Grafana vs New Relic vs Datadog Enterprise
Tiga observability platform saya evaluate paralel 26 bulan di BUMN energy + fintech series-B Jakarta. Grafana self-host = Rp 11 juta/bulan. New Relic = USD 4.000-8.000/bulan. Datadog = USD 8.000-25.000/bulan. Verdict per scale + feature.
TL;DR
- Grafana stack self-host (LGTM): Loki + Tempo + Mimir + Grafana, cost Rp 10-15 juta/bulan, ownership data, ops 12-18 jam/bulan.
- New Relic: APM AI insight matang, data-based pricing, USD 4-8k/bulan typical enterprise.
- Datadog: feature breadth terbesar (APM + RUM + Synthetics + DBM + CI Visibility), USD 8-25k+/bulan.
- Honeycomb: APM-first dengan high-cardinality query, niche premium use case.
- Verdict: Conditional — Grafana self-host untuk cost-sensitive, Datadog untuk enterprise mature feature-rich, New Relic middle.
Konteks
Saya evaluate paralel 26 bulan (April 2024 - Mei 2026) di:
- Fintech series-B Jakarta: Grafana stack self-host primary (24 bulan), New Relic eval 8 bulan untuk 6 service kritis (Oktober 2024 - Mei 2025), Datadog eval 4 bulan untuk RUM + Synthetics (Q1-Q2 2025)
- BUMN energy Jakarta: Grafana stack self-host 18 bulan, sebelumnya Datadog enterprise contract (legacy, decommission Q3 2024 karena cost cutting)
Sebelumnya saya pakai ELK + Prometheus + Jaeger di BUMN 2022-2024 (4 tahun). Pengalaman observability total 6+ tahun.
Pricing (Juni 2026)
Grafana stack self-host
- Software gratis (AGPL/Apache 2.0)
- Infra (cluster Loki + Tempo + Mimir + Grafana):
- Compute total: Rp 7,5 juta/bulan
- Storage R2: Rp 3,1 juta/bulan
- OTel collector + Promtail DaemonSet: Rp 1,8 juta/bulan
- Total: Rp 12,4 juta/bulan
Grafana Cloud (managed alternative)
- Free tier: 50GB log, 10k metric series, 50GB trace, 3 user
- Pro: USD 49/bulan base + USD 8/100GB logs + USD 0,16/1000 active series
- Advanced: USD 299/bulan + advanced feature
- Untuk skala 18 service: ~USD 800-1.500/bulan = Rp 12,8-24 juta/bulan
New Relic
- Free: 100GB ingest/bulan, 1 full platform user
- Standard: USD 49/user/bulan
- Pro: USD 99/user/bulan
- Plus data ingest: USD 0,30/GB di atas free tier
- Untuk fintech saya eval 6 service: ingest ~280 GB/bulan = USD 84 ingest + 5 user × USD 99 = USD 579 = Rp 9,3 juta/bulan
- Untuk full 18 service equivalent: estimated USD 4.000-8.000/bulan = Rp 64-128 juta/bulan
Datadog
- Infrastructure: USD 15/host/month + APM USD 36/host/month
- Log Management: USD 0,10/GB ingest + USD 1,70 per million log event retention
- RUM: USD 1,50/1k session
- Synthetics: USD 12/10k API test
- Untuk 54 host equivalent + APM + Log 1,8 TB + RUM + Synthetics:
- Infrastructure: 54 × USD 15 = USD 810
- APM: 54 × USD 36 = USD 1.944
- Log Management: 1.800 GB × USD 0,10 + 750M event × USD 1,70/M = USD 1.455
- RUM: ~USD 240
- Synthetics: ~USD 120
- Total Datadog: ~USD 4.569/bulan = Rp 73 juta/bulan
- Eval saya 4 bulan RUM + Synthetics only: ~USD 360/bulan = Rp 5,8 juta
Honeycomb
- Free: 20M event/bulan
- Pro: USD 96/bulan base + USD 0,15/M event
- Enterprise: quoted
- Untuk skala saya: USD 96 + 250 × 0,15 = USD 134/bulan untuk events, plus user seat
Total observability stack saya saat ini
| Komponen | Cost/bulan |
|---|---|
| Grafana stack self-host (primary) | Rp 12,4 juta |
| Datadog (RUM + Synthetics only) | Rp 5,8 juta |
| New Relic (decommission pasca-eval) | Rp 0 |
| Total | Rp 18,2 juta/bulan |
Bandingkan kalau full Datadog (replace Grafana stack): Rp 73 juta/bulan. Hybrid saving Rp 55 juta/bulan dengan trade-off integrate 2 tool.
SLO + performance (26 bulan)
Grafana stack self-host
| Metrik | Target | Realisasi |
|---|---|---|
| Ingest availability | 99,9% | 99,94% |
| Query latency Loki p99 (24h) | < 3 detik | 2,1 detik |
| Query latency Tempo p99 | < 2 detik | 1,4 detik |
| Query latency Mimir p99 (1h) | < 1 detik | 720ms |
| Alert latency event-to-notify | < 60 detik | 38 detik |
| Dashboard load p99 | < 5 detik | 3,8 detik |
Datadog
| Metrik | Target | Realisasi |
|---|---|---|
| Ingest availability | 99,99% (SLA) | 99,994% |
| Query latency log p99 | < 3 detik | 1,4 detik |
| Query latency APM p99 | < 2 detik | 980ms |
| Alert latency | < 60 detik | 22 detik |
Datadog unggul query latency (managed infra superior). Worth premium untuk vendor SLA-backed dengan 99,99%.
New Relic
| Metrik | Realisasi |
|---|---|
| Ingest availability | 99,98% |
| Query latency p99 | 1,8 detik |
| Alert latency | 35 detik |
New Relic middle antara Grafana self-host + Datadog di performance.
Capability comparison
| Capability | Grafana stack | New Relic | Datadog |
|---|---|---|---|
| Metric (Prometheus-compat) | Mimir | ya | ya |
| Log aggregation | Loki | ya | ya |
| Distributed tracing | Tempo | ya | ya |
| APM (auto-instrumentation) | OTel auto | matang | matang (terbaik) |
| RUM (Real User Monitoring) | Grafana Faro (beta) | ya | matang |
| Synthetics (uptime monitor external) | Grafana Synthetic Monitoring (beta) | ya | matang |
| Database Monitoring | Pganalyze / pgwatch separate | ya | matang |
| CI Visibility (pipeline metric) | manual via Prometheus | ya | matang |
| Network monitoring | manual | ya | ya |
| Security (CSPM, CWPP) | terbatas | ya (basic) | matang |
| AI insight / anomaly detection | basic | matang | matang |
| Dashboarding | Grafana (best) | sedang | mature |
| Alerting | Grafana Alerting (mature) | ya | ya |
| Self-host option | ya | tidak | tidak |
| Data residency Indonesia | self-host yes | Singapore PoP | Singapore PoP |
| Vendor lock-in | minimal | tinggi | tinggi |
Trade-off arsitektural
Pilih Grafana stack self-host kalau:
- Cost-sensitive (budget < Rp 20 juta/bulan observability)
- Data residency Indonesia wajib (compliance OJK / sektoral)
- Ops capacity 12-20 jam/bulan tersedia
- Feature core (metric + log + trace + dashboard) cukup
- Vendor lock-in concern
Pilih Datadog kalau:
- Budget IT besar (Rp 70-200 juta/bulan observability OK)
- Butuh feature breadth (RUM + Synthetics + DBM + Security)
- Tim engineering tidak fokus ke observability ops
- Speed-to-value prioritas
- AI insight + anomaly detection matter
Pilih New Relic kalau:
- Middle ground cost + feature
- APM AI insight (Errors Inbox, Workloads AI) jadi differentiator
- Data-based pricing predictable lebih dari host-based
Pilih Honeycomb kalau:
- APM-first usage dengan high-cardinality query
- Engineering team observability-mature (familiar event-based observability)
- Niche use case (specific debugging pattern)
High availability + DR
Grafana stack self-host HA
- 3-replica setiap komponen (ingester, querier, distributor)
- Storage R2 multi-region replication
- RPO: 30 detik
- RTO: 3-5 menit untuk regional failover
- Failover semi-manual
Datadog managed
- Vendor SLA 99,99%
- RPO: 0
- RTO: 0 (multi-region active)
- Transparent failover
New Relic managed
- Vendor SLA 99,9% Standard, 99,95% Pro
- Similar Datadog managed
Feature deep-dive
APM auto-instrumentation
Datadog APM agent untuk Java, Python, Go, Node, Ruby, PHP, .NET — drop-in, zero code change, auto-trace 95% framework standard.
OTel auto-instrumentation (untuk Grafana stack): semi auto-drop-in via Java agent, tapi untuk Python/Node butuh manual init. Coverage similar Datadog tapi setup effort 2-3x.
Untuk team yang prefer “drop agent dan jalan”: Datadog menang. Untuk team yang OK setup OTel manual (gain control): Grafana stack OK.
Real User Monitoring (RUM)
Datadog RUM matang: full session replay, error tracking, performance metric, user journey, custom event. Per-session pricing — predictable tapi bisa expensive (Rp 48/session × 100k session/bulan = Rp 4,8 juta tambahan).
Grafana Faro: open source RUM solution. Beta 2025, GA Q4 2026 expected. Untuk Indonesia: belum production-ready, masih iterate.
Saya pakai Datadog RUM (5,8 juta/bulan equivalent). Worth karena product team active pakai data RUM untuk optimization.
Synthetics
Datadog Synthetics: external uptime + API test dari multiple PoP global. Worth untuk SaaS dengan customer expect uptime guarantee.
Grafana Synthetic Monitoring: beta 2025, similar functionality. Tidak production-mature di Indonesia 2026.
Database Monitoring
Datadog DBM: USD 70/DB host. Untuk Postgres / MySQL kompleks dengan query optimization need: worth.
Alternative: Pganalyze open source (gratis self-host) atau pgwatch2 (gratis). Cover 80% feature DBM. Saya pakai Pganalyze, save Rp 9 juta/bulan equivalent.
Hybrid pattern yang saya jalankan
Backend service log + metric + trace
└── OTel SDK ──→ OTel Collector ──→ Loki + Tempo + Mimir (self-host)
│
└─→ Grafana dashboard + alert
Frontend web user behavior
└── Datadog RUM SDK ──→ Datadog cloud
│
└─→ Datadog dashboard
External uptime / SLO monitoring
└── Datadog Synthetics (cloud-based, eksternal PoP)
Trade-off: 2 tooling chain, 2 vendor. Worth karena Grafana stack handle 80% workload (cost-efficient) + Datadog handle 20% premium feature (RUM, Synthetics).
Migration risk
dari Datadog ke Grafana stack
Pengalaman BUMN saya (2024):
- Effort: 14 minggu untuk 14 service
- Pain point: Datadog APM tag schema custom — translate ke OTel attribute, beberapa metric loss historical context
- Outcome: Saving cost Rp 60+ juta/bulan, ops time naik 25 jam/bulan
- Lesson: pre-migration 4 minggu data export + dashboard recreate
dari ELK ke Grafana Loki
Smoother. Filebeat → Promtail config translate straightforward. Effort 8-12 minggu untuk 20 service.
dari Grafana stack ke Datadog
Reverse migration: 6-10 minggu, butuh setup Datadog Agent per pod. Tidak common — biasanya migrate ke arah sebaliknya untuk cost.
Cost of ownership 36 bulan
Skenario: 18 microservice fintech series-B + 6 frontend channel.
| Item | Grafana stack self-host | Datadog full | New Relic |
|---|---|---|---|
| Software/SaaS 3 tahun | Rp 0 | Rp 2,6 miliar | Rp 1,8 miliar |
| Infra | Rp 450 juta | Rp 0 | Rp 0 |
| Ops engineer | Rp 540 juta | Rp 130 juta | Rp 180 juta |
| Migration / setup | Rp 95 juta | Rp 40 juta | Rp 55 juta |
| Total 3 tahun | Rp 1,09 miliar | Rp 2,77 miliar | Rp 2,04 miliar |
Grafana stack 2,5x lebih murah dari Datadog di skala ini. Worth-nya Datadog di 3 tahun = Rp 1,68 miliar premium untuk feature breadth + ops saving. Untuk fintech series-B dengan SRE dedicated: skip premium. Untuk enterprise mature dengan budget IT Rp 20+ miliar/tahun: Datadog worth.
Indonesia specific
Data residency
Grafana stack self-host di GKE Jakarta + R2 Jakarta = 100% data tinggal di Indonesia. Datadog / New Relic: data ke US atau Singapore region. Untuk fintech regulated: self-host mandatory.
Compliance OJK
Audit log retention 7 tahun OJK. Grafana stack: setup retention policy + archive ke S3 Glacier Indonesia (kalau tersedia, kalau tidak Glacier Singapore + disclosure). Datadog: retention policy configurable + Long-Term Retention addon (premium).
Hiring
Grafana stack engineer (PromQL, LogQL, Mimir, Loki ops): pool kecil-medium di Indonesia. Datadog engineer: pool moderate (banyak yang familiar via vendor training). Train internal Grafana engineer 4-6 minggu, sponsor sertifikasi Grafana Cloud.
Yang surprising
Setelah 26 bulan evaluation: Grafana stack performance lebih dekat dengan Datadog dari ekspektasi awal. Gap query latency 0,7 detik vs 1,4 detik bukan game-changer untuk daily ops. Worth-nya Datadog bukan di performance — worth-nya di feature breadth (RUM, Synthetics, DBM) yang tidak ada equivalent self-host mature.
Surprise lain: New Relic AI insight (Errors Inbox auto-cluster, Workloads anomaly detection) ternyata sangat berguna untuk team yang tidak observability-mature. Untuk senior SRE yang sudah punya mental model deep: feature ini tidak banyak add value. Worth premium tergantung skill level tim.
Verdict
Conditional dengan rule konkret:
- Default cost-sensitive enterprise Indonesia: Grafana stack self-host. ROI vs Datadog jelas untuk skala mid-size.
- Enterprise mature dengan budget IT Rp 15+ miliar/tahun + butuh RUM/Synthetics/DBM: Datadog. Feature breadth justify premium.
- Middle ground budget Rp 5-10 juta/bulan: New Relic atau Grafana Cloud Pro.
- Hybrid pattern: Grafana stack untuk core observability + Datadog RUM/Synthetics. Saving 60% vs full Datadog.
- Skip Honeycomb kalau tim engineering bukan observability-mature. Cost-benefit tidak match untuk most enterprise Indonesia.
Threshold konkret transisi self-host ke managed: tim SRE < 0,5 FTE atau ops cost time > Rp 25 juta/bulan. Di atas threshold ini, managed lebih masuk akal cash-flow.
Ditulis oleh Asti Larasati