Stitching together separate observability tools—Prometheus for metrics, Elasticsearch for logs, Jaeger for traces—has left many organizations with data silos, slow queries, and rising infrastructure costs. In 2026, the shift toward unified, all-in-one observability stacks is accelerating.
We evaluated 8 platforms that promise to consolidate metrics, logs, and traces into a single system. Whether you need an open-source solution you can self-host or a fully managed SaaS, this guide covers what actually works for production cloud-native infrastructure.
Why All-in-One Observability Matters
Traditional observability setups require managing multiple systems:
| Signal | Traditional Tool | Query Language |
|---|---|---|
| Metrics | Prometheus | PromQL |
| Logs | Elasticsearch/Loki | Lucene/LogQL |
| Traces | Jaeger/Tempo | Custom/TraceQL |
| Visualization | Grafana | N/A |
This fragmented approach creates real problems:
- Swivel-chair analysis: Jumping between UIs to correlate issues
- No native correlation: Connecting a spike in latency to a specific log line requires manual effort
- Operational burden: Managing 3-4 stateful distributed systems
- Storage costs: Elasticsearch alone can cost $100,000+/month at 100TB/day
All-in-one observability stacks solve these problems by storing all telemetry in a single backend with unified querying and native signal correlation.
For teams already running cloud-native monitoring, transitioning to a unified stack can dramatically reduce complexity.
The 8 Best All-in-One Observability Stacks (2026)
Open Source Unified Platforms
1. SigNoz
SigNoz is an open-source, OpenTelemetry-native observability platform that unifies logs, metrics, traces, and exceptions in a single application. Built on ClickHouse, it’s positioned as the open-source alternative to Datadog.
Architecture:
- Single ClickHouse backend for all signals
- Native OpenTelemetry ingestion (no proprietary agents)
- Unified UI with seamless signal correlation
- Query Builder, PromQL, or direct ClickHouse SQL
Key Features:
- Logs, metrics, traces, exceptions in one pane
- Trace-to-logs and metrics-to-traces correlation
- Alerting with support for Slack, PagerDuty, webhooks
- Dashboards with PromQL and ClickHouse queries
- Self-hosted or SigNoz Cloud options
Pricing:
- Self-hosted: Free (Apache 2.0)
- SigNoz Cloud: Usage-based (~$0.30/GB ingested for logs)
Best For: Startups and cost-conscious teams committed to OpenTelemetry who want full data ownership.
The Reality Check: SigNoz has been in development for 5 years and is maturing rapidly, but lacks some enterprise features (SSO, audit logs) available in commercial platforms. The self-hosted option requires ClickHouse expertise at scale.
2. OpenObserve
OpenObserve is a Rust-based observability platform designed for extreme efficiency. It claims up to 140x lower storage costs compared to Elasticsearch.
Architecture:
- Single binary deployment
- Native object storage support (S3, GCS, Azure Blob)
- SQL for logs/traces, PromQL for metrics
- Built-in UI (no separate Grafana needed)
Key Features:
- Logs, metrics, traces, and frontend monitoring
- SQL + PromQL query support
- Real-time alerting and dashboards
- Functions for data transformation
- Single binary or Kubernetes deployment
Pricing:
- Self-hosted: Free (Apache 2.0)
- OpenObserve Cloud: Usage-based pricing
Best For: Teams prioritizing resource efficiency and simple deployment who can accept a less mature platform.
The Reality Check: OpenObserve is an early-stage project. While promising, it lacks the battle-tested stability and feature depth of more established platforms. Production deployments should proceed with caution.
3. ClickStack (ClickHouse + HyperDX)
ClickStack is an opinionated, open-source observability stack combining ClickHouse, OpenTelemetry Collector, and HyperDX for visualization.
Architecture:
- ClickHouse columnar database (single backend)
- OpenTelemetry Collector for ingestion
- HyperDX for unified UI and alerting
- Native SQL for all queries
Key Features:
- Native cross-signal correlation via SQL JOINs
- 10x less storage than Elasticsearch
- Sub-second queries on high-cardinality data
- Session replay and error tracking (HyperDX)
- Kubernetes-ready deployment
Pricing:
- Open-source: Free
- ClickHouse Cloud: Usage-based
Best For: Organizations with SQL expertise who want maximum query flexibility and cost efficiency at scale.
The Reality Check: ClickStack requires understanding columnar database concepts. Teams used to Prometheus/Grafana will face a learning curve with SQL-based observability.
4. Grafana LGTM Stack
The Grafana Stack combines Loki (logs), Grafana (visualization), Tempo (traces), and Mimir (metrics) into a comprehensive observability solution.
Architecture:
- Separate optimized backends per signal type
- Loki: Label-indexed log aggregation
- Tempo: Index-free distributed tracing
- Mimir: Horizontally scalable Prometheus
- Grafana: Unified visualization layer
Key Features:
- Best-in-class dashboarding and visualization
- Each component highly optimized for its signal
- Large plugin ecosystem
- Strong community and documentation
- Self-hosted or Grafana Cloud
Pricing:
- Self-hosted: Free (AGPL)
- Grafana Cloud: Free tier + usage-based
Best For: Teams with DevOps expertise who prioritize visualization and can manage operational complexity.
The Reality Check: This is a “stack,” not a unified product. You’re managing three separate stateful systems with three query languages (PromQL, LogQL, TraceQL). Correlating signals requires UI-level tricks rather than native database joins. Running LGTM at scale is a full-time job for a dedicated team.
For Grafana-specific guidance, see our Grafana consulting services.
Commercial Unified Platforms
5. Datadog
Datadog is the leading commercial observability platform, offering infrastructure monitoring, APM, logs, security, and more in a single SaaS product.
Architecture:
- Proprietary SaaS backend
- Datadog Agent for collection
- Unified web interface
- Single query interface across signals
Key Features:
- Infrastructure, APM, logs, RUM, synthetics, security
- AI-powered anomaly detection
- 750+ integrations
- Watchdog automatic insights
- Live process and container monitoring
Pricing:
- Infrastructure: ~$15-23/host/month
- APM: ~$31-40/host/month
- Logs: ~$0.10/GB ingested + $1.70/million indexed
- Complex consumption model with multiple SKUs
Best For: Enterprises wanting comprehensive observability without operational burden who can afford premium pricing.
The Reality Check: Datadog is expensive at scale. Organizations with large container footprints or high log volumes regularly report bills exceeding $100K/month. The pricing model is complex, and costs can surprise teams unfamiliar with consumption tracking.
6. New Relic
New Relic provides full-stack observability with all telemetry stored in a single database (NRDB), queried via NRQL.
Architecture:
- Proprietary NRDB database
- Single query language (NRQL)
- APM, infrastructure, logs, browser, mobile, synthetics
- OpenTelemetry compatible
Key Features:
- Unified data model across all signals
- NRQL for flexible querying
- AI-powered anomaly detection (New Relic AI)
- Vulnerability management
- Free tier with 100GB/month
Pricing:
- Free: 100GB/month + 1 full user
- Standard/Pro/Enterprise: Per-user + consumption-based
Best For: Teams wanting integrated app-to-infrastructure monitoring with a generous free tier.
The Reality Check: New Relic’s per-user pricing can become expensive for larger teams. The proprietary NRQL query language requires learning a new syntax.
7. Dynatrace
Dynatrace combines observability with application security and AI-powered automation in a single platform.
Architecture:
- Proprietary Grail data lakehouse
- OneAgent automatic discovery
- Davis AI for root cause analysis
- APM, infrastructure, logs, RUM, security
Key Features:
- Automatic topology mapping
- AI-powered root cause analysis
- Application security built-in
- Full-stack monitoring from code to cloud
- Kubernetes and cloud-native native
Pricing:
- Subscription-based with consumption charges
- Host-based or DPS (Dynatrace Platform Subscription) models
- Enterprise pricing typically $50-100K+/year minimum
Best For: Large enterprises wanting AI-assisted observability with minimal manual configuration.
The Reality Check: Dynatrace is expensive and primarily targets large enterprises. The OneAgent approach is comprehensive but can be resource-intensive. Smaller organizations may find better value elsewhere.
8. Elastic Observability
Elastic Observability leverages Elasticsearch to unify logs, metrics, APM, and uptime monitoring.
Architecture:
- Elasticsearch backend
- Elastic Agent/Beats for collection
- Kibana for visualization
- OpenTelemetry support
Key Features:
- Unified view across logs, metrics, traces
- Machine learning anomaly detection
- Uptime and synthetic monitoring
- SIEM integration
- Self-hosted or Elastic Cloud
Pricing:
- Self-hosted: Free (Elastic License)
- Elastic Cloud: Usage-based starting ~$95/month
Best For: Organizations already invested in Elasticsearch who need observability integrated with security (SIEM).
The Reality Check: Elasticsearch’s storage overhead is 12-19x higher than columnar alternatives. At scale, infrastructure costs can become prohibitive. JVM tuning expertise is required for optimal performance.
Architecture Comparison
| Platform | Backend | Query Language | Correlation | Storage Efficiency |
|---|---|---|---|---|
| SigNoz | ClickHouse | PromQL, SQL, Builder | Native (single DB) | High |
| OpenObserve | Custom (Rust) | SQL, PromQL | Native | Very High |
| ClickStack | ClickHouse | SQL | Native (JOINs) | Very High |
| Grafana Stack | Mimir/Loki/Tempo | PromQL/LogQL/TraceQL | UI-level only | Medium |
| Datadog | Proprietary | Unified | Native | N/A (SaaS) |
| New Relic | NRDB | NRQL | Native | N/A (SaaS) |
| Dynatrace | Grail | DQL | Native | N/A (SaaS) |
| Elastic | Elasticsearch | KQL, Lucene | Native | Low |
Selection Criteria
1. Unified vs. Composable
Choose Unified (SigNoz, OpenObserve, ClickStack):
- Single backend reduces operational complexity
- Native cross-signal correlation
- One query language to learn
- Lower total cost of ownership
Choose Composable (Grafana Stack):
- Best-of-breed components for each signal
- Flexibility to swap individual tools
- Strong existing Prometheus/Grafana investment
- Team has DevOps expertise to manage complexity
2. Open Source vs. Commercial
Choose Open Source (SigNoz, OpenObserve, Grafana Stack):
- Full data ownership and control
- No vendor lock-in
- Lower direct costs (infrastructure only)
- Compliance requirements for data residency
Choose Commercial (Datadog, New Relic, Dynatrace):
- Zero operational burden
- Enterprise support with SLAs
- Advanced AI/ML features
- Budget available for observability platform
3. OpenTelemetry Native
OpenTelemetry has become the vendor-neutral standard for telemetry collection. Platforms with native OTel support (SigNoz, OpenObserve, Grafana Stack) prevent vendor lock-in and simplify instrumentation.
4. Cost at Scale
For high-volume environments (10TB+ daily), cost differences become significant:
| Platform | Approx. Cost at 10TB/day |
|---|---|
| Elasticsearch | $100,000+/month |
| Grafana Cloud | $30,000-50,000/month |
| Datadog | $50,000-100,000+/month |
| SigNoz Cloud | $15,000-25,000/month |
| Self-hosted ClickHouse | $5,000-15,000/month (infra) |
Implementation Patterns
Pattern 1: Full Open Source (Self-Hosted)
Applications → OTel Collector → SigNoz/OpenObserve → DashboardsPros: Maximum control, lowest direct cost, no vendor lock-in Cons: Requires infrastructure expertise, self-managed HA/backups
Pattern 2: Managed Open Source
Applications → OTel Collector → SigNoz Cloud / Grafana CloudPros: Open standards with managed operations Cons: Usage-based costs can scale unexpectedly
Pattern 3: Full SaaS
Applications → Vendor Agent → Datadog/New Relic/DynatracePros: Zero operational burden, enterprise features Cons: Highest cost, potential vendor lock-in
For most cloud-native organizations, Pattern 2 offers the best balance of control and operational simplicity.
Migration Considerations
From Prometheus + Grafana
- Keep Prometheus initially: Use remote write to send metrics to the new platform
- Migrate dashboards gradually: Most platforms import Grafana JSON
- Add logs and traces: The value of unified observability comes from correlation
- Sunset Prometheus when ready: Once comfortable, remove the duplicate system
From ELK Stack
- Evaluate storage savings: ClickHouse-based platforms can reduce storage 10x
- Migrate logs first: This typically represents the largest volume
- Add tracing: Often missing from ELK-only setups
- Preserve Kibana dashboards: Some platforms support import
From Datadog/New Relic
- Instrument with OpenTelemetry: Replace proprietary agents
- Run in parallel: Send telemetry to both platforms initially
- Migrate dashboards and alerts: Manual recreation often required
- Complete cutover: Once confident, disable the commercial platform
Best Practices for Cloud-Native Observability
1. Adopt OpenTelemetry
Instrument applications with OpenTelemetry from the start. It’s vendor-neutral, widely supported, and prevents lock-in regardless of which backend you choose.
# OpenTelemetry Collector configuration
receivers:
otlp:
protocols:
grpc:
endpoint: 0.0.0.0:4317
http:
endpoint: 0.0.0.0:4318
exporters:
otlp:
endpoint: "signoz-otel-collector:4317"
service:
pipelines:
traces:
receivers: [otlp]
exporters: [otlp]
metrics:
receivers: [otlp]
exporters: [otlp]
logs:
receivers: [otlp]
exporters: [otlp]2. Correlate Signals
The power of unified observability comes from correlation. Ensure your instrumentation includes:
- Trace IDs in logs: Connect log lines to distributed traces
- Service names: Consistent naming across all signals
- Environment labels: Distinguish production from staging
3. Right-Size Retention
Not all data needs the same retention:
| Data Type | Typical Retention |
|---|---|
| High-res metrics | 15 days |
| Aggregated metrics | 13 months |
| Logs | 30-90 days |
| Traces | 7-15 days |
| Error traces | 30 days |
4. Implement Alerting Thoughtfully
Focus on symptoms (user-facing issues) rather than causes:
- Alert on error rates, not individual errors
- Alert on latency percentiles (p99), not averages
- Use anomaly detection for baseline deviations
For comprehensive alerting strategies, see our guide on Prometheus monitoring for Kubernetes.
The Future of Observability Stacks
Convergence Toward Unified Platforms
The trend is clear: organizations are moving away from fragmented tooling toward unified observability. Uber’s recent move from a monolithic on-premises stack to cloud-native open-source observability—cutting “hundreds of thousands of dollars” in licensing—exemplifies this shift.
AI-Powered Analysis
All major platforms are integrating AI for:
- Automatic anomaly detection
- Root cause analysis
- Alert correlation and noise reduction
- Natural language querying
Cost Optimization Focus
With observability costs reaching 10-30% of cloud spend for some organizations, cost efficiency is becoming a primary selection criterion. ClickHouse-based platforms and object storage integration are responses to this pressure.
Ready to Consolidate Your Observability Stack?
Choosing and implementing an all-in-one observability platform is a significant decision. The right choice depends on your scale, team expertise, budget, and specific requirements.
Our Prometheus consulting and Grafana consulting services help organizations:
- Evaluate observability platforms against your specific requirements
- Design unified observability architectures for cloud-native infrastructure
- Migrate from fragmented tooling to consolidated stacks
- Implement OpenTelemetry across applications and infrastructure
- Optimize observability costs while maintaining visibility
We’ve helped organizations reduce observability costs by 40-60% while improving mean-time-to-detection.