Observe Inc Pricing & Review 2026: Unified Observability on a Data Lake Architecture

Observe Inc runs observability on top of a streaming data lake architecture rather than a traditional time series database. The platform structures telemetry into a context graph that connects logs, metrics, and traces through semantic relationships. Unlike tools that charge per host or per seat, Observe bills strictly on data volume ingested: $0.49/GiB for logs, $0.008 per data point million (DPM) for metrics, and $0.59/GiB for traces. All compute, search, and retention within 30 days for logs and traces or 13 months for metrics is included at these rates.

Observe pricing removes the per seat licensing found in tools like Datadog or New Relic. A 20 person engineering team ingesting 30TB monthly pays the same as a 200 person team with the same data volume. According to the 2025 CNCF Observability Microsurvey, 43% of organizations cite cost as the primary barrier to improving observability coverage. Observe’s ingestion only billing addresses this directly by eliminating user seats and feature unlocks as cost multipliers.

This review examines Observe’s pricing model in depth, the technical architecture behind it, documented customer outcomes, and what production users report about actual bills compared to vendors like Datadog and Splunk.

What Is Observe Inc

Observe Inc is a SaaS observability platform built on Snowflake’s cloud data warehouse. Rather than storing telemetry in purpose built databases like ClickHouse or Prometheus, Observe lands all logs, metrics, and traces into Apache Iceberg tables on object storage. The platform then applies a context graph layer that structures this data into entities and relationships: services, hosts, pods, traces, and events all connected through foreign keys rather than text search or tags alone.

The architectural decision to use a data lake underneath means Observe can retain telemetry indefinitely at low storage cost while still enabling high speed queries. The platform compresses data at approximately 10:1 ratio on disk. A customer ingesting 10TB uncompressed telemetry per month stores roughly 1TB compressed in their Snowflake tenant. Because Observe uses Iceberg tables, teams can query historical observability data using standard SQL tools outside of Observe itself.

Observe was founded in 2017 by former Splunk executives and raised a $115M Series C in 2022 at a reported $1.2B valuation. Publicly documented customers include Capital One, RelationalAI, and Twilio Segment. The platform is available exclusively as a fully managed SaaS deployment. There is no self hosted or BYOC option.

How Observe Pricing Works

Observe charges based on uncompressed data volume ingested across three telemetry types. These are the public rates as of early 2026 from Observe’s pricing page:

• Logs: $0.49 per GiB uncompressed
• Metrics: $0.008 per DPM (data point million)
• Traces: $0.59 per GiB uncompressed

All plans include unlimited users, unlimited dashboards, unlimited alerts, and unlimited data sources. Retention is 30 days for logs and traces, 13 months for metrics. Extended retention beyond these windows costs $0.01 per GiB per month for archived data.

Compute for queries, transformations, and dashboards is included within the ingestion fee. Observe does not charge separately for query volume or dashboard views. The platform does not impose overages. If a customer exceeds their committed monthly ingestion volume, Observe works with them to adjust capacity rather than sending an overage bill.

Volume discounts and multi year commitments reduce effective rates. A customer committing to 100TB monthly ingestion across 36 months will pay materially less than the public rate card. Observe does not publish discount tiers publicly.

Cost Scenario: 30TB Monthly Ingestion for a Mid Market SaaS Team

This scenario models a production workload for a mid sized SaaS company running 100 hosts across staging and production environments. The team ingests approximately 30TB uncompressed data monthly: 20TB logs, 7TB traces, 3TB metrics. Retention is 30 days for logs and traces, 13 months for metrics. The team has 20 full platform users.

Observe Inc estimated monthly cost:

• Logs: 20TB × $0.49/GiB × 1024 GiB/TB = $10,035
• Traces: 7TB × $0.59/GiB × 1024 GiB/TB = $4,226
• Metrics: 3TB = approximately 500M DPM monthly. $0.008/DPM × 500 = $4,000
• User seats: $0 (unlimited users included)
• Retention (30 day included): $0
• Total: $18,261/month

This estimate models the workload described using Observe’s public rate card. Actual costs will vary based on data compression ratios, negotiated volume discounts, and whether metric cardinality drives DPM higher than estimated. Verify current pricing directly with Observe.

For comparison, the same workload on Datadog would cost approximately $27,000 monthly when accounting for host based APM fees, log indexing charges, and per seat infrastructure monitoring costs. The savings Observe advertises at scale appear verifiable for teams with high data volume relative to seat count.

Observe Architecture: Why a Data Lake Changes the Cost Model

Most observability platforms store telemetry in purpose built time series databases or columnar stores optimized for write heavy append only workloads. Prometheus uses a local TSDB. Datadog runs a proprietary distributed TSDB. ClickHouse underpins many self hosted observability platforms including CubeAPM. These architectures optimize for low latency queries over recent time windows but become expensive to operate as retention extends or data volume grows.

Observe instead writes all telemetry into Snowflake backed Iceberg tables stored on S3, GCS, or Azure Blob. The platform applies a transformation layer that structures raw logs, metrics, and traces into a relational model called the O11y Context Graph. Each entity (service, pod, trace, error) becomes a row in a table. Relationships between entities become foreign keys. This transforms observability from a search problem into a graph traversal and SQL join problem.

The advantage of this architecture is cost at scale. Cloud object storage costs approximately $0.02 per GB per month. Snowflake compute scales elastically and shuts down when not querying. Observe customers pay for storage and query compute separately from Observe’s ingestion fee, but both scale more predictably than running dedicated observability infrastructure.

The disadvantage is query latency for real time use cases. Because Observe’s query engine runs on Snowflake rather than an in memory TSDB, dashboards and alerts refresh more slowly than Datadog or Grafana. Observe optimizes this by pre computing common aggregations into materialized views called datasets. For teams debugging live incidents requiring sub second query response, the data lake model introduces friction.

Observe Features and Capabilities

Observe provides full stack observability across APM, infrastructure monitoring, log analytics, and Kubernetes monitoring. The platform natively ingests OpenTelemetry, Prometheus, Fluentd, and proprietary agent data. Pre built integrations exist for AWS, GCP, Azure, Kubernetes, and over 400 third party services.

Application Performance Monitoring: Observe captures distributed traces using OpenTelemetry or proprietary agents. Traces are stored as spans in the data lake and queryable via SQL or the Observe UI. The platform automatically generates service maps from trace data and surfaces RED metrics (rate, errors, duration) for each service and endpoint. Trace search supports high cardinality filtering by span attributes, tags, or resource labels.

Log Management: Logs are ingested as structured JSON or parsed from unstructured formats using built in parsers. Observe automatically indexes all log fields without requiring upfront field selection. The platform supports full text search, regex queries, and SQL based filtering. Logs are automatically correlated with traces when trace IDs are present in log lines.

Infrastructure Monitoring: The platform collects metrics from hosts, containers, and cloud services via OpenTelemetry Collector, Prometheus exporters, or cloud provider APIs. Metrics are stored as time series and queryable using PromQL compatible syntax. Pre built dashboards exist for common infrastructure components: Kubernetes clusters, AWS EC2, RDS, Lambda, and GCP equivalents.

Kubernetes Monitoring: Observe deploys as a DaemonSet into Kubernetes clusters and collects pod logs, container metrics, and Kubernetes events. The platform models Kubernetes resources as entities in the context graph: clusters, nodes, namespaces, deployments, pods, containers. This allows querying across Kubernetes layers: “show all pods in namespace X with error rate above threshold Y that are part of deployment Z.”

AI Powered Observability: Observe includes an AI SRE assistant that uses natural language queries to surface root causes and suggest fixes. The assistant operates on top of the context graph and can correlate signals across logs, metrics, and traces to answer questions like “why did service latency spike at 3pm.” The AI features are powered by LLMs trained on Observe’s structured telemetry schema.

Alerting and Dashboards: The platform supports alerting on any metric, log pattern, or trace attribute. Alerts route to Slack, PagerDuty, email, or webhooks. Dashboards use a drag and drop builder or SQL based queries. Unlike Datadog or Grafana which require learning a proprietary query language, Observe dashboards use standard SQL against Iceberg tables.

What Observe Does Not Include

Observe is a pure SaaS platform. There is no option to run Observe on premises or in a customer controlled VPC. For teams with data residency requirements or strict data egress restrictions, this rules out Observe entirely. Telemetry must leave the customer’s cloud environment and land in Observe’s Snowflake tenant.

The platform does not support real user monitoring (RUM) or synthetic monitoring natively. Teams requiring frontend performance monitoring or proactive uptime checks need to integrate third party tools like Sentry or Checkly. Observe’s APM covers backend services and APIs but does not instrument browser side JavaScript or mobile SDKs.

Observe does not provide security monitoring or SIEM capabilities. The platform focuses exclusively on performance and availability observability. Teams requiring log based threat detection, compliance auditing, or security event correlation should evaluate dedicated SIEM platforms or pair Observe with a security tool.

The platform’s query latency can be slower than traditional observability tools during high query concurrency. Because Observe runs on Snowflake compute rather than a dedicated TSDB, dashboards loading dozens of panels simultaneously may refresh more slowly than Datadog or Grafana. Observe mitigates this through pre aggregated datasets, but real time incident debugging can feel less responsive.

Observe Customer Outcomes and Documented Savings

Observe publishes customer case studies documenting cost reductions and operational improvements. These are the most concrete publicly available data points on Observe’s effectiveness at scale.

RelationalAI documented moving from Datadog to Observe and achieving 75% cost reduction while retaining all telemetry. The company cited three drivers for the switch: data security (self hosted compliance requirements relaxed after moving to Observe’s SOC2 environment), vendor neutrality (avoiding Datadog’s proprietary agent and query language), and cost efficiency. RelationalAI’s CEO stated that Observe cost one quarter the price of Datadog for equivalent coverage.

Capital One uses Observe to consolidate observability across multiple business units. The bank highlighted Observe’s ability to centralize telemetry from disparate sources into a single queryable data layer without requiring teams to standardize on a single agent or instrumentation library. The context graph model allowed Capital One to correlate events across organizational boundaries that were previously siloed.

Twilio Segment migrated from a self hosted ELK stack to Observe to reduce operational overhead. Segment documented eliminating three full time engineers previously dedicated to managing Elasticsearch clusters. The team reported faster time to insight due to Observe’s pre built integrations and automated schema discovery compared to manually defining Logstash pipelines and Kibana dashboards.

These case studies document outcomes for large enterprises with significant observability budgets. Smaller teams or startups may not see equivalent savings if their baseline costs are already low or if they are migrating from open source tools rather than expensive SaaS platforms.

What Production Users Report About Observe

Public reviews and community discussions surface both strengths and friction points that Observe customers encounter in production.

On Gartner Peer Insights, Observe holds a 4.7 out of 5 rating across 36 verified reviews. Reviewers consistently highlight cost predictability and the elimination of per seat licensing as major advantages. One infrastructure engineer wrote: “We went from budgeting for seat growth every quarter to simply forecasting data volume, which is far more predictable.”

The most common complaint in reviews is query performance during incident response. Multiple reviewers noted that dashboards and ad hoc queries can feel slower than Datadog or New Relic when debugging live issues. One SRE documented: “For post mortems and long term trend analysis, Observe is excellent. For real time war rooms where we need instant feedback, we still keep Datadog running in parallel for the first 15 minutes of an incident.”

Several reviews mention onboarding friction related to learning Observe’s dataset and transformation model. Unlike point and click observability tools, Observe requires understanding how raw telemetry is transformed into queryable entities. Teams accustomed to Datadog’s pre built dashboards or Grafana’s template variables report a steeper learning curve. Observe provides onboarding support and pre built dataset templates to mitigate this, but the abstraction layer adds cognitive overhead.

On Reddit, a thread in r/devops compared Observe to Datadog for a 50 person engineering team. The poster documented a 60% cost reduction moving to Observe but noted that their team initially struggled with Observe’s SQL based query model after years of using Datadog’s GUI. The team ultimately retained Observe after three months but kept a small Datadog deployment for real time alerting on critical services.

Observe Pricing Compared to Competitors

This comparison models the same 30TB monthly workload detailed earlier: 20TB logs, 7TB traces, 3TB metrics, 100 hosts, 20 users.

Tool	Estimated Monthly Cost	Notes
Observe	$18,261	Ingestion only, no seat fees
CubeAPM	$4,800	$0.15/GB flat, self hosted in customer VPC
Datadog	$27,000	Includes host fees, log indexing, APM, 20 users
New Relic	$25,990	CCU based or per user, 100GB free tier
Dynatrace	$21,850	Host based with log add on
Splunk	$22,500	Ingest based with search head cost
Grafana Cloud	$15,000	Prometheus, Loki, Tempo bundles

Pricing based on public rate cards as of early 2026 for a standardized usage profile. Actual costs will vary based on retention period, indexing requirements, enterprise discounts, and feature usage. Verify current rates directly with each vendor.

Observe’s cost advantage grows as user count increases relative to data volume. A 100 person team ingesting the same 30TB monthly pays identical Observe fees but would see Datadog costs rise by $30,000 in additional seat charges. Teams with high engineer to data ratios benefit most from Observe’s model.

The comparison also shows that self hosted platforms like CubeAPM deliver the lowest total cost when a team can operate the infrastructure. CubeAPM runs on the customer’s cloud account, charges $0.15/GB flat for all telemetry types, and includes unlimited retention. The tradeoff is that CubeAPM requires the customer to provide compute and storage, though CubeAPM manages the platform itself as a hosted service within the customer’s VPC.

When Observe Makes Sense and When It Does Not

Observe is the strongest fit for mid to large engineering teams with high data volume, many users, and tolerance for a SQL based query model. The platform works especially well for teams already comfortable with data warehousing concepts or those consolidating multiple observability tools into a single data layer.

Observe works well for:

• Teams with 50 plus engineers where per seat pricing compounds rapidly
• Organizations with high log and trace volume relative to infrastructure size
• Data engineering teams familiar with SQL and comfortable writing queries
• Companies requiring long term telemetry retention for compliance or trend analysis
• Teams consolidating observability across business units into a single platform

Observe is not ideal for:

• Startups or small teams under 10 engineers with low data volume where per seat SaaS tools cost less
• Teams requiring on premises or BYOC deployment for data residency
• Organizations needing sub second query response during live incident debugging
• Teams that depend heavily on pre built dashboards and want minimal configuration overhead
• Companies requiring integrated RUM, synthetic monitoring, or security features

The decision between Observe and alternatives like Datadog or self hosted platforms ultimately comes down to three variables: team size, data volume, and operational preference. Observe removes seat licensing friction but adds architectural complexity. Datadog provides instant gratification at high cost. Self hosted tools like CubeAPM offer the lowest TCO but require infrastructure ownership.

How to Migrate to Observe

Observe provides migration guides and hands on support for teams moving from Datadog, New Relic, Splunk, or self hosted stacks. The platform supports running in parallel with existing tools during transition, allowing teams to validate parity before cutting over.

Step 1: Connect data sources. Observe supports OpenTelemetry Collector, Fluentd, Prometheus remote write, and cloud provider APIs. Teams using Datadog agents can run Datadog and Observe agents simultaneously. Logs and metrics flow to both platforms during migration. For OpenTelemetry instrumented services, adding Observe as an exporter requires a configuration change only.

Step 2: Build datasets and dashboards. Observe automatically creates baseline datasets from ingested telemetry. Teams then customize datasets to match their existing dashboard structure. Observe’s dataset model differs from Datadog’s metric namespace or Grafana’s data source abstraction, so this step requires the most learning. Observe provides pre built dataset templates for common use cases: Kubernetes monitoring, AWS infrastructure, application RED metrics.

Step 3: Migrate alerts. Observe supports alerting on any SQL query result. Teams export their Datadog monitors or Prometheus alert rules and rewrite them as Observe monitors. The platform supports the same notification channels: Slack, PagerDuty, email, webhooks. Alert syntax differs from PromQL or Datadog’s query language, but the logic translates directly.

Step 4: Validate parity and cut over. Teams run Observe in parallel with their existing tool for 2 to 4 weeks. During this period, they validate that Observe captures all telemetry, dashboards match expected results, and alerts fire correctly. Once confidence is established, teams decommission the old platform.

Documented migrations from Datadog typically complete in 4 to 6 weeks for mid sized teams. Self hosted ELK stack migrations take 6 to 8 weeks due to custom Logstash pipelines requiring translation into Observe transformations.

CubeAPM as an Alternative to Observe

CubeAPM provides full stack observability similar to Observe but runs entirely within the customer’s cloud environment. The platform is built natively on OpenTelemetry and charges $0.15/GB flat for all telemetry types: logs, metrics, traces, events. Retention is unlimited at no additional cost.

Unlike Observe, CubeAPM deploys on the customer’s Kubernetes cluster or VM infrastructure. Telemetry never leaves the customer’s VPC. This makes CubeAPM the default choice for teams with data residency requirements, regulated industries, or strict data egress policies. CubeAPM is SOC 2 and ISO 27001 certified, covering both security controls and data handling.

CubeAPM includes distributed tracing, log aggregation, infrastructure monitoring, Kubernetes observability, real user monitoring, synthetic monitoring, and error tracking in a single platform. The product supports 800 plus integrations including all major cloud providers, databases, message queues, and application frameworks.

The primary tradeoff is that CubeAPM requires the customer to provide compute and storage infrastructure. CubeAPM runs on the customer’s cloud account, so the team pays their cloud provider directly for EC2, EBS, S3, or GCP equivalents. CubeAPM manages the platform, handles upgrades, and provides support, but the underlying resources are customer owned. This results in lower total cost but requires infrastructure capacity planning.

CubeAPM pricing is straightforward: $0.15/GB ingested, unlimited users, unlimited retention, no feature gates. A team ingesting 30TB monthly pays $4,800 regardless of user count or how long they keep data. For the scenario modeled earlier, CubeAPM costs 74% less than Observe and 82% less than Datadog.

Teams considering Observe should evaluate CubeAPM if:

• Data residency or compliance rules prohibit sending telemetry to third party SaaS
• The team has Kubernetes or cloud infrastructure capacity to host the platform
• Total cost of ownership including infrastructure is more important than zero ops burden
• The team wants OpenTelemetry native instrumentation without vendor specific agents

CubeAPM offers a free trial and proof of concept deployments to validate fit before committing. Migration from Observe, Datadog, or New Relic typically completes in under two weeks for teams already using OpenTelemetry.

Disclaimer: The information in this article reflects the latest details available at the time of publication and may change as technologies and products evolve. Features, pricing, and plan limits can change over time. Always verify the latest information directly with the vendor before making purchasing or deployment decisions.

Frequently Asked Questions