Look, if you’re still wrestling with Logstash in 2025, you already know the feeling: another plugin breaks after an update, the JVM eats half your memory, and someone’s spending Friday night debugging filter syntax.
You didn’t sign up to become an ELK whisperer. You signed up to ship features.
Good news-there are now tools that handle logs without making you hate your life. Here are the alternatives real teams are switching to right now-and staying with.
1. AppFirst
AppFirst focuses on removing infrastructure code entirely, not on being a log shipper. Developers describe what their app needs (compute, database, queues) and the platform spins up compliant resources automatically across AWS, Azure, or GCP. Logs still flow out through normal channels, but the service itself does not provide a dedicated log management or observability stack.
It fits teams that want to ship features without writing Terraform or waiting on DevOps reviews, rather than teams hunting for a Logstash replacement. Observability stays up to whatever tools you already use; AppFirst just makes sure the underlying infra exists and stays secure.
נקודות עיקריות:
- Declarative app-centric provisioning
- Built-in security and compliance defaults
- Multi-cloud support (AWS, Azure, GCP)
- SaaS or self-hosted control plane
- No custom Terraform or CDK required
Pros:
- Developers own infra without writing it
- Enforces best practices automatically
- Instant environments, no PR reviews
- Works across major cloud providers
Cons:
- Not a log management or observability tool
- Still early-stage product
- Limited to supported resource types
- Requires trusting a new platform
פרטי קשר:
- אֲתַר אִינטֶרנֶט: www.appfirst.dev
2. Elastic
Elastic serves as a distributed engine for search and analytics, where logs fit right into its handling of structured and unstructured data. Developers pull in log streams alongside other inputs, letting the system parse and index them on the fly for quick retrieval. Pipelines within the setup allow for transformations like filtering or enriching entries before storage, all while keeping things indexed for later queries. The open-source core means setups can run without vendor lock-in, and it scales across nodes to manage growing volumes without much reconfiguration.
Beyond basic ingestion, the platform supports vector embeddings for logs tied to AI tasks, blending semantic search with traditional filters to spot patterns in noisy data. Real-time aggregation helps in breaking down high-volume streams into actionable summaries, and integrations pull from diverse sources without heavy custom coding. As part of a broader stack, it often pairs with tools for visualization, though the focus stays on efficient storage and fast lookups rather than end-to-end alerting.
נקודות עיקריות:
- Open-source foundation under Apache license for flexible deployment
- Ingest pipelines for parsing, transforming, and enriching logs
- Handles structured, unstructured, and vector data in one system
- Real-time indexing and search across distributed clusters
- Supports hybrid queries mixing full-text and vector methods
Pros:
- Scales horizontally for large log volumes
- Quick setup for basic log indexing
- Broad plugin ecosystem for inputs and outputs
- Efficient columnar storage reduces query times
Cons:
- JVM overhead can spike memory use
- Complex configs for advanced pipelines
- Relies on ecosystem tools for full observability
- Learning curve for optimization at scale
פרטי קשר:
- אתר אינטרנט: www.elastic.co
- לינקדאין: www.linkedin.com/company/elastic-co
- פייסבוק: www.facebook.com/elastic.co
- טוויטר: x.com/elastic
3. Better Stack
Better Stack pulls together observability with a focus on logs, using agents to scoop up entries from services without rewriting code. The system lets users sample data at query time or batch it for efficiency, storing everything in user-controlled buckets to skip vendor-managed tiers. Queries run via simple filters or SQL-like syntax, grouping similar patterns to cut down on noise, and dashboards visualize trends without deep scripting.
Tied into tracing and incidents, logs contextualize errors or slowdowns, with AI flagging outliers for review. eBPF probes map dependencies automatically, linking log spikes to network flows or database calls. Pricing kicks off free for lighter loads, then scales to paid plans where a terabyte of logs with thirty-day retention runs under a thousand bucks monthly, including sampling tools to trim irrelevant data.
נקודות עיקריות:
- eBPF and OpenTelemetry for code-free collection
- Query-time sampling and pattern grouping
- S3-compatible storage for direct access
- Integrates logs with traces and metrics
- Slack workflows for incident ties
Pros:
- Cost controls via spam marking and sampling
- Drag-and-drop dashboards for quick views
- Owns-your-data storage option
- Bundles observability in one interface
Cons:
- Relies on external buckets for long-term holds
- AI features still rolling out in phases
- Less mature for pure security workflows
- Query limits on free tier
פרטי קשר:
- Website: betterstack.com
- Phone: +1 (628) 900-3830
- Email: hello@betterstack.com
- LinkedIn: www.linkedin.com/company/betterstack
- Twitter: x.com/betterstackhq
- Instagram: www.instagram.com/betterstackhq
4. Fluentd
Fluentd acts as a collector that sits between log sources and storage backends, routing entries through a lightweight core. Plugins hook into apps or files for intake, then forward parsed data to outputs like databases or queues, keeping the middle layer straightforward. The design favors modularity, so swapping connections happens without rebuilding the whole flow, and it buffers bursts to avoid drops during peaks.
Community contributions keep the plugin count high, covering formats from JSON to syslog, and the setup runs on minimal resources compared to heavier engines. As a CNCF project, updates come from shared efforts, ensuring compatibility across cloud setups. Buffering and retry logic handle flaky networks, making it a steady choice for aggregating logs from scattered endpoints.
נקודות עיקריות:
- Plugin system for inputs, filters, and outputs
- Buffers data to manage throughput spikes
- Decouples sources from destinations
- Apache-licensed for open use
- CNCF graduated status for reliability
Pros:
- Low footprint on servers
- Easy plugin swaps for new sources
- Handles diverse log formats out of box
- Fault-tolerant with retries
Cons:
- Needs extra tools for search or alerts
- Plugin quality varies by contributor
- Config files can grow unwieldy
- Lacks built-in analytics layer
פרטי קשר:
- Website: www.fluentd.org
- Facebook: www.facebook.com/pages/Fluentd/196064987183037
- Twitter: x.com/fluentd
5. Splunk
Splunk ingests logs from clouds, on-prem, or apps through agents and APIs, normalizing formats for unified storage. The platform correlates entries across domains, applying rules to enrich or route them into searchable indexes. AI layers predict issues from patterns, while natural language queries pull insights without rigid syntax, and dashboards track metrics tied to log events.
As a Cisco acquisition, the system extends to security ops, blending log analysis with threat hunting via automated workflows. Scalability comes from distributed indexing, handling mixed data types without silos, though it leans on add-ons for niche integrations. Real-time streaming keeps views current, and anomaly detection flags deviations early in the pipeline.
נקודות עיקריות:
- 2000-plus integrations for broad ingestion
- AI-driven correlation and prediction
- Natural language search over logs
- Supports traces, metrics alongside logs
- Agentic workflows for response
Pros:
- Deep cross-domain analytics
- Handles any data source seamlessly
- Reduces alert fatigue with AI
- Extensible via apps and add-ons
Cons:
- Steep ramp-up for custom setups
- Higher resource needs for full features
- Vendor ecosystem can add costs
- Less flexible for open-source purists
פרטי קשר:
- אתר אינטרנט: www.splunk.com
- Phone: 1 866.438.7758
- דוא"ל: info@splunk.com
- כתובת: 3098 אולסן דרייב סן חוזה, קליפורניה 95128
- לינקדאין: www.linkedin.com/company/splunk
- פייסבוק: www.facebook.com/splunk
- טוויטר: x.com/splunk
- אינסטגרם: www.instagram.com/splunk
6. Graylog
Graylog centralizes logs for both security and operations use, pulling in data from servers, containers, and cloud services through standard inputs. The platform normalizes entries on arrival, routes them via pipelines, and stores everything in searchable indexes while letting users preview archived chunks without restoring full volumes. Built-in rules detect anomalies or threats, and investigations happen from a single interface that ties events to timelines.
Deployments run on-prem, in private clouds, or as managed service with identical features across options. Storage stays flexible – hot tiers for recent data, colder ones for older logs – and licensing avoids per-volume charges that surprise budgets. API security and compliance checks come baked in, making it a fit for shops that need SIEM capabilities alongside everyday log browsing.
נקודות עיקריות:
- Pipeline processor for routing and enrichment
- Archive preview without full restore
- On-prem or cloud deployment options
- Built-in anomaly and threat detection
- No ingest-based pricing surprises
Pros:
- Keeps costs predictable even with high volume
- Same experience across deployment types
- Handles security and ops in one tool
- Easy archive search and restore
Cons:
- Setup takes more steps than pure SaaS
- Search syntax has its own quirks
- Smaller ecosystem of pre-built integrations
- Resource usage grows with retention
פרטי קשר:
- אתר אינטרנט: graylog.org
- Email: info@graylog.com
- Address: 1301 Fannin St, Ste. 2000 Houston, TX 77002
- לינקדאין: www.linkedin.com/company/graylog
- פייסבוק: www.facebook.com/graylog
- טוויטר: x.com/graylog2
7. Sematext
Sematext ships logs, metrics, traces, and synthetic checks into one hosted platform that correlates everything automatically. Agents or OpenTelemetry endpoints feed data in, then dashboards mix logs with traces or frontend events without jumping between tools. Alerts fire from any signal, and anomaly detection spots odd patterns without writing rules for every case.
Pricing follows pay-as-you-go with a cap option that drops excess data instead of billing surprises. Retention and sampling adjust per source, and pre-built integrations cover common stacks so most setups start collecting within minutes. Mobile app logs and user journey tracking sit alongside server logs, giving a broader picture than pure log-only tools.
נקודות עיקריות:
- Combines logs, metrics, traces, and synthetics
- Pay-as-you-go with daily volume caps
- Pre-built dashboards for popular apps
- Correlation across signals out of box
- Mobile and frontend monitoring included
Pros:
- No overage shocks thanks to hard caps
- Quick setup for standard environments
- Ties logs directly to traces and RUM
- Flexible retention per data source
Cons:
- Hosted-only, no self-managed version
- Advanced queries need learning their syntax
- Smaller community compared to open tools
- Feature sprawl can feel busy at first
פרטי קשר:
- Website: sematext.com
- טלפון: 1-347-480-1610
- Email: info@sematext.com
- לינקדאין: www.linkedin.com/company/sematext-international-llc
- פייסבוק: www.facebook.com/Sematext
- טוויטר: x.com/sematext
8. Fluent Bit
Fluent Bit runs as a lightweight agent that gathers logs, metrics, and traces from hosts or containers, then forwards them wherever needed. Written in C, it keeps memory and CPU low even on edge devices, and the plugin model supports inputs like tail, systemd, or Prometheus scrapes. Filters enrich or trim data mid-flight, and backpressure handling prevents drops when destinations slow down.
Configuration stays in a single file, making rollouts via Kubernetes DaemonSets or systemd straightforward. Output plugins cover the usual suspects – Elasticsearch, Splunk, Kafka, cloud storage – and OpenTelemetry export works natively. Updates come frequently from the CNCF project, keeping it aligned with modern observability standards.
נקודות עיקריות:
- C-based for minimal resource use
- Native OpenTelemetry and Prometheus support
- Filters for parsing and modification
- Backpressure and retry built in
- Single config file approach
Pros:
- Runs almost anywhere, even constrained nodes
- Fast startup and low overhead
- Handles logs, metrics, traces uniformly
- Mature Kubernetes integration
Cons:
- No built-in storage or query layer
- Debugging misconfigured filters takes patience
- Limited UI – mostly config-driven
- Fewer filters than the older sibling
פרטי קשר:
- Website: fluentbit.io
- Twitter: x.com/fluentbit
9. Logit.io
Logit.io runs a managed platform that takes logs, metrics, and traces from any source through standard Beats, Fluentd, or OpenTelemetry inputs. Once data lands, it gets stored in dedicated Elasticsearch and OpenSearch clusters, with built-in cold storage for older logs that users can search without re-indexing everything. Dashboards and alerts come pre-configured for common stacks, and the service handles scaling, backups, and updates behind the scenes.
The whole setup lives in the cloud, either on shared clusters for smaller workloads or isolated ones when compliance needs kick in. Retention periods stretch as long as needed without the usual tiered pricing surprises, and the interface stays familiar to anyone who has used the ELK stack before. Support sits in the UK, which helps with European data residency questions.
נקודות עיקריות:
- Managed Elasticsearch and OpenSearch clusters
- Built-in cold storage with direct search
- Supports Beats, Fluentd, OTEL inputs
- Isolated or shared hosting options
- Pre-built dashboards for common apps
Pros:
- No cluster maintenance on your side
- Familiar Kibana-style interface
- Flexible retention without re-indexing cost jumps
- European hosting available
Cons:
- Fully hosted – no on-prem option
- Pricing scales with daily volume
- Less control over underlying cluster tuning
- Smaller ecosystem of niche integrations
פרטי קשר:
- Website: logit.io
- Email: sales@logit.io
- Twitter: x.com/logit_io
10. Atatus
Atatus offers a hosted observability service that includes log collection alongside traces, errors, and real-user monitoring. Logs flow in through agents or direct API pushes, then get parsed and linked to the matching transaction trace so jumping from a log line to the exact request takes one click. The search interface mixes structured filters with free-text, and alerts can trigger from log patterns or error spikes.
Everything runs as SaaS with a free tier for low-volume projects and paid plans that unlock longer retention and more sources. The same dashboard handles frontend, backend, and infrastructure signals, which keeps context switching low when chasing down issues.
נקודות עיקריות:
- Logs tied directly to transaction traces
- Includes RUM and error tracking
- Hosted with free tier available
- Single pane for logs, traces, metrics
- Agent and agentless collection options
Pros:
- Easy correlation between logs and traces
- Covers full stack in one tool
- Quick setup for supported frameworks
- Free tier covers small apps
Cons:
- SaaS-only deployment
- Retention limited on lower plans
- Less flexible for custom parsing needs
- Smaller footprint in pure log-heavy setups
פרטי קשר:
- Website: www.atatus.com
- Phone: +1-760-465-2330
- Email: success@atatus.com
- Address: No.51, 2nd Floor, IndiQube Alpine, Labour Colony, SIDCO Industrial Estate, Ekkatuthangal, Guindy, Chennai
- LinkedIn: www.linkedin.com/company/atatus
- Facebook: www.facebook.com/pages/Atatus/535723933196096
- Twitter: x.com/atatusapp
- Instagram: www.instagram.com/atatusapp
11. SigNoz
SigNoz provides an open-source observability platform built on OpenTelemetry collectors and clickhouse-backed storage. Logs, metrics, and traces land in the same backend, letting users run queries that span all three signals without exporting elsewhere. The UI mimics Jaeger for traces and adds log search with live tailing, while dashboards stay fully customizable.
Self-hosted installations give control over data location and cost, and the project stays active under Apache license. Community editions handle most workloads, with an optional cloud version for teams that prefer managed hosting. ClickHouse keeps query speeds reasonable even when retention stretches out.
נקודות עיקריות:
- Open-source with OpenTelemetry native collection
- ClickHouse storage for logs, metrics, traces
- Unified query across all signals
- Self-hosted or managed cloud options
- Live tail and trace-to-log linking
Pros:
- Full data ownership when self-hosted
- No vendor lock-in on collection
- Fast queries on large retention
- Active community contributions
Cons:
- Self-hosting requires ops effort
- ClickHouse tuning has a learning curve
- Fewer pre-built integrations than commercial tools
- Cloud version still maturing
פרטי קשר:
- Website: signoz.io
- LinkedIn: www.linkedin.com/company/signozio
- Twitter: x.com/SigNozHQ
12. OpenObserve
OpenObserve ships as an open-source tool focused on high-volume log, trace, and metric ingestion using a columnar store under the hood. Data gets compressed heavily on disk, and queries run directly on parquet files in object storage, which keeps costs down when retention grows. The interface offers log search, live tail, and basic dashboards, all accessible through a single binary or Docker setup.
Deployments stay lightweight compared to traditional ELK stacks, and the project targets environments where storage pricing matters. Rust components handle ingestion speed, and the whole thing runs on Kubernetes or bare metal without heavy dependencies.
נקודות עיקריות:
- Open-source with object-storage backend
- Heavy compression for long retention
- Single binary or container deployment
- Supports logs, traces, metrics
- Direct parquet query engine
Pros:
- Very low storage cost at scale
- Simple deployment footprint
- No separate search cluster needed
- Good for cold and hot data mix
Cons:
- Younger project – fewer polished integrations
- UI still catching up to mature tools
- Limited alerting features so far
- Manual scaling on Kubernetes
פרטי קשר:
- Website: openobserve.ai
- Address: 3000 Sand Hill Rd Building 1, Suite 260, Menlo Park, CA 94025
- LinkedIn: www.linkedin.com/company/openobserve
- Twitter: x.com/OpenObserve
13. Estuary
Estuary packs logs, metrics, traces, and profiles into one ClickHouse-backed store that works with existing agents. It speaks the same protocols as Loki, Prometheus, Tempo, and Pyroscope, so swapping it in usually means just changing an endpoint URL in Grafana or elsewhere. Everything lands in a single system instead of running separate silos, and the storage layer uses NVMe and DuckDB for queries that stay quick even when data piles up.
Open-source under AGPLv3, it runs wherever Docker or Kubernetes lives, and the pricing model stays flat instead of charging per gigabyte ingested. That setup appeals to folks who already lean on Grafana stacks but want fewer moving parts and predictable bills. Correlation between signals happens naturally since nothing gets split across different backends.
נקודות עיקריות:
- Drop-in compatible with Loki, Prometheus, Tempo, Pyroscope
- ClickHouse plus DuckDB query engine
- Single backend for all telemetry types
- AGPLv3 open-source license
- Flat-cost billing model
Pros:
- Works with existing Grafana data sources
- Fast queries thanks to columnar storage
- No separate components to manage
- Predictable cost regardless of volume
Cons:
- Still newer, smaller community
- Self-managed only for now
- Advanced features lag behind dedicated tools
- Requires comfort with ClickHouse tuning
פרטי קשר:
- Website: estuary.dev
- Address: 244 5th Ave, Suite 1277, New York, NY, 10001, US
- LinkedIn: www.linkedin.com/company/estuary-tech
- Twitter: x.com/EstuaryDev
14. CubeAPM
CubeAPM delivers managed observability that sits inside your own cloud account. Logs, traces, metrics, and infrastructure signals all flow into one place with retention that does not shrink unless you say so. The agents and collectors run in your VPC, so data never leaves your environment, yet the dashboards and storage get handled for you.
Setup leans toward teams that want SaaS convenience without sending raw logs outside their perimeter. The interface keeps things straightforward, and the pricing avoids the usual per-host or per-gigabyte surprises that catch people off guard.
נקודות עיקריות:
- Runs entirely inside customer cloud accounts
- Unlimited retention on logs and traces
- Managed control plane with customer data plane
- Covers APM, infrastructure, and logs
- Single-tenant isolation
Pros:
- Data stays in your own cloud
- No retention cutoffs on paid plans
- Less egress cost compared to public SaaS
- Simple pricing structure
Cons:
- Still requires some agent deployment
- Smaller integration catalog
- Newer player, fewer battle-tested stories
- Limited to supported cloud providers
פרטי קשר:
- Website: cubeapm.com
- LinkedIn: www.linkedin.com/company/cubeapm
- Twitter: x.com/CubeAPM
15. New Relic
New Relic offers a hosted observability platform that ingests logs alongside metrics, traces, and infrastructure data. Logs get parsed on ingest and become queryable through the same NRQL language used for everything else, so a single dashboard can mix log patterns with metric charts. The system ties errors and traces back to specific log lines when possible.
Everything runs as SaaS with a free tier that covers basic use and paid plans that open longer retention and more ingest. The agent ecosystem stays broad, and the UI leans toward pre-built experiences rather than raw query writing.
נקודות עיקריות:
- Unified NRQL queries across all data types
- Hosted with free tier available
- Automatic log parsing and enrichment
- Broad agent and integration support
- Built-in anomaly detection
Pros:
- One query language for everything
- Quick setup for supported languages
- Mature alerting and dashboard library
- Ties logs directly to traces and errors
Cons:
- SaaS-only deployment
- Pricing can climb with heavy ingest
- Less control over underlying storage
- Some features locked behind higher plans
פרטי קשר:
- אתר אינטרנט: newrelic.com
- Phone: (415) 660-9701
- Address: 1100 Peachtree St NE, Atlanta, GA 30309
- לינקדאין: www.linkedin.com/company/new-relic-inc-
- פייסבוק: www.facebook.com/NewRelic
- טוויטר: x.com/newrelic
- אינסטגרם: www.instagram.com/newrelic
Wrapping It Up
Logstash got a lot of us through the early days, but honestly, keeping it happy in 2026 feels like maintaining a vintage car: you can do it, but why would you when there are quieter, faster, cheaper rides that don’t leak memory or need a new plugin every other Tuesday?
The alternatives out there now cover every possible angle. Need something tiny that just ships logs without drama? It exists. Want a full-blown observability platform that ties logs to traces and still doesn’t bankrupt you at the end of the month? Also exists. Prefer to stay open-source and run everything yourself, or just throw a credit card at a managed service and forget about it? Both paths are solid these days.
At the end of the day, pick the one that gets out of your way the fastest. The right tool is the one you stop thinking about five minutes after you set it up, so you can go back to building the actual product instead of babysitting pipelines. Your logs deserve better, and so do you.
