
Automate work across thousands of apps — no code required
Datadog records 5,175,760 monthly visits with -4.8% month-over-month growth and Freemium, Free Trial, Paid, Contact for Pricing, Free pricing. Fleak records 3,778 monthly visits with -24.4% growth and Freemium, Contact for Pricing pricing.

Self-healing security data pipelines that cut SIEM cost
| Dimension / Metric | ![]() | |
|---|---|---|
| Official Domain | datadoghq.com | fleak.ai |
| Pricing Model | Freemium, Free Trial, Paid, Contact for Pricing, Free | Freemium, Contact for Pricing |
| Monthly Visits | 5.18M (5,175,760) | 3.78K (3,778) |
| MoM Traffic Delta | -263.56K (-4.8%) | -1.22K (-24.4%) |
| Primary Market & Share | 🇺🇸 United States (41.4%) | 🇺🇸 United States (86.2%) |
| Direct Visits / mo | 2.75M | 1.5K |
| User Rating | 4.7 | 4.2 |
| Supported Platforms | Website | Website |
Unified observability, security and incident response for cloud-scale engineering teams
Agents, cloud integrations and OpenTelemetry collectors feed one platform where metrics, distributed traces, profiles and logs can be pivoted between without context switching.
Bits AI, an agent builder and LLM/AI-agent observability help teams triage alerts, explain anomalies and monitor model latency, token spend and agent traces.
Cloud SIEM, cloud security posture management, vulnerability management, workload protection and code scanning correlate threat signals with the telemetry you already collect.
Real user monitoring, session replay, product analytics, synthetic and mobile testing sit alongside CI visibility, test optimization, feature flags and DORA metrics.

Self-healing security data pipelines that cut SIEM cost
Fleak evaluates each event type against what every downstream tool needs, then sends real-time correlation to the SIEM, hunt data to the lake, compliance logs to the archive and noise nowhere.
When an upstream source changes its schema, Fleak detects the drift, generates corrected configuration and redeploys — the vendor cites an average heal time around three minutes.
Every source is normalized into a single schema on arrival, so 280 sources stop meaning 280 parsers and 280 detection variations.
Pipelines can be described in plain English and built automatically, with an MCP server and AI observability for agent sessions.

Automate work across thousands of apps — no code required
Build, share, and control AI agents that actually do the work
Build, share, and control AI agents that actually do the work

SpecialistSpecializes in tracing and debugging production LLM applications, but does not provide general cloud infrastructure metrics or log management.
SpecialistSpecializes in tracing and debugging production LLM applications, but does not provide general cloud infrastructure metrics or log management.

Free / Open-sourceOpen-source data integration engineered for AI agents and analytics
Free / Open-sourceOpen-source data integration engineered for AI agents and analytics

SpecialistProvides advanced AI-driven threat detection for cloud networks, but focuses purely on security rather than general application performance monitoring.
SpecialistProvides advanced AI-driven threat detection for cloud networks, but focuses purely on security rather than general application performance monitoring.
SpecialistNo-code connector that links your apps and automates routine work without developers
SpecialistNo-code connector that links your apps and automates routine work without developers

SpecialistEvaluate, monitor, and govern your AI applications in production
SpecialistEvaluate, monitor, and govern your AI applications in production

No-code AI web scraping that builds, runs, and repairs its own bots
No-code AI web scraping that builds, runs, and repairs its own bots

SpecialistThe collaboration layer for AI engineering teams
SpecialistThe collaboration layer for AI engineering teams

SpecialistStandardize LLM evaluation, observability, and red teaming across your whole org
SpecialistStandardize LLM evaluation, observability, and red teaming across your whole org
SpecialistAI governance, testing, and observability for regulated enterprises
SpecialistAI governance, testing, and observability for regulated enterprises

SpecialistFocuses on machine learning and LLM model observability, whereas Datadog primarily targets traditional infrastructure and application performance monitoring.
SpecialistFocuses on machine learning and LLM model observability, whereas Datadog primarily targets traditional infrastructure and application performance monitoring.