Imply
UnclaimedDecouple your observability and security tools for faster, cheaper, and more open data analysis.
Visit WebsiteTL;DR - Imply
- Decouples observability and security data stacks for efficiency.
- Enables faster queries and significant cost reductions without workflow changes.
- Integrates with AI and BI tools for advanced data analysis and insights.
Pricing: Paid only
Best for: Enterprises & pros
4.5/5 across review platforms
Pros & Cons
Pros
- Reduces costs by over 70%
- Achieves 10x faster queries
- Requires zero workflow changes or migrations
- Supports high-fidelity data for advanced analytics and AI
- Offers flexible deployment options (A-Series for performance, D-Series for cost-optimization)
Cons
- Pricing model is usage-based, which can be complex to estimate for new users
- Requires understanding of data capacity and performance tiers (A-Series vs. D-Series)
Ratings Across the Web
4.5(5 reviews)
Ratings aggregated from independent review platforms. Learn more
Preview
Key Features
Observability Warehouse architectureSeamless integration with existing toolsBest-in-class data efficiency and query speedZero disruption to current dashboards, queries, and agentsResource-optimized data storageProven scalability for large datasetsBroad ecosystem integrationsFull fidelity data retention
Pricing Plans
Free TrialStarter
Starts at $100/month
- Small environment for proof-of-concept evaluations
- Project size limited to 25 GB
- Variable performance
Standard
Starts at $600/month
- Right-sized environments at two performance levels for production use
- Project sizes up to 9.6 Terabytes
- Uptime SLA of 99.9%
- Private networking options
Custom
Contact us for pricing
- Super-flexible environments with highest scale for production use
- Unlimited project size
- Uptime SLA of 99.95%
- Highly customizable environment
- Scheduled upgrades
About Imply
By Toolradar Team·
Imply provides an Observability Warehouse, a new data layer designed to decouple and optimize existing observability and security stacks. It allows organizations to store more data, support diverse use cases, and reduce costs without disrupting current workflows. By integrating seamlessly with existing ingestion and visualization tools, Imply enables users to ingest data once and utilize it across various platforms, ensuring best-in-class efficiency with faster queries and optimized storage.
The platform is built for scale, offering full fidelity data retention and consistently fast performance. It transforms raw observability data into actionable insights, models, and answers, making it AI and BI ready. Users can interact with their data conversationally through AI tools like Claude and ChatGPT, feed high-fidelity data into machine learning pipelines, and visualize insights in BI tools such as Tableau and Power BI. Imply aims to break the 'black box' of traditional observability, providing measurable benefits like significant cost reductions and query speed improvements.
How we evaluate tools →Source: imply.io
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Imply FAQ
How does Imply Lumi achieve a 70%+ cost reduction and 10x faster queries compared to traditional observability stacks?
Imply Lumi achieves these benefits by implementing an Observability Warehouse architecture that decouples data storage from processing. This allows for more efficient data compression (typically 10-15x), optimized resource allocation, and a pay-only-for-what-you-use model, leading to significant cost savings and enhanced query performance across large datasets.
Can Imply integrate with my existing custom-built data ingestion pipelines or does it require specific connectors?
Imply is designed for seamless integration and supports a broad range of ecosystem integrations. It allows you to ingest data once and use it anywhere, implying compatibility with various ingestion methods without requiring a complete overhaul of your custom pipelines. The platform aims for zero disruption to your existing workflows.
What is the difference between A-Series and D-Series projects in Imply Polaris, and how should I choose between them?
A-Series projects are high-performance environments optimized for use cases requiring sub-second query responses at high concurrency. D-Series projects are cost-optimized environments designed for running low-latency queries on large amounts of data. You should choose A-Series for critical, real-time analytics with high user interaction, and D-Series for large-scale data analysis where cost efficiency is a primary concern.
How does Imply ensure 'full fidelity' data retention, and what are the implications for historical data analysis?
Imply ensures full fidelity data retention by storing all your observability data without aggregation or downsampling. This means that even months or years of historical data remain granular and accessible, which is crucial for detailed investigations, feeding accurate machine learning models, and performing comprehensive business intelligence analysis over extended periods.
Beyond Claude and ChatGPT, what other specific AI tools or platforms can directly access and query data stored in Imply?
Imply is designed to be AI-ready, allowing conversational access to data through various AI tools. While Claude and ChatGPT are explicitly mentioned, the platform's open and decoupled architecture suggests compatibility with other AI tools that can leverage its data layer for instant answers and insights, particularly those capable of integrating with BI tools or direct data access APIs.
Source: imply.io