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

Optimized 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 Sagemaker

Platform Comparison - When TrueFoundry Makes Sense?

#1

Platform Foundation

Amazon SageMaker is a fully managed machine learning service that offers a comprehensive range of functionalities, from data preparation and model training to deployment and ML governance, all within the AWS ecosystem. In contrast, TrueFoundry’s underlying architecture leverages Kubernetes and specializes in specific areas such as ML and LLM deployment, training/fine-tuning, and infrastructure optimization.

#2

⁠Cross cloud + On prem

While SageMaker's performance, security, and scalability are tightly integrated with AWS infrastructure, leading to potential cloud lock-in, TrueFoundry offers greater flexibility by operating across different cloud providers and even on-premises environments.

#3

Cost Optimization

TrueFoundry enables savings of more than 40% on total costs compared to running identical workloads on Sagemaker. SageMaker puts a markup of 25-40% on instances that are provisioned using SageMaker whereas TrueFoundry helps teams make use of raw Kubernetes through EKS.

#4

Flexibility

TrueFoundry imposes no restrictions on code style or the libraries used for deployment, offering complete flexibility for data scientists to use their preferred frameworks like FastAPI, Flask, Pytorch Lightning, and Streamlit. It seamlessly integrates with state-of-the-art tools throughout the ML/LLMOps lifecycle.

General Overview

Type of platform
Managed Platform
Part of AWS cloud ecosystem
Setup on own infra
Runs on top of Kubernetes. Self hostable data plane and control plane in your own VPC or on prem
Not available on on-prem infrastructure or any other cloud than AWS
No Lock In and Interoperability
No lockin and high extensibility.The entire platform is API driven, adding any component is trivial for a user
High vendor lockin. It is difficult to move to another cloud provider/MLOps tool because of existence of platform specific codae that is inserted into the models
SLAs + Support
24x7 slack support with on call assistance for urgent tickets

Premium support with a dedicated account manager .We boast a 9.9/10 for customer support on G2.
General guidance tickets with SLA of 1 day and Production system down tickets within 1 hour Enterprise support plan
Enterprise Grade
Security & Compliance
Yes. HIPPA and SOC2 compliant. Read our security whitepaper here
AWS compliance program
User access management
Permission control at the cluster, workspace, or deployment level with an intuitive user interface.
AWS IAM for sagemaker roles, notebooks and APIs
Pricing model
user- based modular pricing with access to all platform capabilities
Pricing depends on usage. You can get started for free with AWS free tier. Costs can be estimated using the AWS Pricing Calculator.
Cost optimization
~40% cost savings (benchmarked to Sagemaker )via using bare kubernetes, spot instances, infra and model optimizations, autoscaling & fractional GPUs
Sagemaker Savings Plan in excahage of usage commitment for 1-3 years

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
Limited to AWS
CI/CD support
Integration with your CI/CD pipeline and existing infrastructure along with complete change logs, IaaC and rollbacks.
Can be implemented by using sagemkaer pipelines
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
Limited
No constraint of libraries
No code style or library restrictions, providing complete flexibility to use preferred frameworks like FastAPI, Flask, PyTorch Lightning, Streamlit
Restrictions hamper code portability
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 via Sagemaker pipelines

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.
Amazon Bedrock provides a bunch of foundational models
Model infrastructure optimization
Yes. Pre configured GPU options for different model servers such as VLLM
Not sure
Hugging Face model deployment
Yes. CPU usage, Request per second and time based autoscaling
Yes
LLM performance benchmarking
Yes
No
Memory management and latency optimization
Yes
Not sure
AI templates
No. We give you the flexibility to stitch together models, dbs (including vector databases), services etc to create your own workflows
Yes. Sagemaker Jumpstart

How to Evaluate?

Infra configurations and optimizations,Hugging face deployment, Cost optimization

LLM Finetune

Finetune foundational models
Yes
Connect to your own data source
Point to your own data in S3, Snowflake, Databricks, etc
Native integration to S3 buckets
Compare finetuning runs
Yes
Yes
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
Yes-Custom code required using HuggingFace PeFT package
Run finetuning workflow as a job
Used for long-running training with automatic retries
Yes
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
Yes
Cost Attribution and control
Yes
Yes
Prompt Engineering
Yes
Limited
Fallback, retries and rate-limiting support
In the roadmap
No
Guardrails Integration
In the roadmap. Also, Integrates with guardrails platforms currently
Limited
Caching and Semantic Caching
In the roadmap
Yes
Support for Vision and Multimodal models
In the roadmap
Yes
Run Evaluations on your data
In the roadmap
Limited

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.
Needs stitching together of various AWS components
Vector database
Yes. Chroma, Qdrant and Weaviate support
Utilizes Amazon OpenSearch as a vector database. However, it has native integrations to Pinecone ( a managed VectorDB)
Embedding models
Yes
Yes

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
Can run notebooks on GPUs
Data preparation
Yes. Multiple data connectors. Shared volumes across notebooks can also be used
Seamless integration to Amazon EMR clusters and AWS Glue
Customizable base images
Yes
Yes.Supports custom docker images
Auto culling and saving
Yes. Auto shutdown with certain minutes of inactivity
No
AI powered tools
No
Integrated with Amazon Code Whisoerer

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
Resilient spot training
Yes
Yes
Metrics and Logging
Thorough tracking of custom metrics, dashboards, checkpointing support etc. along with system metrics and logs
Yes
Pipeline / DAG orchestration
In roadmap
Yes

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

Model Deployment + 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 model servers such as Torch serve and Tritron Inference server
Rollout strategies
Various rollout strategies such as canary, blue green, rolling update
Yes
Header based routing and Traffic Shaping
Yes
Custom work
Async Deployments
Yes
Yes
Cost estimation of service
Yes
No
Cascading / Ensemble Models
Yes
Custom work
Model caching
Yes
Custom work
Microbatching
In the roadmap
Yes
Serverless Deployment
In the roadmap
Yes
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. With Sagemaker Experiments
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
Yes
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.
Yes
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
General OverviewCore FeaturesLLM ModulesML Modules
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*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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