Fluentd has been a reliable workhorse for years, and its plugin ecosystem is still hard to beat. But let’s be real: by 2026, managing heavy Ruby dependencies in a modern microservices environment has become a bit of a headache. Most teams hit the same wall eventually-as soon as you scale up in Kubernetes or edge environments, Fluentd’s memory footprint starts to climb, and those configuration files quickly turn into unmanageable “spaghetti.” The good news is that the landscape has shifted. We now have high-performance, lightweight alternatives written in Rust or Go that handle logs, metrics, and traces without breaking a sweat. If you’re tired of fighting with resource overhead and complex deployments, it’s time to look at the tools that are actually built for today’s telemetry demands.
1. AppFirst
AppFirst simplifies infrastructure for applications by letting developers specify basic needs like compute resources, databases, networking, or a Docker image. The platform then automatically provisions the matching secure, cloud-native setup across AWS, Azure, or GCP, complete with IAM roles, secrets, and best practices baked in. No Terraform, CDK, or manual VPC fiddling required – it handles naming conventions, security boundaries, and multi-destination routing behind the scenes. Built-in logging, monitoring, and alerting come along for the ride, giving visibility without extra setup.
The approach targets teams frustrated with infra code or DevOps bottlenecks, so developers can focus purely on app logic. Multi-cloud stays consistent since the app definition doesn’t change when switching providers. Some find the hands-off provisioning refreshing for small-to-medium teams, though it assumes trust in the automated choices for compliance-heavy environments. Self-hosted deployment exists for those needing full control.
Faits marquants :
- Automatic provisioning of compute, databases, messaging, networking
- Built-in logging, monitoring, alerting
- Cost visibility tied to apps and environments
- Centralized auditing for infrastructure changes
- Options SaaS ou auto-hébergées
Pour :
- Removes infra coding entirely for developers
- Consistent multi-cloud experience
- Security and best practices enforced automatically
- Quick setup for shipping apps fast
Cons :
- Less customization than manual IaC tools
- Relies on platform’s choices for provisioning
- Observability limited to what’s built-in
- Not a dedicated log processor or collector
Informations de contact :
- Site web : www.appfirst.dev
2. Fluent Bit
Fluent Bit serves as a lightweight processor and forwarder for logs, metrics, and traces. It collects data from various sources, applies filters for enrichment, and routes the processed information to chosen destinations. The tool runs on multiple operating systems including Linux, Windows, macOS, and BSD variants. It uses a pluggable architecture and keeps a small memory footprint, usually around 450kb at minimum.
The design emphasizes asynchronous operations and efficient resource usage, which suits containerized setups, cloud environments, and even resource-limited devices like IoT hardware. Configuration stays straightforward with simple text files, and the project remains fully open source under the Apache License. Some users find the plugin system quick to pick up once they get past the initial learning curve, though debugging complex filters can feel a bit fiddly at first.
Faits marquants :
- Handles logs, metrics, and traces in one agent
- Supports Prometheus and OpenTelemetry compatibility
- Includes over 80 plugins for inputs, filters, and outputs
- Built-in buffering and error-handling mechanisms
- Stream processing with basic SQL-like queries
Pour :
- Extremely low CPU and memory consumption
- Fast deployment as a single binary with no external dependencies
- Works well in Kubernetes and edge scenarios
- Easy to extend with custom plugins
Cons :
- Smaller plugin ecosystem compared to some older alternatives
- Configuration syntax can get verbose for advanced filtering
- Less built-in transformation power for very complex parsing
Informations de contact :
- Site web : fluentbit.io
- Twitter : x.com/fluentbit
3. Vector
Vector functions as a high-performance pipeline for observability data. It collects logs and metrics from numerous sources, transforms them using programmable rules, and routes the results to a wide range of backends. Written in Rust, it ships as a single binary with no runtime dependencies, which makes installation and upgrades fairly painless across different platforms.
The pipeline model breaks down into sources, transforms, and sinks, allowing flexible compositions. It offers strong guarantees around data delivery and backpressure handling. Many find the remap language (Vector Remap Language) powerful for cleaning up messy logs, though it takes a few tries to get comfortable with the syntax. The project is open source and actively maintained by a community.
Faits marquants :
- Unified processing for logs and metrics
- Supports multiple configuration formats including YAML, TOML, and JSON
- Built-in support for end-to-end acknowledgements
- Deployable as agent, sidecar, or aggregator
Pour :
- Memory-safe and efficient runtime
- Clear documentation with many ready examples
- Vendor-neutral design
- Good handling of high-throughput scenarios
Cons :
- Steeper initial learning curve for the remap language
- Traces support still emerging
- Configuration files can grow lengthy for big pipelines
Informations de contact :
- Website: vector.dev
- Twitter: x.com/vectordotdev
4. Filebeat
Filebeat works as a lightweight shipper aimed at grabbing logs from files and pushing them to a central spot. It tails files in real time, reads new lines as they appear, and forwards events without much fuss. Built on the libbeat framework, it runs as an agent on hosts and handles interruptions by remembering where it stopped. Setup often involves pointing it at log paths and picking an output like Elasticsearch or Logstash.
People like how straightforward it feels for basic forwarding jobs, especially when paired with modules that auto-handle common formats and add parsing or dashboards. Configuration stays pretty minimal most of the time. Debugging can get annoying if a module doesn’t behave exactly as expected on weird log variations, though.
Faits marquants :
- Monitors and tails log files or locations
- Uses harvesters to read content line by line
- Supports modules for common sources with preconfigured paths and parsing
- Forwards to outputs like Elasticsearch or Logstash
- Remembers position after restarts or interruptions
Pour :
- Very low resource footprint on hosts
- Simple to install and configure for file-based logs
- Reliable at not dropping lines during issues
- Integrates smoothly with Elastic tools
Cons :
- Limited built-in processing compared to heavier tools
- Modules sometimes need tweaking for non-standard logs
- Not as flexible for non-file sources without extra work
Informations de contact :
- Site web : www.elastic.co
- LinkedIn : www.linkedin.com/company/elastic-co
- Facebook : www.facebook.com/elastic.co
- Twitter : x.com/elastic
5. Graylog
Graylog functions as a centralized log management platform that ingests, stores, searches, and analyzes logs. It supports various input types including syslog and application events, with pipeline rules for routing and basic processing. Data gets collected from sources, indexed for quick querying, and visualized through dashboards or alerts. Deployment works in cloud-hosted, on-prem, or hybrid setups with consistent behavior across them.
The platform includes built-in ways to manage costs like archiving and selective restore without extra charges for everything. Some find the search interface handy for digging through large volumes once set up, but initial input configuration can feel a bit scattered if coming from simpler shippers. It leans more toward full log ops than pure lightweight forwarding.
Faits marquants :
- Central ingestion and indexing of logs
- Pipeline management for routing and processing
- Search, dashboards, and alerting features
- Supports archiving with preview and selective restore
- Deployment options include cloud, on-prem, hybrid
Pour :
- Handles long-term storage without spiking costs unexpectedly
- Good for centralized search across many sources
- Built-in visualization and basic analysis tools
- Flexible inputs for different log types
Cons :
- Heavier setup for just forwarding compared to dedicated shippers
- Resource needs scale with indexed volume
- Pipeline rules can get complex to debug
Informations de contact :
- Site web : graylog.org
- Courriel : info@graylog.com
- Adresse : 1301 Fannin St, Ste. 2000 Houston, TX 77002
- LinkedIn : www.linkedin.com/company/graylog
- Facebook : www.facebook.com/graylog
- Twitter : x.com/graylog2
6. Splunk
Splunk serves as a platform for ingesting, indexing, searching, and analyzing machine data including logs. It collects from diverse sources in real time, parses formats as needed, and makes data queryable through a web interface. Forwarding often happens via agents that send to central indexers for processing and storage. The system supports hybrid or cloud deployments with broad integrations for logs alongside other data types.
Many use it in environments where deep search and correlation matter more than minimal forwarding. The interface gives solid control once data flows in, though getting everything tuned for high volume can involve some ongoing tweaks. Not the lightest option for edge collection.
Faits marquants :
- Ingests logs and other machine data from many sources
- Indexes for fast searching and analysis
- Supports real-time streaming ingestion
- Includes parsing and transformation during processing
- Works with forwarders for collection
Pour :
- Powerful search and visualization once set up
- Handles varied data formats well
- Good integrations across environments
- Scales for large ingestion volumes
Cons :
- Resource intensive on indexing side
- Forwarders add another layer compared to direct shippers
- Configuration for parsing can pile up quickly
Informations de contact :
- Site web : www.splunk.com
- Téléphone : 1 866.438.7758 1 866.438.7758
- Courriel : education@splunk.com
- Adresse : 3098 Olsen Drive San Jose, California 95128
- LinkedIn : www.linkedin.com/company/splunk
- Facebook : www.facebook.com/splunk
- Twitter : x.com/splunk
- Instagram : www.instagram.com/splunk
- App Store: apps.apple.com/us/app/splunk-mobile/id1420299852
- Google Play : play.google.com/store/apps/details?id=com.splunk.android.alerts
7. Cribl
Cribl operates as a central data engine focused on telemetry from IT and security sources. It onboards information from various places, then routes, transforms, reduces, or replays it before sending onward. The setup allows changes to fields, formats, or protocols along the way, acting like a middle layer for shaping flows. People often place it between sources and destinations to gain more control without adding agents everywhere.
Integrations cover many common tools, letting data move freely while applying adjustments. Deployment leans toward a central tier for handling the heavy lifting. Some appreciate the flexibility for tweaking pipelines on the fly, but configuring packs and schemas can feel a tad overwhelming when starting out on complicated routes.
Faits marquants :
- Central routing and shaping for logs, metrics, traces
- Transformation of fields, formats, protocols
- Reduction and replay capabilities
- Searching, storing, visualizing options
- Works without requiring new agents
Pour :
- Gives fine control over data flows in one spot
- Handles multiple telemetry types together
- Easy to adjust routes centrally
- Integrates with existing tools smoothly
Cons :
- Adds another layer that needs management
- Initial setup for transforms takes time
- Might overcomplicate simple forwarding jobs
Informations de contact :
- Website: cribl.io
- Phone: 415-992-6301
- Email: sales@cribl.io
- Address: 22 4th Street, Suite 1300, San Francisco, CA 94103
- LinkedIn: www.linkedin.com/company/cribl
- Twitter: x.com/cribl_io
8. rsyslog
rsyslog acts as a high-performance engine for collecting and routing event data on Linux systems. It ingests from files, journals, syslog sockets, Kafka, and other sources, then applies parsing, filtering, and enrichment using RainerScript or modules. Buffering uses disk-assisted queues for reliability during outages. Output goes to files, Elasticsearch, Kafka, HTTP, or similar endpoints.
The tool runs on single hosts or in containers with simple config files. Many stick with it for classic syslog forwarding plus modern pipeline needs. RainerScript gives decent control over rules, though complex parsing sometimes needs mmnormalize tweaks. It bridges old-school logging and newer data flows nicely in container setups.
Faits marquants :
- Ingests from files, syslog, journals, Kafka
- RainerScript for parsing, filtering, enrichment
- Disk-assisted queues for buffering
- Modules for inputs and outputs
- Docker-friendly deployments
Pour :
- Extremely fast and lightweight on resources
- Reliable with proven long-term use
- Flexible rules without heavy dependencies
- Easy quick starts on Linux
Cons :
- Configuration syntax takes getting used to
- Parsing complex formats needs extra modules
- Less native for non-Linux environments
- Documentation scattered across versions
Informations de contact :
- Website: www.rsyslog.com
9. NXLog
NXLog offers a telemetry pipeline platform for collecting, processing, and routing logs, metrics, and traces. It supports agent-based or agentless modes from wide OS versions and sources. Data gets reduced, transformed, enriched, then sent to SIEM, APM, or observability tools. Built-in storage handles retention for compliance or analysis.
The solution targets centralized log management with noise reduction for downstream systems. Many deploy it to optimize SIEM ingestion or monitor ICS/SCADA setups. Configuration stays agent-focused with policies for routing. It provides solid control over data flows, though managing agents across environments adds some overhead.
Faits marquants :
- Collects logs, metrics, traces from many sources
- Agent and agentless collection modes
- Reduction, transformation, enrichment features
- Routes to SIEM, APM, observability platforms
- Built-in storage for retention
Pour :
- Wide source support including legacy systems
- Helps cut SIEM noise and costs
- Good for compliance routing
- Flexible processing in one tool
Cons :
- Agent management needed for scale
- Not the lightest for simple forwarding
- Configuration can grow detailed
- Less emphasis on pure edge use
Informations de contact :
- Website: nxlog.co
- LinkedIn: www.linkedin.com/company/nxlog
- Facebook: www.facebook.com/nxlog.official
10. Grafana Loki
Grafana Loki handles log aggregation with a focus on storing and querying logs from applications and infrastructure. It indexes only labels attached to log streams instead of full text content, which keeps storage needs low and queries fast when filtering by metadata first. Logs get pushed from various clients in any format, with no strict ingestion rules. The system pairs well with Grafana dashboards for visualization and alerting based on log patterns.
Many run it alongside Prometheus for metrics, since the label-based approach feels familiar. Real-time tailing works nicely for live debugging sessions. Some note the simplicity shines in Kubernetes clusters where labels come naturally from pods. Parsing at query time adds flexibility but can slow things down if queries get too broad or complex.
Faits marquants :
- Indexes labels only for log streams
- Supports any log format at ingestion
- Integrates natively with Prometheus and Grafana
- Stores logs in object storage for durability
- Enables metrics and alerts from log lines
Pour :
- Keeps storage costs down with minimal indexing
- Easy to start with flexible ingestion
- Seamless switch between metrics and logs in UI
- Reliable for high-throughput writes
Cons :
- Query performance drops without good labels
- No full-text indexing means slower searches on content
- Relies on upstream agents for collection
- Formatting decisions pushed to query time
Informations de contact :
- Site web : grafana.com
- Courriel : info@grafana.com
- LinkedIn : www.linkedin.com/company/grafana-labs
- Facebook : www.facebook.com/grafana
- Twitter : x.com/grafana
11. Logz.io
Logz.io offers an observability platform centered on logs with extensions to metrics and tracing. It uses AI-driven insights for faster root cause analysis and automated anomaly detection. The system ingests telemetry, applies processing, and presents unified views with workflow navigation. Deployment includes cloud-hosted options with focus on quick recovery and reduced manual work.
Many use it for log-heavy environments where AI helps surface issues. Real-time alerts and correlations across signals feel handy for ops teams. Some appreciate the AI agent for natural queries on data. It leans more toward full observability than basic collection, with emphasis on intelligence over raw forwarding.
Faits marquants :
- Log management with AI insights
- Unified telemetry including metrics and traces
- Workflow-driven navigation and alerts
- Real-time AI for root cause and anomalies
- Cloud-based with generative AI features
Pour :
- AI speeds up troubleshooting noticeably
- Good at connecting logs to other signals
- Handles large-scale log ingestion
- Reduces manual digging with smart suggestions
Cons :
- More platform than lightweight collector
- AI features add complexity for simple use
- Relies on cloud hosting for full power
- Less focus on edge or agent collection
Informations de contact :
- Site web : logz.io
- Email: info@logz.io
- Adresse : 77 Sleeper St, Boston, MA 02210, USA
- Linkedin: www.linkedin.com/company/logz-io
- Twitter : x.com/logzio
12. OpenObserve
OpenObserve serves as an open-source observability backend for logs, metrics, and traces at scale. It ingests telemetry through standard protocols like OpenTelemetry, then stores and queries data with low overhead. The design prioritizes efficiency and cost control using columnar storage and compression. Setup works on single nodes or clusters, often with object storage for long-term retention.
Users note the performance holds up well for high-volume ingestion without heavy indexing. Querying stays fast thanks to smart partitioning. Some run it as a cost-effective alternative to managed services. It fits teams wanting self-hosted observability without big bills, though initial tuning for retention policies matters.
Faits marquants :
- Handles logs, metrics, traces in one system
- OpenTelemetry compatible ingestion
- Columnar storage for efficient queries
- Supports petabyte-scale with compression
- Fully open source under AGPL-3.0
Pour :
- Keeps costs low through smart storage
- Fast ingestion and query performance
- Easy self-hosting options
- No full-text indexing bloat
Cons :
- Needs good upfront config for scale
- Less mature ecosystem than older tools
- Query language has its own quirks
- Compression trades some flexibility
Informations de contact :
- Site web : openobserve.ai
- Adresse : 3000 Sand Hill Rd Building 1, Suite 260, Menlo Park, CA 94025
- LinkedIn : www.linkedin.com/company/openobserve
- Twitter : x.com/OpenObserve
13. SolarWinds
SolarWinds gathers logs alongside data from networks, infrastructure, databases, applications, and security into one unified monitoring system. Logs arrive through agents or agentless polling, get centralized, and correlate with other metrics or events for search and analysis. The platform supports searching, filtering, and linking logs to incidents to speed up troubleshooting. Deployment options include self-hosted for full control on your own infrastructure or SaaS for easier cloud management.
In real setups logs often serve as part of the bigger IT health picture, especially when problems span multiple layers. Some use it for compliance-driven log retention. The interface allows deep dives, but it leans more toward IT operations teams than developers who want quick app log parsing and debugging. AI features help spot anomalies in log patterns, though tuning them usually takes a few rounds of adjustment.
Faits marquants :
- Log collection via agents or agentless methods
- Centralization with other monitoring signals
- Search, filtering, and correlation capabilities
- Integration into incident response processes
- Choix de déploiement en auto-hébergement ou en mode SaaS
Pour :
- Connects logs to complete IT visibility
- Handles hybrid environments smoothly
- Useful for long-term compliance storage
- AI assists in noticing unusual log behavior
Cons :
- Logs are secondary to network and infra focus
- Agent installation adds some overhead
- Less depth for complex application parsing
- Can feel heavy if you only need basic forwarding
Informations de contact :
- Site web : www.solarwinds.com
- Téléphone : +1-855-775-7733
- Courriel : sales@solarwinds.com
- Adresse : 4001B Yancey Rd Charlotte, NC 28217
- LinkedIn : www.linkedin.com/company/solarwinds
- Facebook : www.facebook.com/SolarWinds
- Twitter : x.com/solarwinds
- Instagram : www.instagram.com/solarwindsinc
- Google Play: play.google.com/store/apps/details?id=com.solarwinds.app
- App Store: apps.apple.com/us/app/solarwinds-service-desk/id1451698030
14. SigNoz
SigNoz brings logs, metrics, and traces together in a single open-source observability platform built around OpenTelemetry. Logs flow in through the collector from various sources, get indexed, and become available for search, analysis, and correlation with other telemetry types. Everything lives in one dashboard that includes APM views, distributed tracing, customizable dashboards, error tracking, and alerting. The backend scales to handle large volumes without major issues.
It particularly helps when debugging distributed systems – a trace can immediately show related logs without switching tools. Self-hosting via Docker is straightforward for smaller setups, and a cloud version exists for those who prefer less infrastructure work. OpenTelemetry’s semantic conventions make queries consistent, but custom fields sometimes require extra mapping on ingestion. APM features track requests end-to-end and provide performance insights.
Faits marquants :
- OpenTelemetry-native handling of logs, metrics, traces
- Ingestion from multiple sources via collector
- Search and analysis with cross-signal correlation
- Configurable dashboards and alerting
- Self-hosted or cloud deployment options
Pour :
- Unifies different telemetry types in one place
- Scales reasonably well for production use
- Strong native OpenTelemetry support
- Open source keeps it flexible and cost-free
Cons :
- Depends on proper upstream instrumentation
- Custom queries and analysis need initial setup
- Dashboards start fairly basic
- Alert configuration takes some trial and error
Informations de contact :
- Site web : signoz.io
- LinkedIn : www.linkedin.com/company/signozio
- Twitter : x.com/SigNozHQ
Conclusion
Choosing a Fluentd replacement isn’t about finding a “perfect” tool; it’s about finding the one that stops causing you on-call alerts. If your main frustration is high CPU usage on your nodes, a lightweight binary is going to feel like a massive win. If you’re drowning in data costs, you’ll want something that can filter and “shape” your logs before they ever hit your expensive storage. In practice, many modern setups are moving toward a hybrid model: using tiny, efficient forwarders on the edge and a more robust processor in the middle. The bottom line is that your logging pipeline shouldn’t be the bottleneck of your infrastructure. If your current setup feels brittle or overpriced, it’s probably time to migrate. Test a few of these in a staging environment-you’ll likely find that observability doesn’t have to be this complicated.
