Introduction
OpenObserve is a strong option for teams that want unified observability without the cost and operational weight of traditional Elasticsearch-based logging stacks. It brings logs, metrics, traces, dashboards, alerts, RUM, pipelines, and LLM observability into one platform, with OpenTelemetry support and an object-storage-based architecture built for high-volume telemetry.
Its biggest strengths are simple ingestion-based pricing, flexible Cloud and self-hosted deployment options, SQL and PromQL support, and a storage model that can help reduce observability costs at scale. Instead of charging per host or per user, OpenObserve Cloud prices around ingestion, query volume, and retention, which makes cost planning easier for many teams.
This review breaks down OpenObserve pricing, Cloud and self-hosted plans, real cost scenarios, key cost drivers, user review themes, limitations to validate, and alternatives such as CubeAPM, Datadog, Grafana Cloud, New Relic, and SigNoz.
What Is OpenObserve?
OpenObserve is a unified observability platform designed to collect, store, search, and analyze telemetry data across logs, metrics, traces, dashboards, alerts, RUM, and LLM monitoring. Its documentation lists logs, metrics, traces, LLM monitoring, dashboards, alerts, pipelines, and RUM as product areas.
The platform is built around a modern storage model. In high-availability deployments, OpenObserve uses PostgreSQL for metadata and object storage such as Amazon S3, MinIO, or Google Cloud Storage for Parquet files. This architecture is central to its cost positioning because object storage is usually cheaper than index-heavy storage systems.
OpenObserve can be deployed as OpenObserve Cloud, self-hosted OpenObserve Enterprise, or the open-source edition. The project is open source, but the license is AGPL-3.0, not Apache 2.0. OpenObserve says it changed from Apache 2.0 to AGPL-3.0 in November 2023.
Key Features
OpenObserve brings logs, metrics, and traces into one platform. Its documentation describes log search with SQL and full-text queries, metrics from Prometheus-compatible sources, and distributed tracing for understanding request flow across services.
In high-availability mode, OpenObserve stores metadata in PostgreSQL and stores Parquet data files in object storage such as S3, MinIO, or GCS. This allows the platform to separate compute from storage and use cheaper object storage for telemetry data.
OpenObserve supports ingestion from OpenTelemetry Collector and other telemetry sources. Its docs list OpenTelemetry among supported ingestion sources, alongside Fluent Bit, Vector, Prometheus, Filebeat, Telegraf, Kubernetes, and other common sources.
OpenObserve supports SQL-style log analysis and Prometheus-compatible metrics workflows. This helps teams avoid learning a proprietary query language when they already use SQL or PromQL in their monitoring stack.
The platform includes dashboards, alerting, and pipelines for transforming or routing telemetry data during ingestion. These are listed in OpenObserve’s official documentation as core product areas.
OpenObserve also includes real user monitoring and LLM monitoring. Its documentation lists RUM for session replay and error tracking, and LLM monitoring for prompts, tokens, latency, and cost across LLM applications.
OpenObserve Pricing Options
OpenObserve pricing has changed, so older references to a 200 GB/month Developer Cloud tier should not be used. OpenObserve’s June 2025 pricing update said the company moved Cloud to a fully usage-based model and discontinued the Cloud free tier.
OpenObserve Cloud pricing
| Pricing item | Details |
| Cloud trial | 14 days |
| Ingestion | $0.50 per GB ingested |
| Query volume | $0.01 per GB queried |
| Metrics retention | 15 months included |
| Logs, traces, RUM, and other non-metrics retention | 30 days included |
| Extra non-metrics retention | $0.02 per GB per additional 30-day period |
| Users | Unlimited users, no per-seat charges |
| Dashboards and alerts | No stated limits on dashboards or alerts |
Self-hosted pricing
| Plan | Detail |
| Open-source edition | Free self-hosted edition |
| Self-Hosted Enterprise | Free up to 50 GB/day ingestion |
| Above 50 GB/day | Contact OpenObserve sales |
| Enterprise features | SSO, RBAC, federated search, query management, workload management, audit trail, and sensitive data redaction |
What Does OpenObserve Really Cost?
Disclaimer
These are directional editorial estimates based on OpenObserve’s public pricing. They are not official OpenObserve quotes. Actual costs depend on telemetry volume, query usage, retention settings, sampling, filtering, pipeline usage, deployment model, discounts, and contract terms.
OpenObserve pricing is mainly driven by telemetry ingestion, query volume, and retention. Unlike host-based APM tools, OpenObserve does not price Cloud usage by the number of hosts, CPU cores, or users. Its public pricing lists ingestion at $0.50/GB, query volume at $0.01/GB, 15-month metrics retention, and 30-day retention for logs, traces, RUM, and other non-metrics data. Extra non-metrics retention costs $0.02/GB for each additional 30-day period.
The scenarios below use OpenObserve Cloud monthly pay-as-you-go pricing. They do not include annual discounts, enterprise volume discounts, pipeline costs, AI feature usage after preview, extended retention beyond the default 30 days, or OpenTelemetry Collector infrastructure.
| Team size | Logs | Traces | Metrics | Total monthly ingest |
| Small team | 720 GB/month | 360 GB/month | 1 GB/month | ~1.1 TB/month |
| Growing team | 3.6 TB/month | 1.8 TB/month | 5 GB/month | ~5.4 TB/month |
| Mid-market team | 18 TB/month | 9 TB/month | 25 GB/month | ~27 TB/month |
Scenario 1: Small Team, Around 1.1 TB/Month
Situation
A small engineering team runs around 10 hosts across application services, a database, and supporting infrastructure. They need centralized log search, basic dashboards, distributed traces, alerting, and enough visibility to troubleshoot production issues without managing a larger observability stack.
Why teams at this stage consider OpenObserve
- They want logs, metrics, and traces in one platform.
- They want OpenTelemetry support without adopting a proprietary agent-first workflow.
- They want pricing based on ingestion instead of hosts or users.
- They need a lower-cost alternative to traditional log and APM platforms.
- They want the option to start with Cloud and later evaluate self-hosting if data control becomes important.
Estimated usage profile
| Configuration | Detail |
| Logs | 720 GB/month |
| Traces | 360 GB/month |
| Metrics | 1 GB/month |
| Query assumption | 1,081 GB queried/month |
Estimated monthly cost
Disclaimer: This is a directional editorial estimate based on AppSignal’s public pricing model and observed calculator behavior. It is not an official AppSignal quote.
| Component | Calculation | Monthly cost |
| Ingestion | $0.50 × 1,081 GB | ~$541 |
| Query volume | $0.01 × 1,081 GB | ~$11 |
| Default retention | Included | $0 |
| Total estimated cost | ~$552/month |
What this scenario shows
At small-team scale, OpenObserve stays predictable because the bill follows telemetry volume, not host count. Logs are the largest cost driver here, making up 720 GB of the 1.1 TB monthly ingest. If this team wants to reduce spend, the first place to look is log verbosity, trace sampling, and filtering low-value telemetry before ingestion.
Scenario 2: Growing Team, Around 5.4 TB/Month
Situation
A growing SaaS team runs around 50 hosts across application services, Kubernetes workloads, databases, background jobs, and customer-facing systems. The team needs log search, distributed tracing, shared dashboards, alerting, and enough query performance for daily troubleshooting.
Why teams at this stage evaluate OpenObserve more carefully
- Observability cost starts becoming visible in the engineering budget.
- Host-based pricing becomes harder to forecast as infrastructure grows.
- Logs and traces increase quickly as services, traffic, and deployments scale.
- Teams need better filtering, sampling, and retention planning.
- OpenTelemetry support becomes more important as the team avoids vendor lock-in.
Estimated usage profile:
| Configuration | Detail |
| Logs | 3,600 GB/month |
| Traces | 1,800 GB/month |
| Metrics | 5 GB/month |
| Query assumption | 5,405 GB queried/month |
Estimated monthly cost
Disclaimer: This is a directional editorial estimate based on AppSignal’s public pricing model and observed calculator behavior. It is not an official AppSignal quote.
| Component | Calculation | Monthly cost |
| Ingestion | $0.50 × 5,405 GB | ~$2,703 |
| Query volume | $0.01 × 5,405 GB | ~$54 |
| Default retention | Included | $0 |
| Total estimated cost | ~$2,757/month |
What this scenario shows: At growing-team scale, OpenObserve is still easy to model, but telemetry discipline matters more. Logs alone account for 3.6 TB/month, while traces add another 1.8 TB/month. The bill is not inflated by adding more engineers or hosts, but it will rise directly with noisy applications, unsampled traces, wider queries, and longer retention needs.
Scenario 3: Mid-Market Team, Around 27 TB/Month
Situation: A mid-market engineering team runs around 250 hosts across Kubernetes clusters, backend microservices, databases, queues, and customer-facing applications. Multiple teams rely on observability for production debugging, incident response, release validation, and service-level monitoring.
Why teams at this scale scrutinize OpenObserve costs:
- Observability becomes a serious monthly infrastructure line item.
- Logs and traces can grow faster than host count if applications are noisy.
- Query volume becomes important because wide searches across large datasets add cost.
- Retention planning matters, especially for audit, compliance, and long-running investigations.
- Teams must decide whether Cloud convenience is worth the cost compared with self-hosting.
Estimated usage profile:
| Configuration | Detail |
| Logs | 18,000 GB/month |
| Traces | 9,000 GB/month |
| Metrics | 25 GB/month |
| Query assumption | 27,025 GB queried/month |
Estimated monthly cost
Disclaimer: This is a directional editorial estimate based on AppSignal’s public pricing model and observed calculator behavior. It is not an official AppSignal quote.
| Component | Calculation | Monthly cost |
| Ingestion | $0.50 × 27,025 GB | ~$13,513 |
| Query volume | $0.01 × 27,025 GB | ~$270 |
| Default retention | Included | $0 |
| Total estimated cost | ~$13,783/month |
What this scenario shows
At mid-market scale, OpenObserve can still be easier to understand than host-based or seat-based platforms, but the monthly bill becomes meaningful. The main cost driver is ingestion, especially logs and traces. If the team extends retention from 30 days to 90 days for non-metrics data, that would add another $1,081/month because extra retention is priced at $0.02/GB for each additional 30-day period.
Optional 90-day retention add-on for Scenario 3
| Component | Calculation | Extra monthly cost |
| Extra retention | $0.02 × 27,025 GB × 2 extra 30-day periods | ~$1,081/month |
With 90-day non-metrics retention, the mid-market estimate would increase from about $13,783/month to about $14,864/month.
What This Cost Model Shows
OpenObserve pricing is easier to model than tools that combine host fees, user fees, indexed span fees, and separate signal-based charges. The main formula is simple: ingestion plus query volume plus any extra retention.
But the estimates also show one important point: ingestion-based pricing is only predictable when telemetry volume is controlled. Logs, trace volume, query behavior, and retention choices still need active governance. At 1.1 TB/month, OpenObserve looks lightweight. At 27 TB/month, the same pricing model becomes a serious budget item, so teams should run a proof of concept using real production telemetry before committing.
What Actually Drives OpenObserve Costs?
The biggest cost driver is the amount of telemetry sent into OpenObserve. Cloud ingestion is priced at $0.50/GB, so noisy logs, unsampled traces, verbose Kubernetes events, and high-volume application telemetry directly increase monthly cost.
OpenObserve Cloud also charges $0.01/GB queried. This matters for teams that run frequent wide-range searches across large log or trace datasets. Narrower time windows, better filters, and dashboard discipline can help reduce unnecessary query volume.
Metrics retention is included for 15 months. Logs, traces, RUM, and other non-metrics data include 30 days by default. Longer non-metrics retention costs $0.02/GB for each additional 30-day period.
The open-source and self-hosted options can reduce license spend, but they do not remove infrastructure costs. Teams still need to plan for storage, compute, upgrades, scaling, high availability, backups, and operational support.
OpenObserve supports OpenTelemetry ingestion, but production teams often run OpenTelemetry Collectors to batch, filter, enrich, sample, and route telemetry before it reaches the backend. That collector layer also needs compute, memory, networking, and operational care.
OpenObserve User Reviews
OpenObserve has a 4.9/5 rating from 15 Gartner Peer Insights reviews in the Observability Platforms category. This is a strong rating, but the sample size is still small compared with older observability vendors, so it should be treated as a useful early signal rather than a broad market benchmark.
What users praise
| Theme | Verified summary |
| Cost efficiency | Users often evaluate OpenObserve because it is positioned as a lower-cost alternative to tools like Datadog, Splunk, and Elasticsearch-based stacks. |
| Unified experience | OpenObserve combines logs, metrics, traces, dashboards, alerts, and other telemetry workflows in one platform. |
| OpenTelemetry support | The platform supports OpenTelemetry ingestion, making it easier for teams already moving toward vendor-neutral instrumentation. |
| Storage architecture | OpenObserve’s use of Parquet and object storage is a key part of its cost and scale story. |
| Query flexibility | SQL-style querying and Prometheus-compatible metrics workflows are useful for teams that do not want a proprietary query language. |
What users criticize or should validate
These points reflect review themes and product evaluation risks. They should not be treated as universal platform limitations.
| Area to validate | Why it matters |
| Platform maturity | OpenObserve is newer than Datadog, New Relic, Grafana, and Splunk, so teams should test workflows before committing. |
| Integration coverage | The ecosystem is growing, but it is not as broad as long-established platforms. |
| Trace workflows | Teams with complex distributed tracing needs should test trace search, navigation, and correlation during proof of concept. |
| Self-hosting overhead | Self-hosted deployments require planning for upgrades, scaling, storage, backups, and high availability. |
| Enterprise support needs | Larger teams should verify SLA, support, governance, security, and deployment requirements before buying. |
OpenObserve Alternatives
OpenObserve vs CubeAPM
OpenObserve is a strong option for teams that want a low-cost, OpenTelemetry-friendly observability backend with cloud and self-hosted options. CubeAPM is better positioned for teams that want OpenTelemetry-native APM with vendor-managed self-hosted deployment, stronger deployment control, and a simpler $0.15/GB ingestion-based pricing story.
| Category | OpenObserve | CubeAPM |
| Pricing model | $0.50/GB ingestion on Cloud, plus query and retention charges | Flat ingestion-based pricing starting at $0.15/GB |
| Deployment | Cloud, open-source self-hosted, self-hosted enterprise | Self-hosted in customer-controlled infrastructure, vendor managed |
| Best fit | Teams wanting cost-efficient unified observability with OSS flexibility | Compliance-sensitive teams wanting managed self-hosted APM and predictable pricing |
| Data control | Stronger with self-hosting; Cloud is vendor-managed | Telemetry can stay inside customer infrastructure |
| Operational overhead | Lower on Cloud, higher when self-hosted | Lower for teams that want self-hosted without managing everything themselves |
OpenObserve vs Datadog
Datadog is a mature enterprise observability platform with a broad product suite and a large integration ecosystem. The tradeoff is pricing complexity. Datadog’s public pricing includes host-based infrastructure pricing, and its pricing documentation covers many product-specific billing models across infrastructure, APM, logs, synthetics, RUM, security, and more.
| Category | OpenObserve | Datadog |
| Pricing model | Ingestion-based, plus query and retention | Multi-product pricing across hosts, logs, APM, metrics, RUM, synthetics, and other modules |
| Infrastructure pricing | No per-host charge on OpenObserve Cloud | Infrastructure Pro listed from $15/host/month on annual billing |
| Users | Unlimited users on Cloud plans | Pricing can vary by product and enterprise plan |
| Self-hosted option | Yes | No self-hosted Datadog backend |
| Best fit | Cost-conscious OTel-first teams | Enterprises needing broad SaaS observability and integrations |
OpenObserve vs Grafana Cloud
Grafana Cloud is built around the Grafana ecosystem and supports metrics, logs, traces, profiles, dashboards, and other observability workflows. Grafana’s pricing page lists a Free tier, Pro from $19/month plus usage, and Enterprise annual plans. The Pro plan includes 13 months of metrics retention and 30 days for logs, traces, profiles, and k6 performance tests.
| Category | OpenObserve | Grafana Cloud |
| Architecture | Unified platform with object storage and Parquet | Grafana Cloud services built around the LGTM ecosystem |
| Pricing model | $0.50/GB ingestion, query charges, retention charges | Base plan plus usage-based pricing across signals |
| Query model | SQL and PromQL | PromQL, LogQL, TraceQL, dashboards, and Grafana ecosystem tools |
| Self-hosting | Yes | OSS components can be self-hosted, Grafana Cloud is managed SaaS |
| Best fit | Teams wanting simpler unified storage and pricing | Teams already standardized on Grafana dashboards and LGTM workflows |
OpenObserve vs New Relic
New Relic is a mature full-stack observability platform with data-ingest pricing and user-based access tiers. Its pricing page lists 100 GB/month of free Original Data ingest, then $0.40/GB beyond the free limit for Standard and Pro, while Data Plus is listed at $0.60/GB beyond the free limit.
| Category | OpenObserve | New Relic |
| Pricing model | Ingestion, query, and retention | Data ingest plus user-based pricing |
| Free data | 14-day Cloud trial, no Cloud free tier | 100 GB/month free ingest |
| Users | Unlimited users on OpenObserve Cloud | User tiers affect cost and access |
| Self-hosted option | Yes | SaaS platform |
| Best fit | OTel-first teams focused on cost control and deployment flexibility | Teams wanting mature SaaS observability with broad product coverage |
OpenObserve vs SigNoz
SigNoz is an OpenTelemetry-native observability platform with cloud and self-hosted options. Its pricing page lists a Teams Cloud plan starting at $49/month, with logs and traces billed at $0.30/GB and metrics at $0.10 per million samples after the included amount.
| Category | OpenObserve | SigNoz |
| OpenTelemetry support | Yes | Yes |
| Storage approach | Object storage and Parquet | ClickHouse-based backend |
| Pricing model | $0.50/GB ingestion, query, and retention | Base cloud fee plus logs, traces, and metrics usage |
| Self-hosted option | Yes | Yes |
| Best fit | Teams wanting object-storage-based observability | Teams wanting ClickHouse-backed OTel observability |
Is OpenObserve the Right Choice?
When OpenObserve works best
OpenObserve is a strong fit for teams standardizing on OpenTelemetry, especially if they want logs, metrics, traces, RUM, dashboards, alerts, and LLM monitoring in one platform. Its docs confirm broad telemetry support and ingestion options across common sources.
It also works well for teams that want a more predictable pricing model than host-based or seat-based observability platforms. OpenObserve Cloud lists $0.50/GB ingestion, $0.01/GB query volume, and clear retention pricing for non-metrics data.
Self-hosted OpenObserve is also worth evaluating for teams that want more control over data residency and infrastructure. OpenObserve says users can migrate between Cloud and Self-Hosted deployments, with Cloud providing managed infrastructure and Self-Hosted giving more control over the deployment environment.
When OpenObserve may not be the right fit
OpenObserve may not be ideal for teams that want the most mature enterprise observability ecosystem out of the box. Larger teams should validate integrations, governance, support, trace workflows, and security controls before committing.
It may also require more operational planning if you choose the self-hosted path. Self-hosting gives more data control, but teams must manage infrastructure, scaling, upgrades, object storage, backup strategy, and high availability.
Finally, teams with strict long-term retention needs should model retention carefully. Metrics retention is included for 15 months, but logs, traces, RUM, and other non-metrics data include 30 days by default, with extra retention priced per GB per additional 30-day period.
Conclusion
OpenObserve is best understood as a cost-focused, OpenTelemetry-friendly observability platform for teams that want logs, metrics, traces, RUM, dashboards, alerts, pipelines, and LLM observability in one tool. Its biggest strengths are usage-based pricing, object-storage-backed architecture, SQL and PromQL support, and deployment flexibility across Cloud and self-hosted options.
The pricing is much cleaner than many traditional observability platforms, but it is not “free Cloud observability.” The old Cloud free tier has been discontinued, and current Cloud pricing is based on ingestion, query volume, and retention. Self-Hosted Enterprise is free up to 50 GB/day, but larger usage or support requirements need sales discussion.
For teams that want low-cost, OTel-native observability and are comfortable validating a newer platform, OpenObserve is a strong candidate. For teams that need deeper APM maturity, vendor-managed self-hosting, stricter deployment control, or simpler per-GB pricing, alternatives like CubeAPM may also be worth evaluating.
Disclaimer
This review is based on public OpenObserve pricing, documentation, GitHub/license information, and public review sources available as of May 2026. Pricing, feature availability, retention rules, support terms, and enterprise packaging can change, so buyers should verify current details directly with OpenObserve before making a purchasing decision.
FAQs
1. What is OpenObserve pricing in 2026?
OpenObserve Cloud pricing is usage-based. Current public pricing lists ingestion at $0.50/GB, query volume at $0.01/GB, 15-month metrics retention, 30-day non-metrics retention, and $0.02/GB for each additional 30-day non-metrics retention period.
2. Does OpenObserve have a free Cloud plan?
No current free Cloud tier is listed. OpenObserve says it discontinued the Cloud free tier in June 2025 and moved Cloud to usage-based pricing. The current Cloud plan includes a 14-day free trial with no credit card required.
3. Can OpenObserve be self-hosted for free?
Yes. OpenObserve has a free open-source edition, and its Self-Hosted Enterprise plan is free up to 50 GB/day ingestion. Usage above that level or additional enterprise support requires contacting sales.
4. Is OpenObserve Apache 2.0 licensed?
No. OpenObserve used to be Apache 2.0, but the project changed to AGPL-3.0 in November 2023. Older content that describes OpenObserve as Apache 2.0 should be corrected.
5. Does OpenObserve charge per host or per user?
OpenObserve Cloud does not charge per host or per seat. Its pricing page says Cloud includes unlimited users and charges mainly by ingestion, query volume, and retention.
6. How long does OpenObserve retain data?
OpenObserve Cloud includes 15 months of metrics retention and 30 days of retention for logs, traces, RUM, and other non-metrics data. Extra non-metrics retention costs $0.02/GB per additional 30-day period.