Build your LLM App in 30 lines of code, no vector database requiredAnnouncing: Pathway is the fastest data processing engine on the market - 2023 benchmarks Make your
Build your LLM App in 30 lines of code, no vector database required 
Monitor your logs in real-timeGet actionable insights on top of data for logistics assets 
Gartner's Representative Vendor
Make yourAI applicationdata pipelinelog monitoringAI applicationrun in real-time
AI applicationdata pipelinelog monitoringAI application
pathway.run()Check it out
index = KNNIndex(enriched_documents, d=embedding_dimension)
query_context = index.query(query, k=3).select(
pw.this.query, documents_list=pw.this.result
)
prompt = query_context.select(
prompt=build_prompt(pw.this.documents_list, pw.this.query)
)
model = OpenAIChatGPTModel(api_key=api_key)
responses = prompt.select(
query_id=pw.this.id,
result=model.apply(
pw.this.prompt,
locator=model_locator,
temperature=temperature,
max_tokens=max_tokens,
),
)
The fastest data processing engine supporting unified workflows for batch, streaming data, and LLM applications
What do people typically use Pathway for?
- I want to fix my latency
quickly.I need latency to be milliseconds or seconds, but my batch job is taking minutes or hours to run each time. - I want to reduce my compute bill quickly.My cloud bill is too high because of Spark and other jobs taking lots of compute resources.
- I want real-time to work from day one.I am launching a new data project and I want it to integrate with real-time data sources easily.
How others use it
Build your LLM App in 30 lines of code, no vector database requiredMonitor your logs in real-timeTrusted by
Gartner's Representative VendorMarket Guide for Data Analytics and Intelligence Platforms in Supply Chain
Why adopt Pathway?
It has soft fur.
Pathway is used by data scientists and engineers alike. You can program in Python or SQL.
Launch your data project quickly with static data sources. Then, connect live data (Kafka, S3/min.io, PostgreSQL,...) and run your code in Pathway's streaming mode.
You can call HTTP API's and integrate with your existing ML models or build your entire LLM stack using Pathway.
It has sharp teeth.
Pathway is powered by a distributed pure-Rust streaming engine. It achieves high throughput with astonishingly low latency.
Pathway works with event stream data and time series, data tables, graph data, and external blobs (video, text).
Pathway supports backfilling with a mix of streaming and static data sources. It allows for late data points and corrections to data.
It is deployed across enterprise.
Pathway is currently used for real-time anomaly detection, predictive analytics, IoT and logs data observability, recommender systems, and alerting.
You can also use Pathway simply to reduce cloud costs, when frequent batch recomputation is eating away at your data budgets.
The data processing layer of Pathway is free and source available.
Use Pathway to create value with your data platform
Live data
Data tables
Live events data
Live transaction records
Live sales data
IoT data
Logistics & moving asset data
Supply chain plans
User inputs
and more!
Value Delivered in Real Time
AI-powered insights from LLM
Anomalies detected
Alerts
Actionable insights
Data harmonization and enrichment
Recommendations
Forecasts
Interactive scenario simulations
and more!
Your data product built with Pathway
It's time to add Pathway to your workflow
Transform your data in motion
using Pathway on top of a Kafka real-time data pipeline
Enrich your data inside ElasticSearch
to unlock Pathway-powered analytics for your ELK stack for free