9 Datadog alternatives: a DevOps guide to cutting observability costs without losing features
If you landed here searching “Datadog alternative,” you’re probably wrestling with one of three things: ballooning telemetry bills, data pipeline sprawl, or vendor lock-in risk. Datadog remains a feature-rich platform with broad coverage across infra, APM, logs, RUM, synthetics, security, and more. The friction is rarely about visibility; it’s about control—controlling what you send, what you keep, and what you pay for. This guide covers the leading alternatives DevOps teams actually trial, where each one fits, and a pragmatic path many mid-size engineering orgs are taking: stay on Datadog for the mature feature set, but insert Sawmills’ smart telemetry management in front of it to right-size volumes, protect availability, and keep spend predictable.
The short list of Datadog alternatives (with links, descriptions, pros and cons)
Datadog — the baseline to beat
A full-stack observability and security platform spanning metrics, traces, logs, RUM, synthetics, cloud cost monitoring, and more. Datadog shines with integrations, dashboards, alerts, and out-of-the-box workflows, making it a common default for fast time-to-value.
Pros
• Broadest “single pane” across infra, APM, logs, RUM, synthetics, and security
• Mature integrations and dashboards; fast onboarding
• Powerful alerting, notebooks, and ecosystem
Cons
• Cost can scale quickly with verbose logs and high-cardinality metrics
• Hard to govern ingest during incidents without upstream controls
• Vendor lock-in concerns for long-term data strategy
Grafana Cloud / Grafana Stack
A composable stack that embraces open formats. Pair Prometheus/Mimir for metrics, Loki for logs, and Tempo for traces with Grafana dashboards. You can mix managed services with self-hosted components to tune cost, performance, and control.
Pros
• Open formats reduce lock-in and improve portability
• Flexible managed + self-hosted model with strong cost levers
• Excellent dashboards and community plugins
Cons
• More DIY for routing, retention, and governance policies
• Performance and reliability tuning spans multiple backends
• Feature cohesion can lag “all-in-one” suites
New Relic
A full-stack telemetry platform known for clean onboarding, solid APM, and a usage-based pricing approach. It offers application maps, browser/mobile monitoring, and logs, with a strong developer UX.
Pros
• Fast time-to-value with good distributed tracing
• Opinionated UI that’s easy for dev teams to adopt
• Usage-based pricing can be attractive at moderate scale
Cons
• High-cardinality data and noisy logs may still sting on cost
• Less flexible than ELK-style pipelines for log shaping
• Some advanced capabilities require add-ons
Elastic Observability (Elastic Cloud)
Search-first logging and analytics with growing coverage for metrics and tracing. Elastic’s schema-on-read approach and ILM policies provide deep control, especially if your org already runs Elasticsearch.
Pros
• Powerful search and flexible queries on large log volumes
• Mature lifecycle management and data tiers to manage cost
• Strong ecosystem and self-hosting option
Cons
• Requires careful index and retention tuning to stay affordable
• Operating at scale can be complex for smaller teams
• Tracing/APM is less opinionated than Datadog/Dynatrace
Honeycomb
Event-based observability that excels at debugging “unknown unknowns.” High-cardinality exploration and rapid query feedback encourage better instrumentation and faster incident resolution.
Pros
• Best-in-class exploration for high-cardinality data
• Great fit for SLO-driven, modern delivery practices
• Encourages intentional event modeling and clear signals
Cons
• Requires investment in event design versus “ship all logs”
• Not a catch-all suite for synthetics/security
• Adoption curve for teams used to log-centric workflows
Dynatrace
An opinionated, enterprise-grade platform with strong automation, topology mapping, and AI-assisted root-cause analysis. Emphasizes curated experiences and governance at scale.
Pros
• Excellent automated dependency mapping and causal analysis
• Enterprise controls and guardrails out of the box
• Reduces toil for large, complex estates
Cons
• Pricing/licensing can be complex
• Can feel heavyweight for mid-size teams
• Less modular than OTel-first approaches
ServiceNow Cloud Observability (Lightstep)
Tracing-first observability with clear service graphs, strong OTel alignment, and tight integration with ServiceNow workflows. Useful for platform teams formalizing SLOs and incident response.
Pros
• Strong tracing lineage and developer ergonomics
• Good fit if your org is already on ServiceNow
• OTel-friendly instrumentation and pipelines
Cons
• Logs/infra story is less unified than some suites
• Features may rely on broader ServiceNow modules
• Smaller marketplace vs. Datadog/Splunk
Splunk Observability Cloud
A real-time streaming observability suite from the SIEM giant, with metrics, traces, and logs. Attractive for organizations already standardized on Splunk for security and compliance.
Pros
• Reduces tool sprawl if you’re a Splunk shop
• Robust enterprise controls and RBAC
• Good streaming capabilities and APM coverage
Cons
• Costs require discipline for logs and cardinality
• UI cohesion varies across components
• Admin overhead if you also run classic Splunk
Chronosphere
A metrics-first platform designed for scale, cost governance, and policy-based control. Known for budgets, guardrails, and shaping time series to curb cardinality and sprawl.
Pros
• Excellent for high-cardinality, multi-tenant environments
• Built-in policies and budgets to shape metrics cost
• Clear ownership and accountability patterns
Cons
• Metrics-centric; you still need a logs/traces strategy
• Smaller ecosystem than long-time incumbents
• Requires org change to fully leverage budgets/policies
Open-source OTel + your choice of backends
Maximum control and minimum lock-in by assembling your own stack. Run OTel Collectors and choose best-of-breed backends per signal, balancing cost and performance with your SRE capacity.
Pros
• Total portability and vendor independence
• Can be highly cost-effective at scale when tuned
• Choose the best backend per signal
Cons
• You own reliability, upgrades, and SRE toil
• Longer time-to-value than managed suites
• Governance/guardrails are DIY unless augmented
Sawmills + Datadog — the smarter “alternative”
Keep Datadog’s feature-rich platform, but insert Sawmills upstream to control what you ingest. Sawmills provides an AI-powered telemetry pipeline with one-click actions to sample, route, drop, enrich, aggregate, standardize, redact PII, manage cardinality, deduplicate logs, and allocate costs by value groups. You preserve the dashboards and workflows your teams rely on while regaining control over volume, availability, and spend.
Data volume drops without sacrificing visibility, because low-value noise is sampled or aggregated before it hits paid ingestion. Automated guardrails protect both your budget and your SLOs during incidents.Centralized multi-pipeline management keeps OTel config consistent and auditable across collectors.
You don’t have to choose between a mature, feature-rich platform and a sustainable observability bill. Pair Datadog with Sawmills to explore, govern, and automate your telemetry before it becomes cost or risk. You’ll preserve dashboards, alerts, and workflows your teams rely on, while cutting ingestion, stabilizing availability, and gaining real accountability across engineering.
Schedule a demo of Sawmills smart telemetry management to see live recommendations on your own telemetry flows and apply one-click fixes that reduce costs and improve quality instantly.
