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Parseable was presented in a Show HN post as an open observability data lake that handles 100 million time-series events per minute. The company describes a cloud-native platform for logs, metrics and traces, but the supplied material does not explain the benchmark conditions or independently verify the throughput figure.
A Show HN post has introduced Parseable as an open observability data lake capable of processing a claimed 100 million time-series events per minute. The company describes a platform for collecting and querying logs, metrics and distributed traces, with data stored in open columnar formats on object storage; the supplied product material does not provide test conditions or independent validation for the throughput figure.
Parseable says the platform is designed for high-cardinality telemetry and uses a columnar data model and object-storage-native architecture. Its website describes a deployment process in which users deploy the software, connect an object store and configure telemetry agents. The company says the architecture is stateless and can run on public or private cloud infrastructure.
The platform combines logs, metrics and traces with dashboards, alerts, service maps, error pages and query tools. Parseable lists SQL and natural-language querying, access controls, forecasting and AI-assisted analysis among its features. Its product material also names PromQL, distributed queries and an AI interface as enterprise capabilities; the source does not specify which features are available in every edition.
Parseable lists self-hosted open-source, managed cloud and enterprise deployments, including bring-your-own-cloud options. It says the software integrates with telemetry agents, data sources, visualization tools, authentication systems and large language models. The website also promotes companion tools for connecting AI agents to telemetry, investigating data from Slack or a command line, and instrumenting Kubernetes workloads.
What the Throughput Claim Could Mean
Observability systems ingest operational data used to diagnose outages, track service performance and investigate security or reliability issues. A platform that can accept high volumes of telemetry while keeping data queryable could appeal to organizations that need to retain detailed records across many services. The stated rate of 100 million events per minute is therefore a prominent performance claim, though its practical meaning depends on event size, workload, hardware, retention and query demands.
Parseable’s emphasis on object storage and open formats speaks to two common infrastructure concerns: controlling storage costs and retaining ownership of telemetry. The company says its design avoids vendor lock-in and supports data sovereignty. Those are vendor-positioned benefits, not independently established outcomes in the material provided; buyers would need to assess compatibility, operating costs, governance and performance in their own environments.
The product’s combination of telemetry views and AI-related tools also reflects an effort to make operational data useful beyond conventional dashboards. Connecting assistants to live telemetry may simplify investigation, but the source gives no evaluation of accuracy, access boundaries or operational impact. These questions matter when systems can expose sensitive production data or influence incident response.
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Parseable’s Storage And Deployment Model
The source describes Parseable as a cloud-native observability platform that stores telemetry in open columnar formats on object storage. In this model, customers provide or select an object store and connect agents that send data. The company presents this as an alternative to keeping all telemetry within a proprietary observability service, while offering hosted and enterprise deployment options as well as self-hosting.
Parseable says it unifies logs, metrics and distributed traces in one platform, with SQL and natural-language interfaces for querying. Its listed integrations span telemetry collection, visualization, authentication and AI tools. The company also advertises security and governance features, and its website labels the service GDPR certified and SOC 2 Type II; the supplied material does not include certification records, audit scope or dates.
The Show HN framing is a product presentation rather than a reported benchmark study. The supplied source contains product descriptions and performance-related claims, but no test report, methodology, named customer deployment or third-party assessment. That distinction is important when comparing the headline rate with other observability systems.
““Cloud native, data lake architecture that scales as you go.””
— Parseable website
high throughput time-series database
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Benchmark Details Still Missing
The source does not explain how Parseable measured the claimed 100 million events per minute. It gives no event size, data shape, hardware configuration, number of concurrent writers, duration of the test, or comparison baseline. It is also unclear whether the figure refers to ingestion alone or to sustained performance while queries, retention and other workloads run.
No independent benchmark, customer case study or third-party verification is included in the supplied material. The source also does not provide pricing, edition-by-edition feature availability, retention limits or specific service-level commitments. Its security labels are presented without supporting audit details here. These points remain open for prospective users to verify directly with Parseable.
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What Users Can Verify Next
The next useful step is a reproducible benchmark or a detailed test report specifying workload, infrastructure and measurement method. That would help readers judge whether the headline throughput applies to their event mix and deployment conditions. Parseable has not provided a date for such documentation in the material supplied.
Teams considering the platform can also test it against their own telemetry sources and object-storage setup, then compare ingestion rates, query latency, total operating costs and data governance controls. The company lists self-hosted, managed and enterprise options, but the source does not say whether any product or benchmark updates are scheduled.
AI-powered telemetry analysis software
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Key Questions
What is Parseable?
Parseable describes itself as an open observability platform for collecting and querying logs, metrics and distributed traces. Its architecture uses object storage and open columnar formats, according to the company.
Is the 100 million events-per-minute figure independently verified?
The supplied source does not include independent verification or benchmark methodology. It should be treated as a company-presented performance claim until test details or third-party results are available.
How can Parseable be deployed?
The company lists self-hosted open-source, managed cloud and enterprise deployments, including bring-your-own-cloud options. It says users can connect an object store and configure telemetry agents.
Which observability data does it support?
Parseable says it brings together logs, metrics and distributed traces, with dashboards, alerts and query tools. The source does not provide a full compatibility matrix for every agent or data source.
What performance details are not yet available?
The supplied material does not state the event size, test hardware, workload, test duration or comparison baseline behind the throughput claim. It also does not say how performance changes when queries and other workloads run at the same time.
Source: hn
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