Observability Market Dynamics: The $28B Consolidation Wave, OpenTelemetry, the Cost Revolt & What Comes Next

Market Overview & Key Numbers

Three forces are colliding: (1) consolidation — platform companies in security, networking, and data are acquiring observability startups, (2) commoditization — OpenTelemetry is making backends interchangeable, and (3) cost pressure — companies at scale are revolting against per-GB pricing that produces million-dollar annual bills. The result is a market that is simultaneously massive and under siege.

Revenue Landscape: Who’s Winning

The Power Law

Datadog alone captures more revenue ($3.43B) than all open source and indie alternatives combined. With 31,400 customers and 4,310 generating over $100K ARR (+19% YoY), it dominates the enterprise. 31% of customers use 6+ products; 16% use 8+ products — deep platform lock-in. Yet its pricing is the single biggest driver of the open source revolt.

The $40B+ Consolidation Wave

The Convergence Thesis

Three types of companies are acquiring observability startups:

1. Security companies (Palo Alto Networks → Chronosphere). Security and observability share the same data: logs, network flows, endpoint telemetry. Converging them eliminates duplicate data pipelines and enables unified threat detection + performance monitoring.
2. Networking/infrastructure companies (Cisco → Splunk). Network telemetry is observability data. Cisco now has Splunk + AppDynamics + ThousandEyes for full-stack visibility from network to application.
3. Data platform companies (Snowflake → Observe, ClickHouse → HyperDX). Observability is a data problem. If you already store and query petabytes, adding observability is a product expansion, not a new capability.

The implication for startups: standalone observability companies are becoming acquisition targets rather than IPO candidates. Chronosphere ($3.35B), Observe (~$1B), and HyperDX were all acquired before reaching maturity as independent companies. The window for building a standalone observability business is narrowing.

OpenTelemetry: The Market Reshaper

Why OTel Changes Everything

OpenTelemetry decouples instrumentation (how you collect telemetry) from backends (where you send it). This is the most important structural change in the observability market since the term was coined:

Vendor portability

Instrument once with OTel, send to any backend. Switch from Datadog to Grafana Cloud without re-instrumenting your applications. This shatters the traditional lock-in model that vendors relied on for retention.

Multi-vendor strategies

Route critical traces to Honeycomb for deep analysis, bulk logs to Loki for cost efficiency, and metrics to VictoriaMetrics for performance. The OTel Collector’s pipeline architecture (receivers → processors → exporters) makes this trivial.

Commoditization of collection

If every vendor supports the same instrumentation standard, the differentiation shifts from “how easy is it to get data in?” to “how good is your query engine, AI, and UX?”

SDK Status

The Implication

OTel is the kingmaker. With near-universal vendor support and 50% adoption, it makes observability backends interchangeable. This favors open source and cost-effective alternatives, because the switching cost drops from “re-instrument everything” to “change the exporter config.” Vendors that relied on proprietary agents for lock-in (Datadog’s dd-agent, New Relic’s agent) are losing their moat.

The Observability Tax Revolt

The Scale of the Problem

Observability costs often grow faster than revenue or headcount. The dynamic: more microservices → more hosts → more logs/metrics/traces → exponentially higher bills. SaaS platforms charge per-GB ingested, per-host monitored, or per-custom-metric — creating a perverse incentive to reduce observability at exactly the moment you need more visibility.

Why Bills Explode

• Container/microservice architectures multiply “host” counts vs. monolithic apps
• Custom metrics and high-cardinality dimensions are major cost drivers
• More visibility = more cost, which incentivizes teams to sample aggressively and drop data
• Multi-dimensional pricing (per-host + per-GB + per-metric + per-span) makes bills unpredictable
• Vendor pricing tiers change, often retroactively raising costs for existing usage patterns

Migration Economics

Conventional migration

~6 months, up to $200K in consulting fees. Re-instrumenting applications, rebuilding dashboards, migrating alerts

Modern migration (with OTel)

Days to weeks. Change the OTel Collector exporter config. Dashboards and alerts still need migration, but the hardest part (instrumentation) is portable

Typical savings from Datadog alternatives

60–80% cost reduction with managed alternatives; 90%+ savings with self-hosted ClickHouse-based solutions at multi-TB/day scale

Open Source Observability Stack

VictoriaMetrics: The Self-Funded Dark Horse

VictoriaMetrics has achieved 300%+ growth in 2024, 1 billion+ Docker downloads, and Spotify as a customer — all with zero external funding. It’s Prometheus-compatible (drop-in replacement) with dramatically better performance and storage efficiency. This is the most impressive bootstrapping story in the observability space, proving that a focused, high-performance open source tool can compete against well-funded startups.

Case Study: The Grafana Playbook

The LGTM Stack

The AGPLv3 Strategy

In 2021, Grafana Labs moved Grafana, Loki, and Tempo from Apache 2.0 to AGPLv3 to prevent cloud providers (AWS, Azure, GCP) from offering managed services without contributing back. Key nuances:

• AGPLv3 is OSI-approved — still “true” open source (unlike BSL or SSPL)
• Plugins, agents, and some libraries remain Apache 2.0
• Grafana Enterprise and Cloud are commercially licensed
• The license deters cloud providers from competing while keeping the community intact

Why It Works

1. Grafana is the de facto visualization layer. Even Datadog and New Relic users often pipe data into Grafana for dashboards. This makes Grafana the “control plane” of observability
2. Each component is best-of-breed. Loki is the cost leader for logs (indexes metadata only, not full text). Mimir scales Prometheus to petabytes. Tempo is the simplest tracing backend
3. Self-hosted is free, Cloud is paid. Companies self-host to save money, then migrate to Grafana Cloud as they grow and operational overhead becomes a burden. Natural upsell path
4. The AI wave benefits them. Anthropic, Lovable, and OutSystems are Grafana Cloud customers. AI companies generate massive telemetry (GPU metrics, inference traces, token-level logging) and need cost-effective observability

The ClickHouse Convergence

ClickHouse (41K GitHub stars) is emerging as the default storage engine for cost-conscious observability platforms. Its columnar architecture delivers millisecond query times on terabytes of log data at a fraction of Elasticsearch’s storage cost.

Who’s Building on ClickHouse

• SigNoz (24.2K stars, $7M funding) — the “open source Datadog alternative,” OTel-native, built entirely on ClickHouse
• HyperDX / ClickStack — acquired by ClickHouse in March 2025. Released as ClickStack (May 2025): single ClickHouse database for logs, metrics, traces, and session replays. SQL joins across all signal types
• Uptrace — OpenTelemetry + ClickHouse-based observability
• VictoriaLogs — VictoriaMetrics’ log solution (though not ClickHouse-based, competing in the same cost-efficient space)

Why ClickHouse Wins for Observability

Compression

Columnar storage compresses log data 10–20x better than row-oriented databases. TB/day becomes manageable

Query speed

Millisecond aggregation queries on billions of rows. The analytical workload of observability (group-by, percentiles, top-N) is ClickHouse’s sweet spot

SQL

Standard SQL (with extensions) instead of proprietary query languages. Lower learning curve. ClickStack enables SQL joins between logs and traces

Cost

90%+ savings vs. Elasticsearch at multi-TB/day scale. This is the single biggest driver of adoption

The signal: ClickHouse Inc.’s acquisition of HyperDX and release of ClickStack shows the database company is going all-in on observability as a first-class use case. This creates a gravitational pull for any new observability startup — why build your own storage when ClickHouse is purpose-built for your workload?

eBPF: Zero-Instrumentation Observability

eBPF (extended Berkeley Packet Filter) allows programs to run in the Linux kernel without modifying kernel source code. For observability, this means capturing requests, metrics, and traces without instrumenting applications — no SDK, no code changes, no restarts.

Why eBPF Matters

• Zero code changes: capture HTTP requests, database queries, DNS lookups at the kernel level
• Low overhead: kernel-level collection is more efficient than user-space agents
• Language-agnostic: works regardless of programming language or runtime
• Network visibility: deep packet inspection and network flow analysis for free

Key Players

OpenTelemetry eBPF Instrumentation (OBI)

In 2025, the first alpha release of OBI was announced — a collaborative project based on Grafana Beyla, donated by Grafana Labs with contributions from Splunk, Coralogix, Odigos, and others. This represents the standardization of eBPF-based observability under the OpenTelemetry umbrella. When OBI matures, it will further commoditize data collection and make vendor differentiation entirely about the backend.

AI & AIOps in Observability

Every major vendor now offers AI-powered observability features. The differentiation lies in whether AI is truly embedded in the core product or bolted on as an assistant layer.

Dynatrace’s Causal AI: The Technical Differentiator

Most observability AI uses correlational analysis: “these events happened at the same time, so they might be related.” Dynatrace’s Davis AI uses causal analysis: deterministic root cause identification based on dependency graphs and topology. This produces fewer false positives and more actionable results. It’s the only approach that doesn’t degrade with noisy, high-cardinality data — which is exactly what modern microservice architectures produce.

Dash0: AI-Native from Day One

Founded in 2023 by Mirko Novakovic (who previously founded Instana, acquired by IBM), Dash0 is the most interesting new entrant. With $44.5M raised and 270+ customers in nine months, it’s building observability with AI as the core interaction model from the start, not retrofitting AI onto a traditional dashboard-and-alert product.

Log Management: From ELK to the Modern Stack

Why ELK Is Being Replaced

The ELK stack (Elasticsearch, Logstash, Kibana) dominated log management for a decade but struggles at modern scale:

• Resource consumption: full-text indexing of all log content requires massive compute and storage
• Complexity: operating production ELK clusters at scale requires dedicated teams
• Cost: Elasticsearch storage costs are 10–20x higher than columnar alternatives

The Modern Log Architecture

The Modern Pattern

1. Collection: Fluent Bit or Vector (lightweight, high-performance)
2. Pipeline: OpenTelemetry Collector (vendor-agnostic routing and transformation)
3. Storage: Loki (cost-effective, indexes metadata only) or ClickHouse (blazing query speed) or both
4. Visualization: Grafana

This replaces the monolithic ELK stack with composable, best-of-breed components that can be mixed and matched. The key innovation: Loki indexes only labels (metadata), not full log text, delivering 10x storage cost reduction versus Elasticsearch. For use cases that need fast full-text search over massive log volumes, ClickHouse offers sub-second analytics on terabytes.

Pricing Models & Why They Matter

The pricing trap: Datadog’s multi-dimensional pricing (per-host + per-GB + per-custom-metric + per-span) means organizations often can’t predict their bill. Enterprise customers negotiate 10–30% discounts at higher volumes, but the fundamental per-GB model means costs scale linearly with growth. At multi-TB/day, annual bills reach millions — which is why self-hosted alternatives and ClickHouse-based solutions gain traction at exactly the customer segment Datadog earns the most from.

Business Models in Observability

The Winning Pattern for New Entrants

1. Build on OTel-native foundations (don’t create proprietary agents)
2. Use ClickHouse for storage (90%+ cost reduction vs. Elasticsearch)
3. Open source the core product for community and trust
4. Monetize via managed cloud service + enterprise features (SSO, RBAC, compliance)
5. Differentiate on AI-powered root cause analysis, not just dashboards

Opportunities & Gaps

1. The “Datadog for AI Companies”

AI companies generate fundamentally different telemetry: GPU utilization, inference latency distributions, token-level logging, prompt/completion traces, model versioning, hallucination detection, and cost-per-inference metrics. Traditional observability tools don’t model these concepts natively. Coralogix acquired Aporia (AI observability) for this reason. There’s room for a purpose-built platform.

2. Observability FinOps

IBM bought Apptio for $4.6B to manage IT spend. But there’s no tool that specifically manages observability spend — showing which teams, services, and log sources are driving costs, recommending sampling strategies, and enforcing budgets. The $65M–$170M Datadog bills create a massive pain point that no one is directly solving.

3. Cross-Signal Correlation

The traditional “three pillars” (logs, metrics, traces) are still stored and queried as silos in most platforms. ClickStack’s SQL-join approach across signal types is a step forward, but the real opportunity is automatic correlation: when an alert fires on a metric, automatically surface the relevant logs and traces without manual querying.

4. SME Observability

Datadog is enterprise-priced. Self-hosted Grafana requires ops expertise. There’s a gap for opinionated, simple, affordable observability for teams of 5–50 engineers who don’t want to choose between 15 configuration options but need more than uptime monitoring. Better Stack ($3.4M revenue, profitable) is scratching this surface but the market is vastly underserved.

5. Continuous Profiling as the Fourth Pillar

Logs, metrics, and traces tell you what happened. Continuous profiling tells you why it happened at the code level (which function consumed CPU, which allocation caused GC pressure). Grafana acquired Pyroscope. Datadog added continuous profiling. But standalone tooling is immature and the category is wide open.

6. Edge and IoT Observability

Cloud-centric observability tools don’t work well for edge computing, IoT fleets, or embedded systems. Bandwidth constraints prevent shipping all telemetry to a central backend. Local aggregation and intelligent sampling at the edge is a largely unsolved problem.

7. Bootstrapper Opportunities

Specialized status pages + incident management

Better Stack is profitable here. The opportunity is combining status pages with deep observability integration (auto-detect incidents from metrics, auto-update status pages, auto-notify stakeholders)

Synthetic monitoring

Checkly ($32M raised, 1,000+ customers) proves demand. Code-first synthetic monitoring (Playwright-based) that integrates with CI/CD has strong developer appeal

Observability for specific stacks

Laravel, Rails, WordPress, Shopify — vertical observability that understands framework-specific metrics and failure modes. Generic tools miss the context that framework-aware tools can provide

Migration tooling

Helping companies migrate off Datadog. Dashboard conversion, alert migration, OTel re-configuration. Groundcover built an automated migration tool. There’s a consulting/SaaS opportunity here

The Bottom Line

The observability market is massive ($28.5B, growing 19–20% CAGR) and in the middle of a structural transformation. OpenTelemetry is breaking vendor lock-in. ClickHouse is collapsing storage costs. eBPF is eliminating the need for instrumentation. AI is making dashboards obsolete in favor of automated root cause analysis. And $40B+ in M&A is consolidating standalone companies into platform plays. The opportunity for new entrants is narrow but deep: AI-native observability, cost management tooling, vertical specialization, and the SME segment are all underserved. The companies that win will be the ones that treat OTel as a given, ClickHouse as the storage layer, and AI as the interaction model — not dashboards.