Best Datadog Alternatives for Log Analytics (2026) — Cut Your Bill by 90%

Updated: February 2026 | Reading time: 16 min

Introduction

Datadog is a powerful observability platform. It is also one of the most expensive. If you have ever opened a Datadog invoice and felt your stomach drop, you are not alone. Engineering teams across the industry are actively evaluating Datadog alternatives because per-GB ingestion fees, per-host charges, and per-metric costs compound into bills that rival the cost of the infrastructure being monitored.

The root cause is architectural. Datadog charges premium rates for ingesting, indexing, and retaining your log data on their proprietary infrastructure. But in 2026, there are alternatives that store the same data on S3 object storage for $0.023/GB/month — and deliver full MELT observability (logs, metrics, events, traces) alongside it.

This guide breaks down the real cost math behind Datadog's log management pricing, then evaluates seven alternatives that can cut your bill by 60-90% without sacrificing query performance or operational capability. Each tool is evaluated on architecture, pricing, query language, OpenTelemetry support, and production readiness.

TL;DR: Datadog charges ~$0.10/GB ingestion + ~$1.70/GB indexing for log management. At 500GB/day, that is over $600,000/year in log costs alone. Parseable stores the same data on S3 in Apache Parquet format for ~$0.023/GB/month — a 90%+ reduction. It also delivers full MELT observability (not just logs), deploys as a single Rust binary, and queries with standard SQL.

The Datadog Cost Problem: Real Math

Before evaluating alternatives, you need to understand exactly how Datadog's log pricing works and why it escalates so aggressively. Datadog's log management has three distinct cost components that stack on top of each other.

Datadog Log Pricing Breakdown

The critical insight: ingestion and indexing are separate charges. You pay $0.10/GB just to get the data into Datadog, then pay again for every million events you want to actually search. Most teams index the majority of what they ingest, which means the effective cost per GB is far higher than the $0.10 headline number suggests.

What Datadog Actually Costs at Scale

Here is the math for log management alone (excluding infrastructure monitoring, APM, and custom metrics):

Indexing estimate assumes ~1 million events per GB (industry average for structured JSON logs). Actual costs vary by log format and event size.

Now add the infrastructure and APM costs that most teams also run alongside log management:

These numbers explain why "reduce datadog bill" is one of the fastest-growing search queries in the DevOps community. The platform is excellent, but the economics break down at scale.

The Hidden Cost Multipliers

Beyond the published rates, several factors inflate Datadog bills beyond initial estimates:

Custom Metrics Explosion: Kubernetes labels, Helm chart annotations, and auto-instrumented application tags generate custom metrics automatically. Teams regularly discover 10x more custom metric series than they expected, each billed at $0.05/month.

Retention Pressure: 15-day default retention is insufficient for most compliance and debugging use cases. Extending to 30 days doubles the indexing cost. 90-day retention multiplies it further.

Log Volume Growth: Application teams add services, increase log verbosity during incidents, and rarely remove log sources. Year-over-year log volume growth of 30-50% is common, creating compounding cost increases.

Per-Host Sprawl: Auto-scaling environments, ephemeral containers, and serverless functions each count as billable entities. A Kubernetes cluster that scales between 20 and 100 nodes during traffic spikes is billed at peak.

Quick Comparison: 7 Datadog Alternatives for Log Analytics

1. Parseable — S3-Native Unified Observability (Best Overall)

Best for: Teams that want full MELT observability at 90%+ lower cost than Datadog, deployed as a single binary with zero infrastructure complexity.

Parseable is a unified observability platform built in Rust that stores all telemetry data — logs, metrics, events, and traces — directly on S3-compatible object storage in Apache Parquet format. It is not a logging tool with metrics bolted on; it is a full MELT observability platform that competes directly with Datadog on feature scope while delivering fundamentally different economics.

Architecture: Why the Cost Difference Is Real

Datadog's pricing premium comes from maintaining proprietary ingestion pipelines, indexing infrastructure, and retention systems — all running on compute-heavy clusters in their data centers. You pay for that infrastructure indirectly through per-GB and per-host fees.

Parseable eliminates that entire layer. A single Rust binary handles ingestion, transformation, and writes data directly to S3-compatible object storage in columnar Apache Parquet format. The only infrastructure dependency is an object store — AWS S3, GCS, Azure Blob, MinIO, or any S3-compatible backend.

Key architectural properties:

• Single binary, <50MB RAM footprint: Download one binary and run. No JVM, no cluster managers, no sidecar containers
• S3-native storage: All telemetry stored in Apache Parquet on object storage. Storage cost = S3 rates (~$0.023/GB/month)
• Built in Rust: Sub-second query performance with minimal resource consumption. Handles thousands of events per second on a single node
• Apache Arrow DataFusion query engine: Columnar execution engine for fast analytical queries over Parquet data
• High availability: Cluster mode with distributed ingestion and smart caching for production-grade deployments

The Cost Math: Parseable vs Datadog

This is the comparison that matters. Here is a side-by-side cost breakdown for log management at three common volume tiers:

At 100GB/day:

At 500GB/day:

At 1TB/day:

Parseable storage estimates assume 80-90% Parquet columnar compression (typical for structured/semi-structured log data). Compute costs assume AWS EC2 instances sized for the ingestion rate.

The savings are not a rounding error. At 500GB/day, you save over $300,000 annually. That is engineering headcount, infrastructure investment, or product development budget that was previously going to an observability vendor. For a deeper breakdown of where Datadog costs accumulate, see our Datadog log management cost analysis.

Full MELT Observability — Not Just Logs

Parseable is not a Datadog alternative for logs only. It delivers full MELT observability:

• Logs: High-throughput ingestion, real-time tail, SQL-based search and aggregation
• Metrics: Time-series metrics stored in the same Parquet format, queryable with the same SQL interface
• Events: Structured event ingestion for tracking deployments, config changes, incidents
• Traces: Distributed tracing with native OTLP ingestion, span search, and trace-to-log correlation

This means you can replace Datadog entirely — not just its log management module. One platform, one query language, one bill.

SQL Query Interface

Parseable uses standard SQL as its query language, powered by Apache Arrow DataFusion. No proprietary syntax, no vendor-specific functions, no certifications required.

-- Find top error-producing services in the last hour
SELECT service_name, level, COUNT(*) as error_count
FROM application_logs
WHERE level = 'error'
  AND p_timestamp > NOW() - INTERVAL '1 hour'
GROUP BY service_name, level
ORDER BY error_count DESC
LIMIT 20;
 
-- Correlate slow traces with their log context
SELECT t.trace_id, t.duration_ms, l.message, l.level
FROM traces t
JOIN application_logs l ON t.trace_id = l.trace_id
WHERE t.duration_ms > 5000
  AND t.p_timestamp > NOW() - INTERVAL '30 minutes'
ORDER BY t.duration_ms DESC;

Every engineer on your team already knows SQL. There is no SPL to learn, no NRQL to memorize, no LogQL to parse. Onboarding friction drops to zero.

Native OTLP Endpoint

Parseable provides a native OpenTelemetry Protocol (OTLP) endpoint over both HTTP and gRPC. Point your existing OpenTelemetry Collectors at Parseable and start ingesting immediately. No proprietary agents, no Datadog-specific libraries, no vendor lock-in at the instrumentation layer.

AI-Native Analysis

Parseable integrates with Claude and other LLMs for natural language log queries. Ask questions in plain English — "show me all 5xx errors from the payment service in the last hour" — and get SQL queries generated automatically. This is particularly valuable for on-call engineers who need answers fast without remembering exact SQL syntax at 3 AM.

Deployment Options

Parseable Cloud is the fastest way to get started — a managed observability platform starting at $0.37/GB ingested ($29/month minimum). Sign up at app.parseable.com and start ingesting in under a minute.

For teams that need full infrastructure control, Parseable is also available as a self-hosted deployment with source code on GitHub:

• Single binary: curl https://sh.parseable.io | sh — running in under 60 seconds
• Docker: Single container, no orchestration required for development and staging
• Kubernetes: Helm chart for production cluster deployments

2. SigNoz — OpenTelemetry-Native Monitoring

Best for: Teams committed to OpenTelemetry that want a Datadog-like UI without Datadog-like pricing.

SigNoz is an open-source observability platform built natively on OpenTelemetry and ClickHouse. It provides unified logs, metrics, and traces with a modern UI that makes the transition from Datadog feel familiar.

Architecture

SigNoz uses ClickHouse as its storage and query backend. ClickHouse's columnar architecture delivers fast aggregations and good compression for time-series observability data. The platform accepts data exclusively through OpenTelemetry protocols, which enforces a clean, vendor-neutral instrumentation strategy from day one.

Strengths

• OpenTelemetry-first: OTel is not an integration — it is the only way data enters SigNoz. This ensures clean, portable instrumentation
• Modern UI: Service maps, flame graphs, and trace-to-log correlation that rival commercial platforms
• Unified signals: Logs, metrics, and traces in a single interface with built-in correlation
• Active community: Regular releases, responsive maintainers, and a growing ecosystem

Limitations

• ClickHouse operational overhead: Running ClickHouse at scale requires expertise in sharding, replication, and resource sizing. This is not S3-simple storage — it is a database cluster that needs care and feeding
• SigNoz Cloud pricing: At $0.3/GB for logs, SigNoz Cloud is cheaper than Datadog but still charges per-GB ingestion. At 100GB/day, that is ~$110,000/year for the cloud offering — better than Datadog's $195,000+ but far from the $3,000-$10,000 range that S3-native platforms achieve
• Self-hosted ClickHouse storage costs: Even self-hosted, ClickHouse runs on SSDs and EBS volumes, not S3 object storage. Storage costs are 5-10x higher than S3 for the same data volume
• Smaller ecosystem: Fewer pre-built integrations and dashboards compared to Datadog or Grafana

Cost

Free and open source for self-hosted deployments. SigNoz Cloud starts at $0.3/GB ingested for logs with separate pricing for metrics ($0.1 per million samples) and traces ($0.3/GB). At 100GB/day of logs on SigNoz Cloud, expect approximately $110,000/year. Self-hosted deployments reduce this to $25,000-$40,000/year in ClickHouse infrastructure costs, depending on retention and cluster sizing.

3. Grafana Cloud — Best for Existing Grafana Users

Best for: Teams already invested in the Prometheus/Grafana ecosystem that want to add log management without switching platforms.

Grafana Cloud provides a managed observability stack built on Grafana's open-source projects: Loki for logs, Mimir for metrics, Tempo for traces, and Grafana for visualization. It offers the most generous free tier in the industry and integrates seamlessly with existing Prometheus deployments.

Architecture

Grafana Loki, the log backend, takes a fundamentally different approach from Datadog: it indexes only metadata labels, not full log content. This dramatically reduces storage and compute requirements but trades off full-text search speed. Logs are stored on object storage (S3, GCS) in compressed chunks, making long-term retention affordable.

Strengths

• Free tier: 50GB logs, 10K metrics series, and 50GB traces per month — genuinely useful for small teams and development environments
• Best dashboarding: Grafana's visualization capabilities are unmatched in the observability ecosystem
• Object storage backend: Loki stores data on S3/GCS, keeping long-term retention costs low
• Ecosystem momentum: Massive community, plugin catalog, and battle-tested at enormous scale

Limitations

• Three query languages: PromQL for metrics, LogQL for logs, TraceQL for traces. Context-switching between three proprietary DSLs slows investigation workflows
• Limited full-text search: Because Loki does not index log content, searching for arbitrary strings across large time ranges can be slow compared to indexed platforms
• Multi-component complexity: Running the full stack (Loki + Mimir + Tempo + Grafana) self-hosted means managing four separate systems with different scaling characteristics
• Grafana Cloud costs at scale: While the free tier is generous, paid tiers at high volume ($0.50/GB for logs) approach commercial pricing. At 500GB/day, log costs alone exceed $90,000/year

Cost

Free tier with 50GB logs/month. Paid plans start at $0.50/GB for logs beyond the free tier. At 100GB/day, Grafana Cloud log costs run approximately $18,000-$22,000/year — significantly cheaper than Datadog but with the trade-off of limited full-text search capability. Self-hosted Loki can reduce this further but adds operational complexity.

4. New Relic — Best for APM-First Teams

Best for: Teams where application performance monitoring is the primary use case and log management is secondary.

New Relic is a full-stack observability platform with deep roots in APM. Its generous free tier (100GB/month, one full-platform user) makes it an accessible starting point, and its unified data model (NRDB) provides genuine cross-signal correlation.

Architecture

New Relic stores all telemetry in its proprietary New Relic Database (NRDB), a column-oriented distributed database designed for real-time analytics. All signal types — logs, metrics, traces, events — share the same backend, enabling cross-signal queries without data movement.

Strengths

• Generous free tier: 100GB/month of data ingest and one full-platform user at no cost
• Mature APM: Auto-instrumentation for 15+ languages with distributed tracing, error tracking, and service maps
• Unified data model: NRDB unifies all telemetry types, enabling cross-signal queries that are genuinely seamless
• Code-level visibility: Vulnerability management, change tracking, and CodeStream IDE integration

Limitations

• Per-user pricing kills at scale: Full-platform users cost $549/month at list price. A team of 20 engineers with full access costs $131,760/year in user fees alone — before any data ingestion costs
• NRQL lock-in: New Relic Query Language is powerful but proprietary. Years of saved queries, dashboards, and alerts are non-portable
• Data ingest costs beyond free tier: $0.30/GB for additional ingestion. At 100GB/day, ingestion alone runs $110,000/year before user fees
• No self-hosted option: All data must flow to New Relic's cloud infrastructure

Cost

Free tier with 100GB/month. Beyond that, $0.30/GB ingested plus per-user fees ($49/month for core users, $549/month for full-platform users). At 100GB/day with 10 full-platform users, annual costs range from $130,000 to $180,000 — in the same ballpark as Datadog for large teams.

5. Elastic Cloud — Best for Full-Text Search

Best for: Organizations that need powerful full-text search capabilities across massive log datasets.

Elastic Cloud is the managed offering for the Elastic Stack (Elasticsearch, Kibana, Logstash). Its Lucene-powered full-text search remains best-in-class for finding specific log entries across petabyte-scale datasets.

Architecture

Elasticsearch stores data in Lucene indexes across clusters of nodes. Each node requires substantial RAM (minimum 16GB heap), CPU, and fast SSD storage. The managed Elastic Cloud abstracts cluster management but the underlying resource requirements — and their costs — remain.

Strengths

• Best-in-class full-text search: Lucene indexing means arbitrary text searches return in milliseconds, even across billions of log entries
• Mature ecosystem: Kibana dashboards, Beats data shippers, Logstash pipelines, and a massive community
• Elastic APM and SIEM: Security analytics (Elastic Security) and APM capabilities in the same platform
• Flexible deployment: Self-hosted, Elastic Cloud, or Elastic Cloud on Kubernetes (ECK)

Limitations

• Infrastructure costs: Elasticsearch nodes need expensive compute. Self-hosted at 100GB/day requires 3-5 nodes with 32GB+ RAM and NVMe SSDs, costing $50,000-$80,000/year in infrastructure alone
• Operational complexity: Shard sizing, JVM tuning, index lifecycle management, and cluster rebalancing require dedicated expertise
• SSPL licensing: The shift from Apache 2.0 to Server Side Public License created uncertainty. OpenSearch forked in response, fragmenting the ecosystem
• No native OTLP: OpenTelemetry support exists but requires integration configuration rather than native endpoints

Cost

Elastic Cloud starts at $95/month for basic configurations. At 100GB/day with standard retention, managed Elastic Cloud runs $80,000-$120,000/year. Self-hosted deployments reduce licensing but infrastructure costs remain significant at $50,000-$80,000/year.

6. Mezmo — Best for Developer-Friendly Logging

Best for: Small to mid-sized teams that want fast, simple log management without full observability requirements.

Mezmo (formerly LogDNA) has built its reputation on developer experience and speed of onboarding. The platform is designed to get you from zero to searching logs in minutes.

Architecture

Mezmo is a SaaS-only platform with proprietary storage and indexing. Its focus is exclusively on log management — it does not provide metrics, traces, or full observability. The architecture prioritizes fast search and real-time streaming (live tail) over deep analytical queries.

Strengths

• Fastest onboarding: Install an agent, see logs within minutes. The onboarding experience is genuinely frictionless
• Clean, fast UI: Live tail, dynamic field parsing, and intuitive filtering that developers enjoy using
• Pipeline processing: Log pipelines for routing, parsing, and transforming data before storage
• Practical integrations: Slack, PagerDuty, webhooks, and common developer tool integrations work out of the box

Limitations

• Logs only: No metrics, no traces, no MELT observability. If you need more than log management, Mezmo requires complementary tools
• SaaS only: No self-hosted option. All data flows to Mezmo's infrastructure
• Enterprise scale: Less proven at hundreds of GB/day compared to Elastic or Splunk
• Limited analytics: Basic alerting and search capabilities compared to SQL-based or SPL-based platforms

Cost

Free tier with limited retention. Standard plans start at $1.50/GB ingested with 7-day retention. Enterprise pricing is custom. At 100GB/day, expect annual costs around $55,000-$80,000 — cheaper than Datadog but with a fraction of the capability.

7. Axiom — Best for Serverless Log Analytics

Best for: Teams using Vercel, Next.js, and serverless architectures that want cost-efficient log storage with on-demand querying.

Axiom takes a serverless approach to log analytics, separating storage from compute. The platform stores all data cheaply and charges for compute only when you query, which aligns well with bursty analysis patterns.

Architecture

Axiom uses a proprietary serverless architecture that decouples storage from compute. Data is stored in a compressed, columnar format with storage costs dramatically lower than traditional indexed platforms. Query compute is provisioned on-demand, meaning you do not pay for idle query capacity.

Strengths

• Storage-compute separation: Store everything, query on demand. No need to pre-select which logs to index
• Generous free tier: 500GB/month with 30-day retention — one of the largest free tiers available
• Modern developer experience: Strong support for Vercel, Next.js, and modern web stacks
• APL query language: The Axiom Processing Language is straightforward to learn and supports complex transformations

Limitations

• Smaller ecosystem: Fewer integrations and a more limited community compared to established platforms
• Logs-focused: While Axiom has expanded capabilities, it is primarily a log analytics platform, not a full MELT observability solution
• Enterprise maturity: RBAC, SSO, audit logging, and compliance features are less developed than mature alternatives
• Proprietary: Not open source. No self-hosted option for data sovereignty requirements

Cost

Free tier with 500GB/month and 30-day retention. Team plan at $25/month with 2TB/month. Enterprise pricing is custom but generally competitive. At 100GB/day, enterprise pricing is negotiable but typically falls below Datadog's rates.

Migrating from Datadog to Parseable

Switching from Datadog does not require a big-bang migration. The recommended approach is a parallel-run strategy: keep Datadog running while routing a copy of your telemetry to Parseable. Once you have validated query performance and coverage, cut over service by service.

Step 1: Deploy Parseable

Option 1: Parseable Cloud (Recommended)

Sign up at app.parseable.com — starts at $0.37/GB ingested, free tier available. Get an OTLP endpoint instantly and skip to Step 2.

Option 2: Self-Hosted

# Option A: Single binary (fastest)
curl https://sh.parseable.io | sh
 
# Option B: Docker with S3 backend (production-ready)
docker run -p 8000:8000 \
  -e P_S3_URL=https://s3.amazonaws.com \
  -e P_S3_ACCESS_KEY=your-access-key \
  -e P_S3_SECRET_KEY=your-secret-key \
  -e P_S3_BUCKET=parseable-data \
  -e P_S3_REGION=us-east-1 \
  parseable/parseable:latest \
  parseable s3-store
 
# Option C: Kubernetes via Helm
helm repo add parseable https://charts.parseable.com
helm install parseable parseable/parseable \
  --set storage.type=s3 \
  --set storage.s3.bucket=parseable-data \
  --set storage.s3.region=us-east-1

Step 2: Configure OpenTelemetry Collector for Dual Export

If you are already using the OpenTelemetry Collector (or the Datadog Agent with OTLP export), add Parseable as a second exporter. This sends data to both Datadog and Parseable simultaneously for parallel validation.

# otel-collector-config.yaml
receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318
 
exporters:
  # Keep Datadog running during migration
  datadog:
    api:
      key: ${DD_API_KEY}
 
  # Add Parseable as parallel destination
  otlphttp/parseable:
    endpoint: "http://parseable-host:8000/v1"
    headers:
      Authorization: "Basic <base64-encoded-credentials>"
 
service:
  pipelines:
    logs:
      receivers: [otlp]
      exporters: [datadog, otlphttp/parseable]
    traces:
      receivers: [otlp]
      exporters: [datadog, otlphttp/parseable]
    metrics:
      receivers: [otlp]
      exporters: [datadog, otlphttp/parseable]

Step 3: Validate Queries in Parseable

Translate your critical Datadog queries and monitors to SQL. Here are common patterns:

-- Datadog: "service:payment-api status:error"
-- Parseable SQL:
SELECT * FROM application_logs
WHERE service_name = 'payment-api'
  AND level = 'error'
  AND p_timestamp > NOW() - INTERVAL '1 hour'
ORDER BY p_timestamp DESC;
 
-- Datadog: count by service where status:error
-- Parseable SQL:
SELECT service_name, COUNT(*) as error_count
FROM application_logs
WHERE level = 'error'
  AND p_timestamp > NOW() - INTERVAL '24 hours'
GROUP BY service_name
ORDER BY error_count DESC;
 
-- Datadog: percentile latency by endpoint
-- Parseable SQL:
SELECT
  http_route,
  APPROX_PERCENTILE_CONT(duration_ms, 0.50) as p50,
  APPROX_PERCENTILE_CONT(duration_ms, 0.95) as p95,
  APPROX_PERCENTILE_CONT(duration_ms, 0.99) as p99
FROM traces
WHERE p_timestamp > NOW() - INTERVAL '1 hour'
GROUP BY http_route
ORDER BY p99 DESC;

Step 4: Set Up Alerts and Dashboards

Configure alerts in Parseable's built-in console or connect Grafana as a visualization layer. Parseable exposes a SQL-compatible API that Grafana can query directly.

Step 5: Decommission Datadog

Once you have validated data completeness, query performance, and alert coverage over a 2-4 week parallel run, remove the Datadog exporter from your OTel Collector config and cancel your Datadog subscription.

Frequently Asked Questions

Why is Datadog so expensive for logs?

Datadog's log pricing has three stacking components: ingestion ($0.10/GB), indexing ($1.70 per million events for 15-day retention), and extended retention surcharges. The indexing cost is the primary driver — at ~1 million events per GB, the effective cost is $1.70/GB for indexed logs on top of the $0.10/GB ingestion fee. This architecture made sense when Datadog was smaller, but at enterprise log volumes (500GB+ per day), the per-GB model produces bills that rival dedicated infrastructure investments. Alternatives like Parseable that store data on S3 object storage ($0.023/GB/month) eliminate both the ingestion and indexing premium entirely.

What is the cheapest Datadog alternative for log management?

Parseable offers the lowest total cost of ownership for log management at scale. Its S3-native architecture means storage costs are $0.023/GB/month — the standard AWS S3 rate — with 80-90% columnar compression reducing actual storage requirements further. At 100GB/day, Parseable costs approximately $3,000-$10,000/year depending on compute sizing, compared to Datadog's $65,000-$195,000/year for equivalent functionality. Parseable is also a full MELT observability platform (not just logs), which means you can replace Datadog entirely rather than supplementing it. See our full open-source log management tools comparison for a broader evaluation.

Can I migrate from Datadog without re-instrumenting my applications?

Yes. If your applications use OpenTelemetry SDKs or the OpenTelemetry Collector, migration is a configuration change — not a code change. You update your OTel Collector's exporter configuration to point at Parseable's native OTLP endpoint instead of Datadog's intake API. Your application instrumentation remains untouched. If you are currently using Datadog's proprietary agents (dd-agent), the migration requires replacing those agents with OTel Collectors, which is a one-time infrastructure change. The recommended approach is a parallel-run period where both Datadog and Parseable receive the same data, allowing you to validate coverage before cutting over.

How does Parseable handle traces and metrics, not just logs?

Parseable is a unified MELT observability platform, not a logging tool. It ingests and stores logs, metrics, events, and traces through a single native OTLP endpoint. All signal types are stored in Apache Parquet format on S3 and queryable with the same SQL interface. This means you can correlate across signal types — join a slow trace span with its associated log entries using a shared trace ID, or correlate metric anomalies with deployment events — without switching tools or query languages. This is the same scope of observability that Datadog provides, at a fraction of the cost. For a detailed comparison of Parseable vs other approaches, see our Elasticsearch vs Parseable analysis.

Is SigNoz cheaper than Datadog?

SigNoz is cheaper than Datadog, but the savings depend on the deployment model. Self-hosted SigNoz eliminates licensing fees, but you still need to run a ClickHouse cluster — which requires SSD-backed compute instances, not cheap S3 storage. At 100GB/day, self-hosted SigNoz infrastructure costs run $25,000-$40,000/year. SigNoz Cloud charges $0.3/GB for logs, which puts 100GB/day at approximately $110,000/year — cheaper than Datadog ($195,000+) but still significant. For teams that want maximum cost savings, S3-native platforms like Parseable deliver storage costs 10-50x lower than ClickHouse-based solutions because object storage is fundamentally cheaper than block storage.

Should I choose Grafana Cloud or Parseable?

It depends on your existing infrastructure and priorities. Choose Grafana Cloud if your team is already deeply invested in the Prometheus/Grafana ecosystem and you primarily need log management alongside existing Prometheus metrics. Grafana's dashboarding capabilities are unmatched, and the free tier is generous. Choose Parseable if you want the lowest possible cost at scale, prefer SQL over three separate query languages (PromQL, LogQL, TraceQL), want full MELT observability in a single binary with minimal operational overhead, or need to self-host for data sovereignty. Parseable's architecture (single binary + S3) is dramatically simpler to operate than the full Grafana stack (Loki + Mimir + Tempo + Grafana), which requires managing four separate systems. For a broader look at open-source log management options, see our complete comparison guide.

Key Takeaways: Choosing the Right Datadog Alternatives

1. Datadog's log pricing stacks three separate charges (ingestion + indexing + retention) that compound to $65,000-$650,000/year for 100GB-1TB/day volumes. Most teams underestimate the total by 40-60%.

2. S3-native platforms like Parseable deliver 90-97% cost reductions by storing telemetry on object storage ($0.023/GB/month) instead of proprietary indexed infrastructure. The savings are architectural, not marketing.

3. Parseable is a full MELT observability platform, not a logging tool. It replaces Datadog entirely — logs, metrics, events, and traces — in a single binary with a SQL query interface.

4. SigNoz Cloud and New Relic still charge per-GB, which means costs scale linearly with data volume. Self-hosted SigNoz uses ClickHouse on SSDs, which is 5-10x more expensive than S3 storage.

5. Migration from Datadog is a configuration change, not a rewrite. If you use OpenTelemetry, point your collector at Parseable's native OTLP endpoint. Run both in parallel for 2-4 weeks, then cut over.

6. SQL beats proprietary query languages for team productivity. Every engineer knows SQL. Nobody needs SPL certification, NRQL training, or LogQL tutorials to query Parseable.

Get Started

• Start with Parseable Cloud — starts at $0.37/GB, free tier available
• Self-hosted deployment — single binary, deploy in 2 minutes
• Read the docs — guides, API reference, and tutorials
• Join our Slack — community and engineering support