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Why Build, train & deploy production grade AI/ML workflows

Low Cost
Run cross cloud with pre-configured resource optimisations at lowest cost
Secure Data
Connect with you data warehouses or lakes securely without data leaving your cloud
Developer-friendly
Doubles developer productivity, intuitive interface &  API driven for easy integrations
Enterprise Ready
CI/CD, RBAC, SSO integrations built in on a SOC2, HIPPA compliant platform

Compare TrueFoundry vs Domino Data Lab

Includes all platform-level mostly infrastructure focused features baked into the platform

General Overview

Type of platform
Managed Platform
Managed Platform
Setup on own infra
Runs on top of Kubernetes. Self hostable data plane and control plane in your own VPC or on prem
Runs on top of Kubernetes. Self hostable data plane and control plane in your own VPC or on prem
No Lock In and Interoperability
No lockin and high extensibility.The entire platform is API driven, adding any component is trivial for a user
Not sure. They have a data plane + control plane structure
SLAs
24x7 slack support with on call assistance for urgent tickets
1 hour enterprise support SLA for urgent tickets
Support
Premium support with a dedicated account manager .We boast a 9.9/10 for customer support on G2.
Limited plan with no support for product customization and custom code development
Security and compliance
HIPPA and SOC2 compliant
HIPPA and SOC2 compliant
User access management
Permission control at the cluster, workspace, or deployment level with an intuitive user interface.
Permission control on project and dataset level
Pricing model
user- based modular pricing with access to all platform capabilities
user-based pricing
Cost optimization
~40% cost savings as compared to Sagemaker via using bare kubernetes, spot instances, infra and model optimizations, autoscaling & fractional GPUs
Analyze reports and setup alerts on a project level

Core Platform Features

Includes all platform-level mostly infrastraucture focused features baked into the platform

Core Features

Core Platform

Hybrid and multi cloud support
Yes
Yes
CI/CD support
Integration with your CI/CD pipeline and existing infrastructure along with complete change logs, IaaC and rollbacks.
Can integrate with existing CI/CD workflows and registries outside of Domino
Autoscaling
Yes. CPU usage, Request per second and time based autoscaling
Yes
Fractional GPUs support
Yes
No
Spot instance layer with built in reliability
Yes
In preview
No constraint of libraries
No code style or library restrictions, providing complete flexibility to use preferred frameworks like FastAPI, Flask, PyTorch Lightning, Streamlit
Limited restictions
Management of dev / staging / prod lifecycle
First class support with unified access management, integration with GitOps tools and one click promotion flow without any code changes
Can be done by creating a production only 'organization'

How to Evaluate?

Deploy on any cloud/on prem with low effort, high performance, SRE best practices and cost optimized way

LLM Essentials

Covers all the features essential to build & scale LLM applications using popular workflows such as prompt engineering, deploying & fine-tuning LLMs, and setting up RAG workflows

LLM Modules

LLM Deploy

Model catalogue
Yes. A curated model catalogue of all popular LLMs with pre-configured settings and top-performing model servers.
No
Model infrastructure optimization
Yes. Pre configured GPU options for different model servers such as VLLM
No
Hugging Face model deployment
Yes. CPU usage, Request per second and time based autoscaling
No
LLM performance benchmarking
Yes
No
Memory management and latency optimization
Yes
No
AI templates
No. We give you the flexibility to stitch together models, dbs (including vector databases), services etc to create your own workflows

How to Evaluate?

Infra configurations and optimizations,Hugging face deployment, Cost optimization

LLM Finetune

Finetune foundational models
Yes
Basic Finetuning flow is in preview mode
Connect to your own data source
Point to your own data in S3, Snowflake, Databricks, etc
No
Compare finetuning runs
Yes
No
Deploy finetuned model
Yes
Yes
Finetune on spot instances
Yes
No
Pre configured resource optimization
Yes
No
PEFT finetuning
Yes - Supports both LoRA and QLoRA in a few clicks. Abstracts away all the details behing the hood
No
Run finetuning workflow as a job
Used for long-running training with automatic retries
No
Run finetuning workflow in a notebook
Used for short, iterative trainings and experiments
Yes

How to Evaluate?

Abstract infra complexity for each model, GPU, model server and PEFT combination, Cost optimizations, Training best practices such as checkpointing etc

AI Gateway

Unified API
Access all LLMs from multiple providers including your own self hosted models.
Yes
Centralized Key Management
Yes
Yes
Authentication and attribution per user, per product.
Yes
No
Cost Attribution and control
Yes
No
Prompt Engineering
Yes
No
Fallback, retries and rate-limiting support
In the roadmap
No
Guardrails Integration
In the roadmap. Also, Integrates with guardrails platforms currently
No
Caching and Semantic Caching
In the roadmap
No
Support for Vision and Multimodal models
In the roadmap
No
Run Evaluations on your data
In the roadmap
No

How to Evaluate?

Multiple LLM integration, Prompt engineering support, Access and cost management, Evaluation and Guardrails implementation

RAG Template

End to end RAG system setup
All the components of the RAG workflow are spun up automatically including embedding model, Vector DB, frontend and backend systems.
Very basic template
Vector database
Yes. Chroma, Qdrant and Weaviate support
Pinecone and Qdrant support
Embedding models
Yes
No

How to Evaluate?

Ease of setting up and stitching all RAG components, Support for diverse options for each component for experimentation

ML Modules

Covers all the features that are required to build, train and deploy ML models in production

ML Modules

Hosted Notebooks

Compute for hosted notebooks
Yes. GUPs included
Yes. GPUs included
Data preparation
Yes. Multiple data connectors. Shared volumes across notebooks can also be used
Multiple data source connectors such as redshift, snowflake
Customizable base images
Yes
Yes
Auto culling and saving
Yes. Auto shutdown with certain minutes of inactivity
No
AI powered tools
No
Yes

How to Evaluate?

Access to compute & custom images. Cost features such as auto culling and volume loading across notebooks

Model training and batch inferencing

Distributed training
Support for distributed and multi node training
Yes. Integration with Spark and Ray
Resilient spot training
Yes
In preview
Metrics and Logging
Thorough tracking of custom metrics, dashboards, checkpointing support etc. along with system metrics and logs
Yes
Pipeline / DAG orchestration
In roadmap
Supports integration with Apache Airflow

How to Evaluate?

For model training features such as artifact management, metric tracking and CI / CD / CT are imperative. On the compute side, distributed and multinode training becomes critical

Real time inferencing

CI/CD
Support scalable API deployment without much interference with code with CI/CD and rollbacks
Yes
Integration with Model serving frameworks
Out of the box integration with vLLM, TGI etc. working on other integrations like TMS
Integrates with Ray serve
Rollout strategies
Various rollout strategies such as canary, blue green, rolling update
No
Header based routing and Traffic Shaping
Yes
No
Async Deployments
Yes
Yes
Cost estimation of service
Yes
Yes
Cascading / Ensemble Models
Yes
No
Model caching
Yes
No
Microbatching
In the roadmap
No
Serverless Deployment
In the roadmap
No
Monitoring
Automated monitoring dashboard for deployed services and provides integration with all popular monitoring tools
Yes

How to Evaluate?

API deployment ease, Versioning and Gitops , Infra management, First class support for servers, Extensibility and integrations

Model tracking

Experiment Tracking
Yes
Yes. Uses MLfow for experiment tracking
Model Registry
Full fledged artifact management with versioning, loading, serialization support. Supports logging and versioning artifacts and metadata
Yes
One-click deployment from model registry
Yes. Have a full fledged model registry and allow direct deployments
Easy deployment from model registry to production
Integrations with tools such as wandb & mlflow
Yes
Yes
Model Versioning
Yes
Yes
Model Lineage Tracking
Yes
Yes

How to Evaluate?

Comprehensive Model registry with seamless model deployment, Version tracking and reverting along with metadata tracking, Integrations

Monitoring

System Monitoring
Yes. CPU, Memory, Network, Disk Usage etc.
No
Service Metrics
Yes. Request volume, latency, success & error rate etc.
Yes
Model Metrics
Yes. Accuracy, Precision, Recall or any other custom metrics depending on the model type
Yes
Drift Tracking
Yes. Model, data and target drift tracking for structured data
Yes
Integrations with tools such as wandb & mlflow
Supports integration with any existing dashboarding and alerting tool being used
Yes
Data Distributions
Custom developed based on client requisition
Yes
Automated Alerts
Custom developed based on client requisition
Yes
Custom monitoring metrics
Custom developed based on client requisition
Yes

How to Evaluate?

Automated and custom logging and alerts, Model + System metrics,Dashboarding, Coverage of supported libraries and frameworks
Sell all features

*Competitive data on this page was collected as of April 1, 2024 and is subject to change or update. TrueFoundry does not make any representations as to the completeness or accuracy of the information on this page. All TrueFoundry services listed in the features comparison chart are provided by TrueFoundry or by one of TrueFoundry’s trusted partners.

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