Open Source Splunk Alternatives

Elasticsearch is a distributed search and analytics engine, a scalable data store and a vector database, tuned for speed and relevance on production-scale workloads. It is the foundation of Elastic's open Stack platform and searches in near real time over very large datasets.

Data is reached through a REST API or one of the official language clients, which keeps it usable from any stack. Elastic publishes its machine learning work and its Lucene contributions through Search Labs, so the engine's direction is visible rather than opaque.

The same engine covers several jobs that are often bought separately.

• Full-text search: relevance ranking over documents at scale.
• Vector search: embeddings stored and queried alongside the rest of your data.
• Retrieval augmented generation: the retrieval half of generative AI applications.
• Observability: logs, metrics and application performance monitoring.
• Security analytics: security logs kept searchable for investigation.

You can create a managed deployment on Elastic Cloud or download and run it yourself. For a laptop, a start-local script brings up Elasticsearch and Kibana in Docker, generates a password and an API key, and writes them to an environment file. That setup leaves transport encryption off and binds to localhost, so it belongs in development only, and its one-month trial of all features reverts afterwards to the free and open Basic tier.

Grafana lets you query, visualize, alert on and understand your metrics no matter where they are stored. Dashboards are made to be created, explored and shared with a team, which is how a data-driven habit spreads beyond the person who built the first panel.

Panels come from plugins, so the ways to show metrics and logs keep growing, and template variables turn a dashboard into a reusable one with dropdowns at the top. A data source is chosen per query, so a single graph can mix sources, custom ones included.

The daily surface is compact.

• Visualizations: fast client-side graphs with many options, extended by panel plugins.
• Dynamic dashboards: template variables that make one dashboard serve many teams or environments.
• Explore metrics: ad-hoc queries and drilldown, with split view comparing time ranges, queries and sources side by side.
• Explore logs: switch from metrics to logs with label filters preserved, then search or stream them live.
• Alerting: define rules visually and let Grafana evaluate them continuously, notifying Slack, PagerDuty, VictorOps or OpsGenie.

Installation guides cover the usual platforms, and a public play instance lets you try it before installing anything. Grafana is distributed under AGPL-3.0-only, with Apache-2.0 exceptions documented separately, and the contributor path includes a developer guide, a style guide and beginner-friendly issues.

OpenObserve gathers logs, metrics, traces, analytics, real user monitoring and AI observability into one platform, offered as an alternative to Datadog, Splunk and Elasticsearch for teams that cannot carry their cost.

Storage is Parquet columnar files on an S3-native design, which the project credits for storage costs up to 140 times lower than Elasticsearch. It is written in Rust and ships as a single binary that is running in under two minutes, with partitioning, indexing and caching cutting the search space for most queries.

The platform is organized around signals rather than separate tools.

• Logs: full-text search, SQL queries, quick filters and a visual query builder over log data.
• Tracing: OpenTelemetry-powered waterfalls, flame graphs and Gantt charts across services.
• Metrics and dashboards: query metrics with SQL or PromQL and build dashboards on any signal.
• Alerts and incidents: alerting and incident tracking sit inside the same interface.
• Multi-tenancy: organizations and streams are first-class concepts with complete data isolation.

A stateless architecture backed by object storage keeps recovery objectives low, and a high availability mode clusters for petabyte workloads. The core is AGPL-3.0, with super cluster federation and federated search across regions reserved for the enterprise edition.

OpenSearch is an open source search and observability suite that brings order to unstructured data at scale. It is the engine underneath self-managed search and log analytics deployments, and the repository here holds that engine rather than the tutorials around it.

The README is deliberately a signpost. Downloads, installation instructions and the full feature documentation live on the project website and documentation site, while forums and a Slack workspace handle questions. Guides inside the repository cover contributing, maintainer and admin responsibilities, release management and testing.

A handful of facts matter before adopting it.

• License: Apache version 2.0, with copyright held by the OpenSearch Contributors.
• Lineage: the codebase includes certain Apache-licensed Elasticsearch code from Elasticsearch B.V., which is not the source of the remaining code.
• Stewardship: OpenSearch is a registered trademark of LF Projects, LLC, and project health is reported through Linux Foundation insights.
• Security reporting: potential vulnerabilities go by email to the OpenSearch security team rather than into a public issue.
• Community conduct: a code of conduct sets expectations for everyone taking part, with a contact address for questions.

Because the practical material sits outside the source tree, teams evaluating OpenSearch should start from the documentation site and the downloads page, then use the forums or Slack for the operational questions that follow.

Logstash is a data processing pipeline that runs on the server side, pulling from many sources simultaneously, transforming what arrives, and forwarding the result to wherever it should be stored. It replaces the collection of one-off scripts that usually accumulates between a log source and a search index.

It belongs to the Elastic Stack alongside Beats, Elasticsearch and Kibana, and Elasticsearch is the destination it is most often paired with. A pipeline is assembled from inputs, filters and outputs rather than written as transport code.

Extensibility is where most of the surface area lives.

• Over two hundred plugins: inputs, filters and outputs covering the sources and sinks most pipelines need.
• Self-contained gems: every plugin is a Ruby gem published to RubyGems and kept in its own repository under the logstash-plugins organization.
• Writing your own: the contributing guide covers developing and testing new plugins, which the project describes as straightforward.
• Separate issue tracking: plugin bugs and pull requests belong in that plugin's repository, while core work stays in this one.
• Packaged builds: officially released binaries plus Debian and RPM packages for the supported platforms.

Building from source needs JDK 21 with JAVA_HOME set, JRuby, rake and bundler, after which gradle tasks install the development and default gems. The repository holds both the Elastic-licensed X-Pack code and the OSS-licensed code, and an environment variable selects a build made only from the latter.

Quickwit is a search engine for observability data, logs and traces today with metrics on the roadmap. Its distinguishing decision is architectural. The index lives on cloud object storage and search runs against it directly, instead of against a fleet of stateful nodes holding local disks.

Getting there meant rebuilding the index data structures and IO paths. Compute and storage are decoupled, indexers and searchers are stateless, and queries return in under a second against Amazon S3, Azure Blob Storage or Google Cloud Storage. The project estimates this runs up to ten times cheaper than Elastic.

Compatibility is where Quickwit tries hardest to be undisruptive.

• Elasticsearch API: a large subset of the query DSL, endpoints and aggregations, usable from existing clients.
• OTEL native: OpenTelemetry ingestion for both logs and traces with no translation layer in front.
• Jaeger native: traces plug straight into Jaeger for distributed tracing workflows.
• Grafana data source: a dedicated plugin for querying Quickwit from existing dashboards.
• Schemaless or strict: index without declaring a schema or enforce one, with schemaless analytics either way.

Operationally it ingests natively from Kafka, Kinesis and Pulsar, supports multi-tenancy through many indexes and partitioning, applies retention policies, and runs delete tasks for GDPR requests. A Helm chart covers Kubernetes and everything is reachable over a REST API.

Migrating off Elasticsearch or OpenSearch is eased by an ES-compatible ingest API, so shippers such as Vector, Fluent Bit and Syslog keep working. A client that balks at a version check is satisfied with extra headers in the node configuration.

HyperDX helps engineers work out why production broke by making logs and traces searchable on top of any ClickHouse cluster. Think of it as Kibana pointed at ClickHouse, and it reads whatever schema is already there instead of demanding a new one.

It is a core component of ClickStack, which bundles ClickHouse, HyperDX, an OpenTelemetry collector, and MongoDB. One Docker command brings the whole set up and serves the interface on port 8080, while teams with an existing ClickHouse instance or a ClickHouse Cloud account can point HyperDX at that instead.

Searching is built for people who do not want to write SQL under pressure.

• Property search: full-text search alongside a short property syntax such as level:err, with SQL optional.
• Correlated signals: logs, metrics, session replays, and traces are searched and viewed in one place.
• Live tail: logs and traces stream in, so the freshest events are always on screen.
• Event deltas: anomalies are compared against normal traffic to show what actually changed.
• Alerts and dashboards: alerts take a few clicks, and high cardinality events chart without a query language.
• Terminal client: an hdx CLI offers a TUI with vim-style keys, ANSI charts, NDJSON output, and Drain pattern mining.

Instrumentation goes through OpenTelemetry, covering Kubernetes, JavaScript, Python, Java, Go, Ruby, PHP, .NET, Elixir, and Rust, with the collector listening on port 4318. Testing wants at least 4 GB of RAM and two cores. Code contributions now use a vouching model, where a maintainer sponsors a newcomer before a first pull request is reviewed.

Three observability signals usually mean three backends, three query languages and no way to join across them. GreptimeDB puts metrics, logs and traces on a single columnar engine over object storage, sharing one table model of tags, timestamp and fields, so signals sharing a service, host or trace identifier correlate in SQL.

Compute and storage are disaggregated. Object storage on S3, GCS, Azure Blob or an S3-compatible endpoint holds the data, while memory and local disk caches keep recent and frequently queried rows near compute. A standalone binary suits development, and the distributed mode splits into frontend, datanode, metasrv and an optional flownode for streaming computation.

Migration can move one signal at a time rather than all at once.

• Ingestion: OpenTelemetry, Prometheus Remote Write, Loki Push, Elasticsearch bulk, InfluxDB line protocol and gRPC.
• Query: SQL and PromQL, Jaeger-compatible trace queries, and the MySQL and PostgreSQL wire protocols.
• Built in: retention policies, downsampling, continuous aggregation, explicit table partitioning and inverted, skipping and fulltext indexes.
• Compatibility limits: LogQL and most Elasticsearch APIs are not covered, and PromQL has documented gaps.
• Edition boundary: read replicas, workload isolation and automated repartitioning belong to the commercial Enterprise build.

The Apache-2.0 core includes cluster deployment, object storage, the flow engine and every ingestion protocol listed above, though repartitioning, region migration and index creation are manual. A Docker image starts a standalone instance with a dashboard on port 4000, and building from source needs a nightly Rust toolchain, protobuf 3.15 or later and C and C++ build essentials.

Running Loki, Prometheus, Tempo and Pyroscope separately means four storage layers and four operational stories. Gigapipe, formerly known as qryn, implements all four sets of APIs itself, so logs, metrics, traces and profiles land in one lightweight stack that existing clients already know how to talk to.

Ingestion is deliberately promiscuous. OpenTelemetry is officially integrated and needs no changes to your instrumentation, while native ingestion also covers Loki, Prometheus, Tempo, Zipkin, Pyroscope, Influx, Datadog and Elastic. Storage sits on ClickHouse, DuckDB or GigAPI with S3 object storage, so the data stays under your control.

Querying works through the languages people already use.

• LogQL: the Loki API is implemented, so any Loki client or the Grafana Loki datasource works with no plugin.
• PromQL: Prometheus API compatibility is provided using WASM.
• TraceQL: Tempo query support covers traces alongside the other signals.
• Profiling API: profiling endpoints export DOT format for Graphviz, and OTLP profiles are ingested through a development endpoint.
• Index stats and tail: stream metadata queries return stream, entry, byte and chunk counts, and live tail takes limit and start parameters and reports dropped entries.

A built-in explorer and a CLI cover people without Grafana, and an OTLP over gRPC receiver takes traces, logs, metrics and profiles. Configuration, a Docker quickstart and cross-cluster setup are documented in the repository. Gigapipe presents itself as the community-powered alternative to vendor-controlled observability stacks, with a hosted free trial for anyone who wants to test before installing. Contributions require a CLA.