Production-Aware AI Agents
for Apache Spark
An agentic platform that optimizes Spark performance and cuts infrastructure costs, proven to deliver up to 100X performance and cost improvements at leading enterprises
Open source users at
Spark is complex.
Hard to debug,
expensive to run.
Meet our agentic platform
Turn your IDE into a production-aware Spark engineer
10-100X
faster Spark jobs
50-90%
infrastructure cost cut
Fix and optimize Spark jobs right in your IDE.
A production-aware IDE extension for Cursor, VS Code, and IntelliJ. Chat with a Spark expert that knows your real production runs. Root-cause failures, generate the fix, and ship code-level optimizations without ever leaving your editor.
100X cost reduction at SimilarWeb →Try it Now
Real results from real teams
See how DataFlint agents helped SimilarWeb cut costs 100X and accelerate execution 13X.
Single job optimization:
Results represent one critical Spark job out of many in SimilarWeb's data pipeline
“DataFlint has been instrumental in helping us achieve engineering excellence in our Apache Spark workloads. Using their platform, we were able to perform deep diagnostics on our Spark jobs, uncovering inefficiencies such as skewed joins, underutilized executors, and suboptimal shuffle operations.”
Works everywhere you run Spark
Our agents connect to all major Spark platforms, from cloud services to on-premises deployments.
AWS EMR
Databricks
Fully supported
Google Dataproc
Fully supported
Microsoft Fabric
Fully supported
Kubernetes
Fully supported
On-Premises
Fully supported
Pick a job, see it optimized
in 10 minutes, free
→
SOC 2 Type II
Compliant
Enterprise-grade security and data protection
Full onboarding of all production jobs in minutes
See how we optimize and debug any Spark job in minutes instead of hours
Trusted by industry Leaders
"DataFlint has been a game changer in Spark observability for Intel Granulate and I`m glad to see it`s the case for Amazon Web Services (AWS) as well"
“That`s great news! This is such a great replacement for the Spark UI. Seamless to setup and packed with data that actually makes sense.”
"great job Meni Shmueli and Daniel A. ! Proactively monitoring spark metrics to derive actionable insights is super important and often overlooked "
“Great news! When you have a hunch about the performance of your spark job, it is great that DataFlint backs your hunch with all the metrics and alerts. Much more easier to pinpoint room for inprovements with DataFlint now!”
"If you`re managing Spark clusters — whether on-prem, in Kubernetes, or in the cloud — DataFlint makes it significantly easier to monitor, troubleshoot, and optimize workloads. Lightweight, open-source, and productivity-focused."
“Will start experimenting with the new version ASAP. From my past experience the ability to view realtime and post execution is so much better than regular spark UI it`s comfortable and faster with great insights ”
"I was using Dataflint a lot in the last few weeks for the optimization of aggregate tokens job. Combining my ideas for optimization and Dataflint suggestions the time went from 1:50 to 1:30 and the cost of the job went from 260 dollars daily to 110. If the costs continue like this then yearly savings is around 55000 dollars!"
“Super helpful for our DE team 💪🏻”
"This is how I see Apache Spark debugging finally becoming democratized! Harness the power of experts at your fingertips interacting with your code! Well done DataFlint - I hope this takes off and becomes the defacto approach in the industry. 🚀"
"DataFlint is a must-have if you are running Apache Spark!"
"Solving the "even with the fix, users struggle to implement it" problem by bringing the DataFlint Copilot right into the IDE is a massive win for big data practitioners. Tackling Spark's notorious debugging and optimization challenges right where developers work, and achieving those incredible 100X results, is a game-changer."
"DataFlint is really a game changer to me. When we are working on Lakehouse project with Apache Spark, it had been a pain to debug, but DataFlint has improved our experience with it. Super amazed with it!"
"Amazing product!—we`re using it widely at Wix"
"Yoooo, big fan of a DataFlint for almost a year! Actually, in the same talk on Apache Spark I was glad to introduce DataFlint. Precisely, I mentioned detailed job explanations, alerts and integrations (Comet, Iceberg, History Server). So huge respect here for making Spark UI more user-friendly and helpful."
“We`re deploying the new version...”
Latest insights and reading from our clients
Read more articles
Product FAQ’s
General-purpose AI tools like ChatGPT, Claude, Gemini, and agentic coding assistants like Cursor and Copilot write Spark code in isolation. They have zero visibility into your actual cluster, data distributions, or runtime behaviour. DataFlint's AI agents are production-aware: they write, review, optimize, monitor, and fix Spark jobs end-to-end on your infrastructure.
- Production-aware intelligence Every suggestion is informed by your live DAG, performance logs, and cost metrics, not just generic Spark knowledge. Agentic tools generate code from documentation; DataFlint generates code from your production reality.
- Continuous optimization loop Generic AI agents give you a one-shot answer and move on. DataFlint stays attached after deployment, learning from runtime performance and automatically surfacing new optimizations as data volumes grow and patterns shift.
- Full observability built in Streams real-time Spark metrics and costs into a single dashboard, flagging slowdowns and anomalies the moment they appear, something no chat-based or agentic AI can do.
- Works alongside your favourite tools DataFlint plugs into VS Code, Cursor, and IntelliJ via its Spark MCP server, so you keep using the AI coding agent you prefer while DataFlint adds the production context it lacks.
Use DataFlint when you need Spark code that's production-ready and cost-efficient, not just syntactically correct.
DataFlint deploys specialized AI agents across your Spark workflow:
- The Agentic Spark Copilot lives in your IDE (Cursor, VS Code, IntelliJ). It root-causes failures, generates the fix, and ships code-level optimizations from chat with full production context.
- The Cluster Agent right-sizes resources in real time, cutting infrastructure costs by up to 50%.
- The Review Agent catches performance regressions in pull requests using real production context, before they hit production.
Under the hood, DataFlint's engine analyzes Spark logs from platforms like EMR, Databricks, and Kubernetes, enriches them, and uses its Spark MCP server to power each agent with real production data.
DataFlint offers several key benefits:
- Faster Issue Resolution: Instantly performs root cause analysis for failing Spark pipelines.
- Optimized Performance: Provides code suggestions to optimize join strategies, resource allocation, and more, leading to significantly faster execution times (e.g., 90X faster and 160X cheaper in our SimilarWeb case study).
- Reduced Costs: Helps cut infrastructure costs dramatically by identifying inefficiencies (e.g., 100X cost reduction in the SimilarWeb pipeline optimization case study).
- Increased Team Velocity: Empowers your data team to ship data pipelines faster and more reliably. In the SimilarWeb case study, a single job achieved 100X cost reduction and 13X faster execution.
- Enhanced Observability: Offers a control center for immediate visibility into failing jobs, performance bottlenecks, and cost metrics.
DataFlint is designed for broad compatibility. It integrates with:
- Spark Platforms: AWS EMR, Databricks, Google Dataproc, Microsoft Fabric, Kubernetes, and on-premises clusters.
- Storage: S3, Azure Blob Storage, Hadoop HDFS, Google object storage.
- Orchestration: Airflow, Databricks Jobs.
- IDEs & Tools: VS Code, Cursor, IntelliJ for code suggestions via the Spark MCP server.
- Observability: DataFlint provides a SaaS UI dashboard and integrates with Slack and Managed Spark History Server.
DataFlint prioritizes your data security and privacy. We monitor and analyze Spark logs, which are performance logs detailing job execution metrics and system events, not your underlying business data. This focus on operational metadata means there are minimal privacy concerns related to sensitive information. Furthermore, DataFlint is AICPA SOC 2 Type II compliant, demonstrating our commitment to robust security controls and practices.
Yes, DataFlint is enterprise-ready from day one. Full onboarding of all production jobs takes minutes, not weeks. It is AICPA SOC 2 Type II compliant with enterprise-grade security and data protection. DataFlint works across AWS EMR, Databricks, Google Dataproc, Microsoft Fabric, Kubernetes, and on-premises deployments, handling complex, large-scale Spark environments with ease.
