from code and user experience inward

Overview

Sentry is a developer-first application monitoring platform covering errors, tracing, logs, session replay, profiling, cron monitoring, uptime monitoring, and AI-assisted debugging. Best for developer-led teams that want fast issue triage without adopting a heavier infrastructure-first observability platform. Unlike Datadog’s broad infrastructure coverage, Sentry focuses on the application layer.

Key Features

• Session Replay: Video-like reproductions of user sessions for web and mobile – not available in most observability platforms
• Error monitoring with stack traces, breadcrumbs, and context
• Distributed tracing and performance monitoring
• Profiling, cron monitoring, and uptime checks
• OpenTelemetry support (SDKs use OTel under the hood for tracing)
• Self-hosted option available

Pricing

Event + usage-based. Team plan from $26/month. Business from $80/month. Logs: $0.50/GB (5GB included). Spans billed by volume above plan usage.

At 30TB/month: ~$15,260/month

Pros

• Best-in-class developer experience for error triage
• Session Replay provides video-like debugging not found in traditional APM
• Self-hosted option for data control
• Strong frontend and mobile debugging capabilities

Cons

• Primarily error and debugging focused – not full infrastructure observability
• Teams needing deep infrastructure monitoring will need a complementary tool
• Pricing at high volume can approach traditional APM costs
• Less suited for infra-first or SRE-led observability workflows

Cost Comparison at 30TB/Month Ingestion

Tool	Est. Cost @ 30TB/mo	Pricing Model	OTel Native	Data Residency	Self-Hosted
CubeAPM	~$5,100/mo all-in	$0.15/GB ingestion-based	Native	Always (in-VPC)	Yes (vendor-managed)
Elastic APM	~$8K-$15K	Deployment-based	Supported	If self-hosted	Yes
Sentry	~$15,260	Event + usage	Supported	If self-hosted	Yes
Grafana Cloud	~$15K-$20K+	Usage-based	Native	If self-hosted	Yes
New Relic	~$20K-$25K+	Ingest + per-user	Supported	SaaS only	No
Dynatrace	~$20K-$35K+	GiB-hour + commit	Supported	Managed option	Managed
Datadog (ref.)	~$30K-$45K+	Host + feature-based	Supported*	SaaS only	No
Splunk	~$35K-$60K+	Host + enterprise	Supported	Limited	Limited

* OTel metrics in Datadog are often billed as custom metrics. Datadog included as reference. All estimates use the methodology assumptions above. Vendor discounts and EDP commitments can significantly reduce SaaS costs.

If you want to model your current Datadog bill before committing to a switch, the Datadog pricing calculator breaks down every cost dimension: hosts, log indexing, custom metrics, APM spans, and cloud egress fees most teams overlook.

The Hidden Cost: Cloud Data-Out Egress

When you send telemetry to any external SaaS platform – Datadog, New Relic, or any cloud-hosted alternative – your cloud provider charges approximately $0.10/GB for data leaving your VPC. At 30TB/month, that is $3,000/month in AWS or GCP egress fees, which does not appear on your observability invoice. Self-hosted platforms running inside your VPC have zero data-out cost.

How to Migrate Away from Datadog

Migrating away from Datadog is manageable, but it is not usually a one-click switch. The difficulty depends on how many Datadog products you use, how much telemetry already flows through OpenTelemetry, and how many dashboards, monitors, and log pipelines your teams rely on.

• Audit current usage: List what your team actively uses in Datadog – infrastructure monitoring, APM, logs, dashboards, monitors, RUM, synthetics, custom metrics, cloud integrations, and Kubernetes visibility.
• Separate must-haves from nice-to-haves: Prioritize production reliability workflows first. Not every old dashboard, alert, or log index needs to move 1:1.
• Review telemetry paths: Check what comes through the Datadog Agent, OpenTelemetry, APIs, cloud integrations, log forwarders, or custom pipelines. OTel-based telemetry is usually easier to reroute.
• Map dashboards and monitors: Dashboards, monitors, SLOs, alert thresholds, tags, and notification rules often need to be rebuilt in the new platform.
• Check log retention and indexing: Decide which logs must stay searchable, which can move to cheaper storage, and how long each team needs access.
• Choose a migration path: Smaller teams may do a direct switch, while larger teams usually prefer phased migration or OpenTelemetry-led dual-write before full cutover.
• Test before switching fully: Validate critical dashboards, alerts, log search, trace correlation, Kubernetes views, RUM, synthetics, and incident workflows before turning Datadog off.

Migration is easiest when telemetry already flows through OpenTelemetry. The hardest parts are usually dashboards, monitors, log indexing, and team workflows built around Datadog over time.

Which Datadog Alternative Is Right for Your Use Case?

• CubeAPM: Choose if cost predictability and data sovereignty are priorities. Ingestion-based pricing of $0.15/GB, unlimited users, runs in-VPC with zero egress cost.
• New Relic: Choose if you want broad SaaS observability with native OTLP ingest and a free tier (100GB per month) to start. Model user fees carefully as your team grows.
• Dynatrace: Choose if enterprise AI automation and causal root-cause analysis are your primary need. Be prepared for the annual commitment and memory-GiB-hour pricing.
• Grafana Cloud: Choose if you are OTel-first, want flexible dashboards, and are comfortable managing or funding the LGTM stack.
• Elastic APM: Choose if your team already runs the Elastic Stack and wants to add distributed tracing without introducing another vendor.
• Splunk: Choose if your organization has an existing Splunk investment and needs unified IT and security observability.
• Sentry: Choose if your team is developer-led, debugging from code inward, and needs session replay and error monitoring more than infrastructure-first observability.

When Datadog Is Still the Right Choice

Datadog is best for teams that want one mature SaaS platform for observability, security, Kubernetes, APM, logs, RUM, synthetics, and integrations, and where cost is not a constraint.

• Your usage fits within a predictable range, and the bill stays manageable across the products you use
• You want one vendor covering infrastructure, APM, logs, RUM, synthetics, Kubernetes, and security without stitching tools together
• Your team actively uses several Datadog modules; replacing a platform your workflows are already built around creates migration work that may outweigh the savings
• The integration ecosystem matters – Datadog’s 1,000+ integrations are hard to replicate quickly
• Cost is not your main constraint, and vendor maturity, support, and feature breadth take priority

Final Thoughts

There is no single best Datadog alternative for every team. Datadog is still a strong platform when you need broad SaaS observability, a large integration ecosystem, and mature workflows across infrastructure, APM, logs, RUM, synthetics, and security.

The right alternative depends on your main constraint. CubeAPM, Grafana, and Elastic make sense for cost predictability and data sovereignty. Dynatrace fits enterprise automation. New Relic offers broad SaaS coverage with a different pricing model. Sentry is the right pick for developer-first debugging.

Before switching, compare your top two options against your real host count, telemetry mix, retention needs, OpenTelemetry setup, and deployment requirements. The numbers at your scale will make the decision clearer than any feature matrix.

Frequently Asked Questions

1. What is the best Datadog alternative in 2026?

There is no single best option for every team. For cost predictability and data sovereignty, self-hosted OTel-native platforms offer the strongest structural advantages. Dynatrace for enterprise AI automation. Grafana for dashboards and OpenTelemetry flexibility. Sentry for developer-first error monitoring.

2. Which Datadog alternative is best for self-hosting?

Grafana (LGTM stack) and Elastic APM support fully self-managed deployment. For teams that want self-hosted observability without managing the backend themselves, vendor-managed self-hosted options exist that handle platform operations within your VPC.

3. Is New Relic better than Datadog?

Not across the board. New Relic offers broad full-stack coverage with OTLP ingest and data-plus-user pricing. Datadog has the larger integration ecosystem and broader product breadth. Both are significantly more expensive than OTel-native alternatives at the same data volume.

4. What is the cheapest Datadog alternative?

Platforms with per-GB pricing and no per-host or per-user fees offer the lowest TCO at most team sizes – particularly when cloud egress savings are included in the calculation. Self-hosted options can reduce costs further by eliminating the SaaS margin and egress fees.

5. Can I use OpenTelemetry to migrate away from Datadog?

Yes. If your services already send data through OpenTelemetry Collectors, testing an alternative backend is low-risk and does not require re-instrumentation. You can dual-write telemetry to both Datadog and the new platform during a transition period, then cut over when dashboards and alerts are validated.

6. How hard is it to migrate away from Datadog?

The difficulty depends on how many Datadog products you use and how much telemetry already flows through OpenTelemetry. OTel-based telemetry is easy to reroute. The hardest parts are rebuilding dashboards, monitors, log indexing rules, and team workflows that were built around Datadog over time.

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