OpenObserve Is Coming for Datadog’s Lunch

What’s Happening

There’s a quiet revolution underway in the observability stack, and it’s being led by a Rust-based, open-source project that most non-infrastructure people haven’t heard of yet. OpenObserve — an AI-native, open-source platform for logs, metrics, and traces — has been steadily climbing GitHub stars (now past 13,000) and capturing attention from engineering teams fed up with the astronomical cost of commercial observability tools. The project positions itself as a full-stack open-source Datadog alternative, and based on the traction it’s getting, the comparison isn’t just marketing fluff.

What’s driving the momentum right now? Two things. First, Datadog’s pricing model continues to be a recurring pain point for startups and mid-market companies. Their per-host, per-GB pricing compounds fast — it’s not unusual for a Series A startup running moderate workloads to see five-figure monthly observability bills. Second, the broader shift toward AI-native infrastructure is real: teams don’t just want dashboards anymore. They want tools that can surface anomalies, correlate incidents, and reduce mean time to resolution using machine learning out of the box. OpenObserve is betting it can deliver both — the cost savings of open source and the intelligence layer startups actually need.

The project, founded by Prabhat Sharma, a former AWS engineer, has been gaining adoption among DevOps teams at fast-growing startups and mid-size enterprises. OpenObserve claims up to 140x lower storage costs compared to Elasticsearch-based solutions, largely because of its columnar storage approach and aggressive compression. That’s a bold claim, but the architecture — built on Apache Arrow and Parquet formats, running on object storage like S3 — lends it credibility. The question is no longer whether this project is interesting. It’s whether it can become the default for the next generation of cloud-native companies.

Why Founders Should Care

Let’s talk money. The global observability market was valued at approximately $2.4 billion in 2023 and is projected to exceed $4.1 billion by 2027, according to Gartner’s Observability Market Guide. That’s a large and growing pie — and it’s dominated by a handful of incumbents whose pricing is, frankly, punitive for startups. Datadog reported $2.68 billion in revenue for fiscal 2025 in their Q4 2025 earnings, with net revenue retention north of 115%. Translation: customers keep paying more every year, often not because they want to, but because switching costs are brutal and observability data is sticky.

This creates a classic disruption wedge. When the incumbent’s greatest strength — deep integration and comprehensive coverage — comes paired with pricing that makes CFOs wince, there’s room for a challenger. OpenObserve is attacking from the bottom: offer 80% of the functionality at 10% of the cost, and bet that the community and AI capabilities will close the gap. If you’re a founder in the infrastructure, DevTools, or platform engineering space, this is a signal worth paying attention to. The observability stack is being unbundled and rebuilt — again.

Timing matters here too. The explosion of microservices, serverless functions, and AI inference workloads means the volume of telemetry data is growing exponentially. Startups deploying LLM-powered features are generating orders of magnitude more logs and traces than traditional web apps. The old model of paying per-GB for log ingestion simply doesn’t scale. Founders building the next wave of AI-first applications need observability that’s both affordable and intelligent — and that’s exactly the gap OpenObserve is targeting.

Who’s Already Moving

OpenObserve isn’t operating in a vacuum. The open-source observability space has gotten crowded and competitive, which is actually a good sign — it means the market is real.

• OpenObserve — The namesake of this trend. Built in Rust for performance, it combines logs, metrics, traces, and a built-in dashboarding UI into a single binary. Its AI features focus on anomaly detection and log pattern analysis. The GitHub repository shows consistent development velocity, with frequent releases and a growing contributor base. The company behind it, OpenObserve Inc., offers a managed cloud version alongside the self-hosted option.
• Grafana Labs — The 800-pound gorilla of open-source observability. Grafana’s LGTM stack (Loki, Grafana, Tempo, Mimir) is the incumbent open-source choice, and the company raised at a $6 billion valuation. But Grafana’s approach requires stitching together multiple tools, and the managed offering (Grafana Cloud) isn’t cheap either. OpenObserve’s unified approach is a direct contrast to Grafana’s multi-tool philosophy.
• SigNoz — A Y Combinator-backed open-source observability platform built on ClickHouse and OpenTelemetry. SigNoz is the closest direct competitor to OpenObserve in terms of positioning. They’ve been growing steadily and offer a clean, Datadog-like UI. The key differentiator between SigNoz and OpenObserve comes down to storage architecture — ClickHouse vs. object storage — and the AI-native capabilities OpenObserve is emphasizing.
• Highlight.io — An open-source session replay and error monitoring tool that’s expanded into full-stack observability. They’re focused more on the frontend and developer experience angle, but their open-source ethos and startup-friendly pricing make them part of the same wave.

And then there are the incumbents: Datadog, Splunk (now part of Cisco), New Relic, and Elastic. These companies aren’t going away, but they’re all facing pressure from open-source alternatives that are getting good enough, fast enough, to pull away the cost-conscious segment of their market.

“We built OpenObserve because we saw the same pattern at every startup I advised: teams would start with a commercial observability tool, love it for six months, then spend the next year trying to figure out how to reduce the bill. That’s a broken model.” — Prabhat Sharma, Founder of OpenObserve

The Startup Opportunity

If you’re a founder reading this, there are several actionable angles here beyond just “use OpenObserve instead of Datadog” (though honestly, that might be a smart move too).

• Build on the observability platform layer. OpenObserve’s open-source nature and plugin-friendly architecture mean there’s room to build vertical-specific observability solutions on top of it. Think: observability for AI/ML pipelines, observability for fintech compliance, observability tailored to IoT edge deployments. The platform is there; the industry-specific intelligence layers aren’t.
• Managed OpenObserve for specific clouds and regions. While OpenObserve Inc. offers its own cloud, there’s opportunity for managed service providers focused on specific regulatory environments — EU data residency, healthcare (HIPAA), or government (FedRAMP). Managed open-source is a proven business model (see: Confluent, Elastic, MongoDB).
• AI-powered incident management built on open telemetry data. If teams are moving their observability data to open-source platforms, the next layer is AI-driven incident correlation, root cause analysis, and auto-remediation. This is where the real value creation happens — and it’s still very early innings.
• Cost optimization and migration tooling. Help companies migrate from Datadog, Splunk, or New Relic to open-source alternatives without losing their minds. Migration tooling, dashboard conversion, alert rule translation — these are unsexy but highly valuable products that companies will pay for.

The meta-opportunity is this: every time a dominant SaaS category gets disrupted by an open-source alternative, a whole ecosystem of startups emerges around the new platform. We saw it with Kubernetes, with Kafka, with PostgreSQL. Observability is next.

Watch Out For

Let’s not pretend this is all upside. There are real risks for both OpenObserve as a project and for founders building in this space.

• Open-source sustainability is hard. OpenObserve Inc. needs to build a viable commercial business on top of the open-source project. The graveyard of open-source companies that couldn’t monetize is long. If the managed cloud offering doesn’t gain traction, development velocity could slow — and in observability, falling behind on integrations is death.
• Datadog’s moat is deeper than pricing. Datadog’s real advantage isn’t any single feature — it’s the breadth of 800+ integrations, the unified platform experience, and the organizational inertia of existing deployments. Switching observability tools is painful. Teams have dashboards, alerts, runbooks, and on-call rotations built around their current tool. OpenObserve needs to make migration trivially easy, and they’re not there yet.
• AI-native is easy to claim, hard to deliver. Every infrastructure tool is slapping “AI-native” on their marketing pages. The question is whether OpenObserve’s AI capabilities — anomaly detection, log clustering, intelligent alerting — actually work better than what Datadog’s Watchdog or Grafana’s ML features already offer. If the AI layer is superficial, the positioning falls apart.
• Community fragmentation. With SigNoz, Grafana Labs, and OpenObserve all competing for the open-source observability crown, there’s a risk of ecosystem fragmentation. If the community can’t coalesce around a standard — OpenTelemetry helps, but it doesn’t solve everything — adoption could stall as teams face paralysis of choice.
• Enterprise sales are a different game. Getting GitHub stars and Hacker News upvotes is one thing. Getting a Fortune 500 company to rip out Splunk and replace it with OpenObserve requires enterprise sales muscle, SOC 2 compliance, SLAs, and professional support — the boring stuff that open-source companies often underinvest in.

The Bottom Line

OpenObserve represents something bigger than one project’s GitHub trajectory. It’s a leading indicator that the $4 billion+ observability market is entering its open-source disruption phase — the same phase that reshaped databases, streaming, and container orchestration over the past decade. The combination of unsustainable incumbent pricing, exploding telemetry volumes from AI workloads, and genuinely capable open-source alternatives is creating a window that won’t stay open forever.

If you’re a founder, here’s what to do with this: First, if you’re currently bleeding money on Datadog or Splunk, run a proof-of-concept with OpenObserve or SigNoz this quarter — you might cut your observability spend by 80%. Second, if you’re building in the infrastructure space, study the ecosystem forming around open-source observability platforms. The picks-and-shovels opportunities — migration tools, vertical solutions, AI layers, managed services — are real and largely unaddressed. Third, if you’re an investor, put observability infrastructure on your radar. The next Confluent or HashiCorp could be getting built right now in this space. The incumbents are fat, the alternatives are maturing, and the market tailwinds are strong. Don’t sleep on this one.