Observability isn’t supposed to be this painful. But here you are: staring at bills that keep growing, drowning in useless logs, and juggling a pile of disconnected tools that barely work together. The promises were simple: visibility, control, insight. The reality feels more like chaos.
You don’t need another shiny pitch about “end-to-end visibility” or “single source of truth”. You need to know which tools are actually worth your time, your money, and your sanity.
This guide strips away the noise. We’ll walk through the major observability platforms, highlight what they do well, point out where they fall short, and give you the straight answers nobody else wants to say out loud.
By the end, you’ll have a clear view of what works, what doesn’t, and which option might finally help you get back to focusing on your actual job.
1. Dash0
Dash0 is a modern, OpenTelemetry-native observability platform. It’s built from the ground up on open standards, which means it’s designed for teams that use OTel and Prometheus and don’t want to be locked into a proprietary ecosystem.
It’s a fully managed service, so you get the benefits of a cohesive platform without the headache of running it yourself. The entire philosophy is about giving you control over your telemetry and costs without forcing you to compromise.
What’s good
- Absolutely zero lock-in. This is the big one. Dash0 is built on OpenTelemetry, PromQL, and Perses for dashboards. If you decide to leave, you take your configurations, dashboards, and alerts with you. Your instrumentation doesn’t change; you just point your OTel collector to a new endpoint.
- Transparent, predictable pricing. The cost model is dead simple: you pay a flat rate per million logs, spans, or metric data points you send. There are no extra charges for users, seats, data ingestion volume (GB), or querying. This approach encourages you to send rich metadata, not penalize you for it. They even have built-in spam filters to drop noisy, low-value telemetry before you get charged for it.
- One query language for everything. You use PromQL to query metrics, traces, and logs. You don’t have to learn LogQL, TraceQL, and a half-dozen other proprietary languages just to connect the dots. This massively reduces workflow friction and the learning curve for your team.
The catch
Dash0 is laser-focused on the modern, cloud-native stack. If your environment is full of legacy applications or you need a laundry list of obscure, non-standard integrations, a more established player might have more out-of-the-box coverage.
The platform is evolving quickly, but it doesn’t yet have every single feature of a behemoth like Datadog that’s been around for over a decade.
The verdict
If you’re a cloud-native team building on Kubernetes and committed to OpenTelemetry, Dash0 is the platform you’d design for yourselves.
It’s the most pragmatic and future-proof choice on this list. It delivers the insights you need without the vendor lock-in, surprise bills, or operational overhead. It respects your time and your budget.
Ready to see for yourself? Try Dash0 with a 14-day free trial and see what a no-nonsense, OTel-native platform can do.
[screenshot of Dash0’s unified PromQL query across logs and traces]
2. Datadog
Datadog is a massive, all-in-one SaaS platform that does everything: infrastructure monitoring, APM, logs, RUM, security, and more. If there’s a type of data you want to collect, chances are Datadog has a product for it.
What’s good
- Comprehensive feature set. Datadog has over 600 integrations and a feature for almost every conceivable use case. Its dashboards and analytics are mature and polished.
- Unified platform feel. For teams that want a single vendor to handle everything, Datadog provides a cohesive, if sprawling, experience.
The catch
- The cost is astronomical and unpredictable. This is the number one complaint you’ll hear from any Datadog user. The pricing model is a complex web of per-host fees, per-GB ingestion costs, indexed spans, and numerous add-on products. It’s incredibly difficult to forecast your bill, and it’s common for a small configuration mistake to blow your entire monthly budget in minutes.
- Deep vendor lock-in. Datadog is built around its proprietary agent and data formats. While they “support” OpenTelemetry, it’s treated as an input to be converted into their internal format. Moving off Datadog often means a complete re-instrumentation of your services.
- OTel is not native. You lose a lot of the rich context from OpenTelemetry when your data is mapped into a proprietary system. Attributes become simple key-value pairs, not the semantically rich data you need for deep analysis.
The verdict
Datadog is the default choice for large enterprises with deep pockets that want a single vendor to do it all and are willing to pay the “Datadog tax” for the convenience. For modern, cloud-native teams that value cost control and flexibility, the proprietary lock-in and shocking bills are a non-starter.
[screenshot of a Reddit thread complaining about Datadog pricing]
3. Honeycomb
Honeycomb pioneered the concept of observability for complex systems, focusing on high-cardinality, high-dimensionality event data. They are true masters of distributed tracing and excel at helping developers debug tricky production issues.
What’s good
- Best-in-class distributed tracing. Their “BubbleUp” feature is fantastic for identifying outliers and understanding patterns in massive datasets.
- Developer-first mindset. The tool is built by and for engineers who need to debug. The entire workflow is optimized for asking questions about your code’s behavior in production.
- Strong OpenTelemetry support. Honeycomb has embraced OTel and supports its native protocol, OTLP, for data ingestion.
The catch
- Trace-centric. While they have added support for logs and metrics, Honeycomb’s heart and soul is in events so the experience for other signal types isn’t as mature as their tracing capabilities.
- Not OpenTelemetry-native. Though it supports OTel, Honeycomb converts incoming data into its own event-based data model for storage and querying, making it not truly OpenTelemetry-native.
The verdict
If your primary pain point is debugging complex microservice interactions and you live and breathe distributed traces, Honeycomb is a good choice. But if you need a balanced, all-around platform for logs, metrics, and traces, you may want to look elsewhere.
4. New Relic
New Relic is another one of the old-guard APM vendors that has transitioned into a broader observability platform. They were one of the first to market with APM and have a mature product for monitoring application performance.
What’s good
- Strong APM capabilities. Their roots in Application Performance Monitoring are still evident. They provide deep code-level insights for a variety of languages.
- Simplified pricing (sort of). They moved to a simpler model based on data ingested and per-user seats. It’s easier to understand than Datadog’s labyrinth, but it has its own pitfalls.
The catch
- Per-user pricing is a collaboration killer. Charging per user discourages teams from giving everyone access to the observability platform. This creates knowledge silos and slows down incident response. True observability should be for everyone, not just those with a paid seat.
- Still a proprietary platform. Like Datadog, New Relic is a proprietary ecosystem. While they support OTel for data ingest, you’re fundamentally sending your data into their black box, making it hard to leave.
- Cost can still bite you. The per-GB ingest cost can add up quickly, forcing you to choose between visibility and your budget.
The verdict
New Relic is a capable APM and observability platform, and their pricing is a step up from Datadog’s complexity. However, the per-user pricing model is fundamentally at odds with the collaborative nature of modern DevOps and SRE teams. It’s a deal-breaker for organizations that want to foster a culture of ownership and shared understanding.
5. Grafana Stack (OSS or Cloud)
Grafana is the de facto standard for telemetry visualization. The Grafana Stack combines Grafana dashboards with Mimir for metrics, Loki for logs, and Tempo for traces. You can run the open-source (OSS) stack yourself or use their managed Grafana Cloud offering.
What’s good
- Built on open source. The stack is built around popular and powerful OSS projects. If you love Prometheus, you’ll feel right at home with Mimir and PromQL.
- Best-in-class visualization. Grafana is unmatched for creating beautiful, flexible, and data-rich dashboards.
- Active community. Being open source means there’s a huge community building integrations, dashboards, and providing support.
The catch
- It’s a “stack,” not a unified product. This is the critical flaw. Mimir, Loki, and Tempo are three separate systems with three separate query languages (PromQL, LogQL, and TraceQL). Correlating signals is a cumbersome process that relies on UI tricks rather than a unified data layer. This creates significant workflow friction.
- High operational burden (OSS). Running the full LGTM (Loki, Grafana, Tempo, Mimir) stack at scale is a massive undertaking. You are responsible for the storage, availability, and performance of three complex distributed systems. It’s a full-time job for a dedicated team.
- Grafana Cloud can be expensive. The managed service solves the operational burden but introduces its own cost complexities based on data volume, active series, and users.
The verdict
Grafana is a phenomenal tool for dashboards. However, the Grafana Stack is not a cohesive observability platform. The disjointed experience of using different query languages for different signals makes it a poor choice for teams that need to move fast during an incident.
If you have the engineering firepower to manage the OSS stack and can live with the fractured workflow, it’s an option. Otherwise, you’re better off with a truly integrated platform.
6. Dynatrace
Dynatrace is an enterprise-focused observability platform that leans heavily on AI and automation for root cause analysis. Its core value proposition is its “OneAgent” technology, which automatically discovers and maps all components and dependencies in your environment.
What’s good
- Automated analysis. Dynatrace’s AI engine, “Davis”, is very good at automatically identifying the root cause of problems in complex enterprise environments.
- All-in-one agent. The OneAgent simplifies data collection by bundling instrumentation for multiple technologies into a single deployment.
The catch
- The “black box” problem. The high degree of automation can make Dynatrace feel like a magic black box. When the AI is right, it’s amazing. When it’s wrong, it can be incredibly difficult to understand why.
- Proprietary and expensive. This is another classic enterprise tool with deep vendor lock-in. The OneAgent is proprietary, and OpenTelemetry support is grafted on, with limitations on how data is ingested and used. The pricing is complex and geared toward large enterprises.
- Overkill for many. The level of automation and the enterprise focus can be overkill for smaller, more agile cloud-native teams who prefer hands-on control and understanding of their systems.
The verdict
Dynatrace is built for large, traditional enterprises that want a highly automated, “set it and forget it” monitoring solution and are willing to pay a premium for it. If you want to understand your systems deeply and maintain control over your data and tools, the proprietary and opaque nature of Dynatrace is a significant drawback.
7. Elastic Observability
Built on the popular Elastic Stack (ELK), Elastic Observability extends the stack’s powerful logging capabilities with APM and infrastructure monitoring. If you’re already running Elasticsearch for logs, it’s a seemingly natural path to a full observability solution.
What’s good
- Powerful log search. Elasticsearch is the king of text search, and this power is at the core of Elastic’s logging solution.
- Unified stack. It offers a single platform for logs, metrics, and traces, leveraging Kibana for visualization.
The catch
- Not OTel-native. Like other incumbents, Elastic’s APM was built before OTel. OpenTelemetry is treated as just another data source to be mapped into Elastic’s data model. You lose the semantic richness and interoperability of a truly native OTel implementation.
- Complex and resource-intensive. Managing a large-scale Elastic Stack is notoriously complex and requires significant hardware and operational expertise. It consumes a lot of resources.
The verdict
If your organization is already heavily invested in the ELK stack for logging, using Elastic Observability is a viable, though compromised, path. However, it’s not an OpenTelemetry-native solution, and the operational overhead of managing the stack yourself is substantial. For teams starting fresh, there are more modern and efficient options.
8. Signoz
Signoz is an open-source, OpenTelemetry-native observability platform that positions itself as a direct alternative to giants like Datadog. It offers a unified solution for logs, metrics, and traces in a single application. You can either self-host the entire stack or use their managed cloud offering.
What's good
- Truly OTel-native and open source. Signoz was built from the ground up with OpenTelemetry at its core. This means it fully understands OTel's semantic conventions and data models, providing a rich, contextual experience. Being open-source means you're never locked in.
- Unified experience. Unlike the Grafana stack, Signoz provides a genuinely integrated experience for all three signals. You can seamlessly navigate from metrics to traces to logs without context switching or learning different query languages.
The catch
- Operational burden is high for self-hosting. While "free and open-source" sounds great, running the Signoz stack yourself means you are responsible for managing, scaling, and securing it. This includes its dependencies like ClickHouse, which can be complex to operate at scale.
- Still maturing. As a newer player, its feature set and UI/UX are still evolving compared to the decade-old incumbents. While it covers the core needs well, you might miss some of the polished workflows of more established tools.
The verdict
Signoz is a good choice for teams that want an open-source, OTel-native platform and have the engineering resources to manage it themselves. Its cloud offering is a strong, up-and-coming contender for those who want a managed service without the proprietary lock-in of the old guard.
9. Chronosphere
Chronosphere is the observability platform built for hyperscale. Founded by ex-Uber engineers who created the M3DB time-series database, it’s designed to handle massive volumes of metrics and trace data. It is OTel-native and focuses on providing a control plane to manage data growth before it gets stored.
What's good
- Built for extreme scale. If you're generating billions of active time series, Chronosphere is one of the few platforms that won't fall over. Its architecture is designed for reliability and performance in the most demanding environments.
- Proactive cost control. Its main differentiator is a control plane that allows you to analyze and shape your telemetry data at ingest time. This helps you tame data growth and control costs by dropping low-value data before you pay to store it.
The catch
- It's enterprise-grade and priced accordingly. Chronosphere is built for the top 1% of companies with massive data challenges. The platform's complexity and pricing are geared towards this market, making it prohibitively expensive and likely overkill for startups and most mid-sized companies.
- Metrics-first focus. While it supports traces, its core strength and origin lie in handling metrics at a massive scale. It might not be the most balanced platform if your primary needs are around logging or trace-based debugging.
The verdict
If you're a large enterprise like Uber and your biggest problem is the sheer volume and cost of your metrics data, Chronosphere is purpose-built for you. For everyone else, it's like using a Formula 1 car to go grocery shopping.
10. ServiceNow Cloud Observability (formerly Lightstep)
This platform started its life as Lightstep, one of the original pioneers in distributed tracing founded by engineers who worked on Google's Dapper. It was a major force behind the OpenTelemetry project. In 2021, it was acquired by ServiceNow, a massive IT Service Management (ITSM) company.
What's good
- Deep distributed tracing DNA. The platform's heritage shines through in its tracing capabilities. It's excellent at analyzing complex traces in large-scale systems and was one of the first to truly embrace high-cardinality data.
- Strong commitment to OpenTelemetry. As one of the co-founders of the project, their support for OTel is deep and genuine.
The catch
- It's part of the ServiceNow blob. The acquisition fundamentally changed its trajectory. The focus is now on integrating with the broader ServiceNow ITSM ecosystem, which appeals to large enterprises but can feel like corporate bloat for agile teams.
- Enterprise focus and pricing. Being part of ServiceNow means it's now aimed squarely at large enterprise customers. The pricing and product direction reflect this, potentially alienating the smaller, developer-focused teams that were its original audience.
The verdict
The underlying technology is still solid, especially for teams with complex tracing challenges. However, it's now best suited for large enterprises already invested in the ServiceNow ecosystem. If you're looking for a nimble, developer-first tool, the ServiceNow mothership might feel like a step in the wrong direction.
11. OpenObserve
OpenObserve is an open-source observability platform designed for performance and efficiency. Written in Rust, it ships as a single binary and aims to drastically reduce the storage and computational resources required to handle logs, metrics, and traces.
What's good
- Highly efficient. Its main claim to fame is performance. By using Rust and building its own data store, it claims to offer 10x lower storage costs and high ingestion performance compared to stacks like Elasticsearch.
- Simple deployment. Being a single binary simplifies the deployment process compared to managing a complex distributed stack with multiple components and dependencies.
The catch
- It's very new. OpenObserve is an early-stage project. It's promising, but it lacks the battle-tested stability, feature depth, and polish of more mature platforms. The ecosystem of integrations and community support is still in its infancy.
The verdict
OpenObserve is an exciting project for early adopters and teams that prioritize resource efficiency above all else. Its performance claims are impressive, but adopting it today is a bet on its future potential. It's a tool to watch, but not yet a mainstream contender for most production workloads.
Final thoughts
Choosing between observability tools in 2025 comes down to a simple question: Do you want a tool built for the past or the future?
The old guard—Datadog, New Relic, Dynatrace—built powerful platforms for a pre-cloud-native world. They are now trying to bolt on support for OpenTelemetry, but it’s not in their DNA. The result is a compromised experience with vendor lock-in, confusing pricing, and data models that strip away the rich context that makes OTel so valuable.
The future of observability is open, composable, and cost-effective. It’s built on standards like OpenTelemetry and PromQL and doesn’t force you into a proprietary black box or punish you with a surprise bill for trying to understand your own systems.
When you look at the landscape through that lens, the choice becomes much clearer. Give Dash0 a try and see the difference an OTel-native platform makes.
