OpenObserve vs Supernova: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of OpenObserve and Supernova — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
OpenObserve
OpenObserve
Open-source unified observability for logs, metrics and traces, with an AI SRE agent that correlates signals and an LLM cost and eval monitor.
Key features
- Unified Telemetry Store: Holds logs, metrics, traces, RUM, session replay and error tracking in a single system instead of separate tools per signal type.
- Columnar Parquet Storage in Rust: Built on the DataFusion engine with no index to build, which underpins the claimed 140x storage and 30x compute reduction versus Elasticsearch.
- Autocorrelation Engine: Continuously pairs signals across frontend, API, application, database, network and infrastructure layers at over a million signals per second.
- AI SRE Agent: Investigates an incident by building a service graph, quantifying SLO and revenue impact, identifying the root cause from trace evidence, and applying a corrective action such as a rollback.
- Proactive Daily Briefing: Reviews every service over a rolling 14-day window and flags the ones degrading, with the deploy or change that coincided with the regression.
- Agentic and LLM Observability: Tracks token spend, per-model usage mix and error rates across models in production, with failed evaluations shown alongside prompt, output and grader score.
- Transparent Usage Pricing: Charges per GB ingested and per GB queried with retention included, rather than tiered seat or host licensing.
- Self-Hosted or Managed Cloud: The same platform can run entirely inside your own infrastructure or as a fully managed service, including BYOB for enterprise deployments.
Best for
- Cutting Observability Spend: Replace an Elastic or Datadog deployment while keeping a year of log retention, using far less storage and compute for the same data.
- Automated Incident Triage: Let the SRE agent correlate an error-rate spike to a specific deploy and propose the rollback before an engineer is paged.
- Monitoring LLM Applications in Production: Track token cost, model mix and evaluation failures across several models serving live traffic.
- Catching Slow Regressions: Surface a service whose p95 latency quietly tripled after an index rebuild, which threshold alerting would miss.
- Full-Stack Root Cause Analysis: Trace a checkout failure from the browser through the API and into the database on one correlated timeline.
- Compliance-Constrained Deployments: Self-host the whole observability stack so telemetry never leaves your own infrastructure.
- SLO Management: Measure which service level objectives an ongoing incident is putting at risk and how much of a user flow is affected.
Supernova
Supernova
An encrypted Iceberg data lake with a built-in engine and MCP endpoint, so Claude and Codex can query every tool your company uses.
Key features
- MCP Endpoint for Claude and Codex: Point any MCP-speaking assistant at mcp.supernova.ai/mcp and every synced table becomes queryable in natural language.
- Encrypted Iceberg Lake: Open Apache Iceberg tables in object storage with table-level encryption, so the data stays in a portable open format you control.
- Zero-Copy Connections: Any engine that speaks Iceberg can read the lake directly, avoiding a second copy of your warehouse.
- Time Travel: Every table retains version history, so you can query the state of your data as of any earlier point.
- Built-In Frontier Models: Ask a question or describe a dashboard in plain language and Supernova generates the models and visualisations without a data team.
- TypeSQL: Schema-aware SQL that autocompletes across joins and type-checks before execution, catching errors the way a typed language would.
- Single-Binary CLI: One command-line tool connects sources, runs queries, tails live table changes and registers the MCP endpoint with Claude Desktop, from a laptop or CI.
- Git-Backed Dashboards: Models and dashboards are readable and writable through Git, putting analytics artefacts under normal version control.
Best for
- Conversational Revenue Analysis: Ask Claude which customers churned last quarter and why, with the answer computed over live Stripe and HubSpot tables.
- Warehouse Cost Reduction: Replace a multi-vendor pipeline-plus-warehouse stack with one usage-billed platform, which the vendor illustrates as $5,640/mo dropping to $540/mo for a hardware company.
- Dashboards Without a Data Team: Describe the dashboard you want in a sentence and have the models and charts generated for you.
- AI-Native Data Access Layer: Give internal agents a governed, encrypted single endpoint for company data instead of per-tool API integrations.
- Auditing Historical State: Use table version history to reconstruct what the numbers looked like before a pricing or schema change.
- CI-Driven Data Workflows: Drive connections, queries and change tailing from pipelines using the single CLI binary.