groundcover

Name: groundcover
Brand: groundcover
Rating: 4.7 (58 reviews)

AI NativeLeader

Monitor everything you run on the cloud without compromising on cost, granularity, or scale.

Est 2021|Valuation $520M|Raised $159.5M|12 Ref Customers

LinkedIn 10K|Website|Web traffic 53K|FTEs 140|HQ Tel Aviv, Israel

MarketsInfrastructure Monitoring Observability Platform Application Performance Monitoring (APM)

Published Oct 15, 2026

01Common complaints

- If you are not Kubernetes-centric - groundcover’s differentiation is strongest in Kubernetes/cloud-native environments; if you are mostly traditional VM/on-prem monitoring, incumbents or specialized tools may fit better. - If you need the most mature “out-of-the-box” workflows and ecosystem - Evaluation threads indicate incumbents like Datadog and Dynatrace are “super easy to use” with mature workflows. If time-to-adoption and polished UI/UX matter more than cost/data-residency, competitors can be a better fit. - If BYOC is a governance or operational blocker - BYOC means infrastructure resources, cloud security controls, and cost governance move partly to you. If your org cannot easily approve or operate in-account vendor-managed deployments, SaaS models may be simpler. - If your use case requires deep custom business telemetry - “Zero instrumentation” via eBPF can reduce effort, but many organizations still want custom spans/attributes/events that require OpenTelemetry instrumentation. - If you need independently verified ROI - Vendor claims of 60–90% savings and specific “save 87%” comparisons may not generalize; if procurement requires third-party validated ROI studies, you may need deeper diligence. - Review footprint / validation gap - Gartner Peer Insights sample size is currently very small (1 rating), reducing confidence for enterprise buyers who rely on peer review volume. - eBPF visibility boundaries - eBPF can provide deep kernel/infrastructure-level insights, but it may not capture all business-level context without OTel instrumentation. - Pricing and ROI claims depend on assumptions - Vendor TCO comparisons show dramatic savings but are not fully transparent in methodology in accessible snippets. - Security feature disclosure - While certifications/compliance are listed in the Trust Center, detailed public documentation of enterprise features (SAML/SCIM/audit logs/encryption specifics) was not captured in accessible excerpts and may require requesting Trust Center artifacts.

02Why they buy

- BYOC architecture with the backend inside the customer cloud/VPC - Differentiates on data control/residency and potentially reduces vendor lock-in around data storage. - eBPF-based, low/zero-code-instrumentation narrative - Reviewers highlight simple installation and local data storage. - Pricing strategy designed to decouple cost from telemetry volume - Node-based pricing can be attractive for log-heavy environments; community discussions regularly point to observability costs as a major pain. - Balanced view: where competitors have advantages - Datadog/Dynatrace/New Relic generally have: - Larger customer bases and more third-party validation - More mature feature breadth and integrations - More battle-tested enterprise referenceability

03Field reports

- Overall review footprint and representativeness - Gartner Peer Insights shows a small sample size for groundcover (1 rating), limiting statistical confidence. - G2 shows a larger but still modest sample size (26 reviews). - Community threads (Reddit) include both praise and skepticism; some posts are clearly vendor-affiliated (e.g., “founding engineering team” thread), so treat those as marketing/community engagement rather than independent reviews. - Review platform highlights (positive) - G2 review excerpt (5/5, CTO/Co-founder; dated 1/15/2024): - “Very simple installation and all data is stored locally in the company's infrastructure.” - G2 comparison page snippets suggest strong perceptions of support responsiveness (G2 comparison text notes support quality is highly rated), though this is a platform-generated summary and should be validated by reading multiple individual reviews. - Review platform highlights (negative / caution) - The captured excerpt does not include low-star quotes directly; however, G2 indicates the presence of 2-star reviews in the seller/product listing. - Gartner Peer Insights sample is too small to provide robust negative patterns. - Community / social quotes and themes (mixed) - Evaluator skepticism on maturity: - Reddit (r/devops) evaluation thread: “can it really stack up against the more mature observability solutions in the market?” - Critique of marketing vs product development: - Reddit (r/Observability) comment in a broader Prometheus vs Datadog comparison thread: “I wish groundcover would invest as much in product development as they do on marketing blog posts and CxO ‘hot takes’ on LinkedIn!” - Cost-savings anecdote (positive but unverifiable): - Reddit (r/sre) removed thread snippet claims a move “from new relic to GC” and “slashed our observability prices by 3x.” - Because the post is “[Removed]” and details are limited, treat this as anecdotal and not independently verifiable. - Observability cost discussion referencing groundcover as simple pricing vector: - Reddit (r/Observability) pricing thread includes a claim “groundcover is the best - Single SKU product - just 1 pricing vector.” This thread includes vendor/competitor participants and should be interpreted carefully. - Why customers chose groundcover over alternatives (as suggested by sources) - Desire to keep telemetry data in their own cloud/VPC (privacy/control), per BYOC messaging and reviewer quote about local storage. - Desire to reduce cost unpredictability vs ingestion-priced SaaS (frequently referenced in community conversations). - Desire to reduce instrumentation effort via eBPF-based collection. - Why some customers left for competitors (churn reasons) - Direct churn stories away from groundcover were not clearly available in accessible sources. - However, evaluation threads indicate concern about maturity and workflow polish relative to incumbents. - Specific outcomes customers achieved (with metrics when available) - Vendor/customer-story pages and community anecdotes mention large % savings (e.g., “cut costs in half,” “3x cheaper”), but most are either vendor-published case studies or unverifiable community posts. These should not be treated as guaranteed outcomes. - Vendor-published customer stories (examples found) - CommonGround customer story page exists (details not fully extracted in this run). - groundcover docs highlight examples such as Nobl9, Tracr (as referenced on the docs introduction page), claiming cost reductions and better coverage. - Critical evaluation of case studies - Many quantitative claims appear vendor-published; methodology (baseline configuration, retention, sampling policies, included modules, negotiated discounts, and internal labor costs) is often not disclosed publicly. - BYOC economics can be favorable, but case studies may omit the customer’s cloud infrastructure costs for storage/compute, and the engineering time to operate/secure the in-account deployment.

04Best fit

- Company sizes (estimated) - Likely strongest fit for: - Mid-market to enterprise engineering orgs running Kubernetes in production - Roughly 50–2,000+ employees (wide band), where observability data volumes and costs become material - Very small teams (single cluster, low telemetry volume) may not realize large benefits versus simpler stacks. - Industries and verticals - Cloud-native SaaS, fintech, gaming, and any K8s-heavy digital product org. - G2 filters show reviewers across multiple industries including financial services (small sample). - Roles and personas - Decision makers: VP Engineering, Head of Platform/SRE, DevOps lead, Security/GRC stakeholders (because BYOC changes data handling). - End users: SREs, platform engineers, backend engineers, on-call responders. - Pain points the product addresses - High observability bills driven by log ingestion and retention - Data residency / privacy constraints that complicate SaaS observability - Time and complexity of manual instrumentation - Deal disqualifiers - Teams that require the broadest mature ecosystem on day one (extensive third-party apps, advanced RUM/synthetics/security add-ons). - Teams unwilling to run observability backend infrastructure inside their own cloud account (even if “managed”).

05The pitch

- What the product does (core value proposition in objective terms) - groundcover is a cloud-native, Kubernetes-focused observability platform that unifies logs, metrics, traces, and Kubernetes events, with data collection heavily based on kernel-level eBPF telemetry. - The vendor positions “zero instrumentation” (no app code changes/SDKs) and “no sampling” as a differentiator, aiming to provide high-fidelity visibility with reduced manual setup. (Docs emphasize eBPF-based auto-collection and “in minutes” time-to-visibility.) - The core architectural claim is a BYOC (Bring Your Own Cloud) deployment model where the backend runs inside the customer’s cloud/VPC; telemetry data is collected/stored/analyzed in the customer environment while groundcover manages the platform. - Target market and market positioning - Primarily targets teams running Kubernetes / modern cloud-native stacks that want full-stack observability while keeping telemetry data in their own cloud account (privacy / control) and seeking more predictable cost than ingestion-based SaaS observability. - Often positioned as a cost-control alternative to Datadog / New Relic / Dynatrace, with emphasis on “predictable node-based pricing” rather than per-GB ingest. - Company background (founding year, HQ, key leadership) - Founded: 2021. - HQ: Tel Aviv, Israel (Gartner Peer Insights listing). - Employee band: 51–200 (Gartner Peer Insights listing). - Public-facing leadership referenced in community posts includes Aviv Zohari (Field CTO / founding engineer) in a Reddit thread; treat this as self-reported. - Funding and growth: investment rounds, acquisitions, estimated customer count/revenue if public - Seed round: shown on Crunchbase as Jan 1, 2022 (investors listed include Angular Ventures and Heavybit; Crunchbase details may be partially obfuscated). - Series B: April 10, 2025 press release states $35M Series B led by Zeev Ventures, with participation from Angular Ventures, Heavybit, and Jibe Ventures. - Independent estimate of customer count/revenue is not reliably public in the sources accessed. - No acquisitions identified in sources accessed. - Pricing model with specific tiers and costs where available - groundcover publishes a BYOC-oriented, node-based pricing approach: cost is based on the monthly average number of monitored Kubernetes nodes (and/or Linux hosts), rather than log/trace/metric volume. - Public pricing page indicates “Free, Team & Enterprise” plans, but the page is heavily interactive; exact per-node dollar figures were not clearly extractable from the captured view. - Vendor also markets a “Total Cost of Ownership” comparison tool (e.g., a sample output showing groundcover $199K vs Datadog $1.47M annually) but the methodology and assumptions are not fully disclosed in the snippet captured. - Marketplace listing (Microsoft marketplace) reiterates BYOC and “unlimited data” messaging, but does not provide a clear unit price. - Total cost of ownership considerations (implementation, training, hidden costs) - Potential cost advantages (if vendor claims hold) - Node-based pricing can reduce exposure to unpredictable ingestion spikes, especially for log-heavy environments. - BYOC may reduce “vendor data egress” concerns and may simplify internal privacy approvals. - Potential cost additions / hidden costs - BYOC shifts infrastructure costs onto the customer’s cloud bill (storage/compute for the observability backend, plus any required dedicated node(s) in each cluster for the sensor). The docs explicitly mention the cost of hosting the backend in your environment as part of pricing/TCO. - Operational overhead may be lower than fully self-managed OSS stacks, but teams still must approve, provision, and govern the in-account deployment. - Training/time-to-value depends on maturity of workflows; some users evaluating from Datadog/Dynatrace cite those tools as “super easy to use,” implying groundcover may be compared against very polished UX. - Key competitors in the space (list 4-6 direct alternatives) - Datadog - Dynatrace - New Relic - Grafana Cloud / Grafana stack (Prometheus/Loki/Tempo) - Splunk Observability - Honeycomb - (Also frequently adjacent in discussions: SigNoz, Elastic Observability)

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