PingThings

Name: PingThings
Brand: PingThings

Time-series intelligence for physical systems

Est 2017|Valuation $29M|Raised $5.6M

LinkedIn 1K|Website|FTEs 24|HQ Anaheim, California, US

Published Aug 18, 2026

01Common complaints

- The organization requires transparent pricing up front for budgeting/approval and cannot engage in a sales-led quote process. - Procurement requires mainstream, independently verified reviews (G2/Capterra/TrustRadius) and a large public customer footprint. - The organization is already deeply standardized on AVEVA PI and switching costs are prohibitive. - Telemetry volumes are not extreme (e.g., traditional SCADA rates) and a simpler stack will provide sufficient performance with lower implementation risk. - The organization requires strict, publicly documented security/compliance artifacts (SOC2/ISO) prior to any engagement; such documentation was not clearly posted on public pages reviewed.

Deal disqualifiers (poor fit)

The organization requires a plug-and-play SaaS with self-serve pricing and instant onboarding.
Procurement requires publicly documented SOC 2 / ISO certifications before engagement.
The organization is already deeply standardized on AVEVA PI and the switching cost outweighs potential performance benefits.
Telemetry volumes are moderate and can be served by standard TSDB/historian stacks with lower implementation risk.

Publicly visible limitations

Public documentation does not clearly specify feature limits, SLAs, or constraints; much of the public content is sales-oriented.

Potential limitations / risk areas (based on available signals)

- Integration complexity and onboarding time (noted in third-party complaints). - API usability concerns (third-party complaint). - Model/prediction accuracy expectations: if the platform name is interpreted as implying out-of-the-box accuracy, the risk of dissatisfaction is increased. - Community/library activity: some PingThings-adjacent Python tooling (e.g., btrdbextras) shows older release activity, which may imply slower public open-source iteration; this does not necessarily reflect private product development velocity.

02Why they buy

Competitive advantages (evidence-weighted)

These are primarily derived from vendor technical positioning rather than independent reviews:

Focus on high-throughput dense time-series ingestion and multi-resolution access.
Utility-centric context modeling (geospatial, topology) treated as core concepts.
An integrated product that combines ingest, TSDB, analytics, and dashboards/app building.

Where competitors clearly have advantages

Review/market visibility: InfluxDB and PI System have more third-party reviews and community content.
Pricing transparency: InfluxDB publishes clear billing dimensions; PI and PingThings are more opaque.
Ecosystem maturity: PI System has decades of industrial integrations and partner relationships.

03Field reports

Review platform availability (G2, Capterra, TrustRadius)

No clearly discoverable, dedicated listing on major review platforms was found during research. This lack of mainstream review-platform presence limits the ability to perform review-based validation.

This limitation increases the relative importance of customer reference calls, proof-of-concept performance testing, security reviews, and contract/SLA scrutiny for procurement decisions.

Third-party review snippets (non-major platform)

A niche review site hosts multiple highly negative comments about the product, citing themes such as difficult setup, API friction, and dissatisfaction with predictive results. That site is not a mainstream B2B review platform with widely recognized verification standards; authenticity and representativeness are therefore unclear. Nevertheless, the reported themes (integration friction, API difficulty, model quality concerns) are specific enough to be treated as potential risk signals to validate during evaluation.

Why customers might select PredictiveGrid (inferred from positioning)

Need for higher ingest/query throughput than some historians/TSDBs provide.
Desire to unify telemetry with context (asset metadata, geospatial, topology).
Interest in an integrated environment to build Python-based analytic applications and dashboards on top of operational sensor data.

Why some customers might leave / not purchase (inferred + review signals)

Implementation complexity (protocol support, schema/context modeling, onboarding).
Disappointment with predictive results if models do not generalize or if data quality is poor.
Preference for established ecosystems (PI System) or more standard TSDB stacks (InfluxDB/Timescale + data lake).
Lack of transparent public proof around security certifications and SLAs.

Public case studies

Public marketing and technical descriptions are present on vendor pages, but there are few detailed, independently verifiable case studies with documented methodology.

Example partnership-style evidence

Industry press described an integrated solution combining power-quality/microPMU sensors with the PredictiveGrid analytics platform to provide visibility into power quality anomalies. This indicates ecosystem engagement and a plausible use case, but it is not a quantified ROI case study.

Vendor-published benchmark claims

Vendor materials present benchmark claims intended to demonstrate high ingest and query performance and support for very large sensor counts. Critical evaluation: - Benchmarks are conceptually credible but lack publicly accessible test harnesses and third-party replication. - Evaluators should request benchmark methodology, hardware specifications, workload characteristics (point size, compression, query patterns), failure modes and tail latency, and cost per throughput unit to validate vendor claims.

04Best fit

Company sizes

Best fit appears to be mid-market to enterprise utilities and grid operators, and large industrial firms with high-frequency telemetry — effectively mid-sized to large organizations.

Industries and verticals

Electric utilities (transmission/distribution)
Grid-edge / distributed energy resource (DER) monitoring
Industrial asset monitoring in energy transition contexts

Roles and personas

VP/Director of Grid Modernization / Digital Grid
OT/IT architecture leads
Data platform engineering
Power systems engineers
Data scientists building operational analytics

Qualification criteria (good fit)

The organization has (or can create) clean, governed telemetry pipelines.
High ingest/query performance at fine time resolution is required.
Context modeling (assets, geospatial, topology) needs to be tightly coupled to time series.
The organization is willing to run a structured pilot and performance tests.

05The pitch

What the product does (core value proposition)

PingThings offers the PredictiveGrid platform, positioned as a high-density time-series and telemetry data platform tailored for electric utility grid sensor data (synchrophasors/PMUs, AMI, power quality, SCADA) and adjacent asset-intensive industries. Product materials describe capabilities for: - ingesting and storing large time-series volumes, including high-frequency streams, - enabling fast retrieval and multi-resolution visualization, - supporting analytics and machine-learning workflows (anomaly detection, prediction), and - providing APIs plus dashboards and Python/Jupyter-style workflows. Vendor materials present high-performance benchmark claims and stress-test results intended to demonstrate extreme ingest/query throughput and large-scale PMU support. Those claims are vendor-published and do not include publicly accessible, independently replicated methodologies.

Target market and market positioning

Primary target: electric utilities and grid-edge / energy-transition stakeholders (grid reliability, power quality, renewable integration). The vendor also cites applicability to transportation, geological, healthcare, and smart cities, but marketing and materials are primarily utility-centric.

Positioning is closer to an industrial time-series platform with an analytics/application layer than a generic time-series database (TSDB).

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