Agentic Platform that Autotunes Apache Spark for Performance and Cost
Data engineers get the root cause and the fix. Platform teams get reliable pipelines. FinOps sees all the spend, and how to cut it. Agents tune the job, the cluster and the storage, and every change stays under your control.
Spark is complex.
Hard to debug,
expensive to run.
From one slow run to your whole Spark bill
Most AI coding tools are blind to production. DataFlint enriches your Spark logs and serves them to your agents through a Spark MCP server, so they ship fixes that actually cut runtime and cost.
10-100X
faster Spark jobs
50-90%
infrastructure cost cut
One platform, served to every agent.
Ask Cursor, Claude Code or your own agent why a Spark job is slow or expensive, and it answers from your production runs, not from guesswork.
Root cause and the fix, down to the line.
DataFlint reads every finished run and names the slow or failing stage, the cause (skew, spill, out of memory, small files) and the line of code to change. The fix lands in Cursor, VS Code or Claude Code.
Execution time (Min.)
9XFaster
A job that tripled in run time after a move from Databricks to EMR
One platform, served to every agent.
Ask Cursor, Claude Code or your own agent why a Spark job is slow or expensive, and it answers from your production runs, not from guesswork.
Root cause and the fix, down to the line.
DataFlint reads every finished run and names the slow or failing stage, the cause (skew, spill, out of memory, small files) and the line of code to change. The fix lands in Cursor, VS Code or Claude Code.
Execution time (Min.)
9XFaster
A job that tripled in run time after a move from Databricks to EMR
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
See DataFlint on your
own Spark jobs
→
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
Latest insights and reading from our clients
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.
