Sampleless vs Datadog: A Complete Comparison for 2026

The short version

Choose Datadog if you need 1,000+ out-of-the-box integrations, synthetic monitoring, or prefer an established vendor with a large community. Datadog has been the Gartner Magic Quadrant Leader for five consecutive years for good reason.

Choose Sampleless if you want to collect 100% of your telemetry data (instead of sampling 1-10%), need predictable flat-fee pricing, or require data sovereignty with BYOC architecture. Sampleless uses OpenTelemetry standards, so you are never locked in.

Pricing comparison

This is where the approaches diverge most significantly.

Datadog pricing (2025-2026)

Datadog uses a multi-dimensional pricing model:

• Infrastructure Monitoring: $15-23/host/month depending on tier
• APM: $31/host/month (Enterprise: $40/host with Continuous Profiler)
• Log Ingestion: $0.10/GB uncompressed
• Log Indexing: $1.70/million events (15-day retention)
• Custom Metrics: $1-5 per 100 metrics overage
• Database Monitoring: $70/host/month

The custom metrics pricing is where costs often spiral. According to SigNoz research, custom metrics can constitute up to 52% of total billing at scale. Here is why: a single metric with 10 endpoints, 3 status codes, and 3 customer tiers creates 90 unique metric timeseries. Add a customer_id tag with 1,000 values and you are at 90,000 metrics from one business metric.

All OpenTelemetry metrics are billed as custom metrics in Datadog.

Sampleless pricing

Sampleless uses flat annual pricing based on cloud account footprint:

• Growth: $100K/year (up to 5 cloud accounts)
• Scale: $180K/year (up to 20 cloud accounts)
• Enterprise: $275K/year (unlimited cloud accounts)

No per-GB fees. No per-host fees. No custom metrics surcharges. Unlimited users included. The price is the price.

Data collection approach

Datadog: Sampling required at scale

Datadog's default sampling is 10 traces per second per Agent, auto-adjusted based on service distribution. Their Intelligent Retention Filter provides:

• Diversity sampling: one span per env/service/operation/resource combination every 15 minutes
• Flat sampling: 1% uniform sample of ingested spans
• Live Search window: 15 minutes (all ingested spans searchable)

In practice, most teams sample 1-10% of traces to control costs. The problem: at 1% sampling with a 0.1% error rate, you need approximately 230,000 requests to achieve 90% confidence of capturing at least one error occurrence.

Sampleless: 100% collection

Sampleless collects every trace, every metric, every log. This is possible because BYOC architecture eliminates the egress costs that make sampling necessary for SaaS vendors.

Complete data means:

• ML baselines are trained on representative data, not a sample
• The trace you need is always there when debugging production issues
• Anomaly detection catches patterns that sampled data would miss

Where Datadog wins

Be honest about what Datadog does well:

• 1,000+ integrations: If you need out-of-the-box connectors for legacy systems, databases, and third-party services, Datadog has years of integration work that Sampleless cannot match.
• Synthetic monitoring: Sampleless does not offer synthetic monitoring. If you need it, Datadog or a specialized tool is required.
• Watchdog AI: Datadog's anomaly detection and root cause analysis has years of refinement. It requires 2 weeks of metric history to train, but works well once established.
• Ecosystem and community: Large user base means more Stack Overflow answers, more third-party tooling, and easier hiring of experienced engineers.

Where Sampleless wins

• Complete data for ML insights: BYOC eliminates egress costs, making 100% data collection economically viable. Better data means better anomaly detection.
• Predictable costs: No surprise bills. No custom metrics traps. No high-water-mark billing where a 5-day traffic spike means paying peak rates for the entire month.
• Data sovereignty: Your telemetry never leaves your cloud. Simplifies compliance with GDPR, HIPAA, SOC 2, and PCI DSS.
• No vendor lock-in: Built on OpenTelemetry and OpenALBA open standards. Your instrumentation works with any OTel-compatible backend.
• Unlimited users: Every engineer, SRE, support person, and stakeholder gets full access. No per-user fees limiting collaboration.

Real cost comparison

For a mid-market company with 200 hosts, moderate APM usage, and 500GB/day log volume:

Note: Datadog costs vary significantly based on usage patterns. The $65 million Coinbase bill (confirmed in Datadog's Q1 2023 earnings call) shows how costs can escalate at scale. OpenAI reportedly spends $170 million annually on Datadog.

Migration considerations

Sampleless is OpenTelemetry-native. If you are already using OTel instrumentation (48.5% of enterprises as of March 2025), migration is straightforward. If you are using Datadog agents, you can run both platforms in parallel during transition.

Typical migration timelines:

• Small teams (5-20 services): 4-8 weeks
• Medium organizations (50-200 services): 3-6 months
• Large enterprises (500+ services): 6-12+ months

Because Sampleless uses open standards, migrating away is equally straightforward. You are never locked in.

Frequently asked questions

The bottom line

Datadog is an excellent platform with a mature ecosystem. If you need extensive integrations, synthetic monitoring, or prefer an established vendor, it is a solid choice.

Sampleless takes a different approach: collect everything, charge predictably, keep data in your cloud. If cost predictability, complete data, and data sovereignty matter to your team, it is worth a conversation.

Neither platform is universally "better." The right choice depends on your priorities.