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.
Outerbase is an AI-native workspace for exploring, querying and visualizing data across SQL and NoSQL databases. It pairs an ask-in-plain-English assistant with a spreadsheet-style table editor, dashboards and a shared data catalog, so engineers and analysts can work on the same data without fighting tooling. As of 2025 the team has joined Cloudflare.
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
Outerbase
Outerbase is an AI-native workspace for exploring, querying and visualizing data across SQL and NoSQL databases. It pairs an ask-in-plain-English assistant with a spreadsheet-style table editor, dashboards and a shared data catalog, so engineers and analysts can work on the same data without fighting tooling. As of 2025 the team has joined Cloudflare.
Ask EZQL natural-language queries
Type a question in plain English and get an instant answer drawn from your connected data, without writing SQL by hand.
AI query editor and auto charts
The AI editor reviews your SQL, corrects mistakes and suggests improvements, then turns result sets into presentation-ready charts automatically.
Spreadsheet-style table editor
A snappy grid built from scratch for fast navigation of large tables, with customizable table plugins and edit-in-place controls.
Self-serve dashboards
Compose analytics boards manually or hand them to the AI, then embed charts anywhere using open-source web components.
強み・メリット
+Broad database coverage across SQL and NoSQL engines, so you rarely need a second tool
+Genuinely useful AI layer: natural-language queries, query fixing and automatic charting in one place
+Table and chart components are open source and embeddable in your own applications
+Strong security posture with SOC 2 Type 2, HIPAA, encryption, role-based access and private AI models
制約・デメリット
–No public self-serve pricing, so you must contact sales to get started commercially
–The natural-language layer is only as good as the underlying schema documentation
–Self-hosting is limited — the managed product is the main path, which may not suit air-gapped environments
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