nOps is an automated cloud cost optimization platform that combines AI-powered cost visibility with autonomous commitment management. It continuously purchases and rebalances AWS, Azure, and GCP savings commitments to raise your effective savings rate, while giving FinOps, engineering, and finance teams hourly visibility into multicloud, Kubernetes, SaaS, and AI costs.
Keebo is an autonomous cloud data warehouse optimization platform that continuously rightsizes Snowflake and Databricks compute, cutting waste while protecting query performance and SLAs.
nOps is an automated cloud cost optimization platform that combines AI-powered cost visibility with autonomous commitment management. It continuously purchases and rebalances AWS, Azure, and GCP savings commitments to raise your effective savings rate, while giving FinOps, engineering, and finance teams hourly visibility into multicloud, Kubernetes, SaaS, and AI costs.
Autonomous commitment management
An autonomous engine maps your usage patterns hourly, buys the optimal mix of Reserved Instances, Savings Plans, and committed use discounts, and rebalances continuously to keep discounts high without locking you into long commitments.
Cost visibility and allocation
See multicloud, Kubernetes, SaaS, and AI spend at hourly granularity, with business context, forecasting, anomaly detection, and unit economics that show what actually drives cost.
AI cost intelligence
Allocate hourly AI spend to the model, account, team, customer, and feature behind it, covering providers such as Amazon Bedrock, OpenAI, Claude, and Cursor, with recommendations for token efficiency, model choice, and caching.
Savings tracking and workflows
Track Effective Savings Rate, commitment coverage, and realized savings in real time, and route alerts and tasks into tools like Slack, Jira, Datadog, and Snowflake.
強み・メリット
+Automated commitment purchasing and rebalancing removes recurring manual work
Savings-share pricing aligns platform cost with measured results
+Hourly multicloud, Kubernetes, SaaS, and AI cost visibility in one platform
+AI cost allocation by model, team, and feature for emerging AI workloads
+Broad commitment coverage and integrations with Slack, Jira, Datadog, and Snowflake
制約・デメリット
–Pricing for the visibility layer is quote-based and scales with cloud spend
–Value depends on having meaningful committed-spend volume to optimize
–Savings-share model means you hand over a percentage of realized savings
–Full value requires connecting cloud accounts and granting read access
Keebo
Keebo is an autonomous cloud data warehouse optimization platform that continuously rightsizes Snowflake and Databricks compute, cutting waste while protecting query performance and SLAs.
Autonomous warehouse rightsizing
Agentic AI adjusts warehouse sizing and compute settings in both directions, holding performance while removing wasted capacity.
Hands-off optimisation
Continuous tuning runs in the background with no query rewriting, code changes or engineering tickets required.
Workload intelligence and observability
Query behaviour, bottlenecks and inefficient patterns are surfaced so data and FinOps teams can act on the same evidence.
Metadata-only, out-of-band architecture
The platform runs out-of-band and, per the vendor, never reads query text or result data, so there is no query-path dependency.
強み・メリット
+Optimisation continues without engineer tickets or code changes
+Out-of-band, metadata-only design adds no query latency
+Covers both Snowflake and Databricks, including serverless SQL
+Workload intelligence gives FinOps and data teams shared visibility
+Reported 27% average savings and hundreds of engineering hours reclaimed
制約・デメリット
–No published list price; onboarding requires a sales conversation
–Focused on Snowflake and Databricks, so other warehouses are out of scope
–Automated tuning still benefits from data-team oversight during ramp-up
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