Discover High-Performance AI Models.
Maximize ROI.
Maximize ROI.
Test AI models from external vendors directly on your data — without ever exposing it. Identify top performing models and boost ROI across your use cases.
trusted by
Easily test which vendor models perform best —
on your data, for your use cases
Easily test
which vendor models perform best — on your data, for your use cases
Easily test which vendor models perform best —
on your data, for your use cases
Easily test
which vendor models perform best — on your data, for your use cases
Discover the best-performing AI models — maximize ROI
Discover the best-performing AI models —
maximize ROI
Discover the best-performing AI models — maximize ROI
Discover the best-performing AI models —
maximize ROI
Keep
your data and IP.
private and protected — stay in full control
Keep
private and protected — stay in full control
your data and IP.
private and protected — stay in full control
Keep
your data and IP.
private and protected — stay in full control
Keep
private and protected — stay in full control
your data and IP.
private and protected — stay in full control
Core Capabilities of the tracebloc Platform
Everything your team needs to allow vendors to finetune and benchmark their models, protect your data, and discover the best models.
Data Control
Pre-Built Data Pipelines
for Seamless Ingestion
Ingesting data is fast and hassle-free. We provide pre-configured pipelines for common use cases, enabling secure and efficient data transfer into the Kubernetes cluster.
Data Control
Pre-Built Data Pipelines
for Seamless Ingestion
Ingesting data is fast and hassle-free. We provide pre-configured pipelines for common use cases, enabling secure and efficient data transfer into the Kubernetes cluster.
Benchmarking
Use-Case–Driven
Evaluation Metrics
Benchmark models against the KPIs that matter most — from accuracy, F1 score, latency, and robustness to energy consumption and carbon footprint. All metrics are fully configurable and tailored to your specific use case.
Benchmarking
Use-Case–Driven
Evaluation Metrics
Benchmark models against the KPIs that matter most — from accuracy, F1 score, latency, and robustness to energy consumption and carbon footprint. All metrics are fully configurable and tailored to your specific use case.
Fine-Tuning & Training
Fully Private Execution
— On-Prem or Cloud
All training, fine-tuning, and inference operations take place entirely within your infrastructure. Your sensitive data and fine-tuned weights never leave your controlled environment.
Fine-Tuning & Training
Fully Private Execution
— On-Prem or Cloud
All training, fine-tuning, and inference operations take place entirely within your infrastructure. Your sensitive data and fine-tuned weights never leave your controlled environment.
Vendor Onboarding
Enterprise-Grade Logging
and Audit Compliance
All activity — from model access to system-level operations — is fully logged and structured for compliance with GDPR, ISO 27001, and the upcoming EU AI Act.
Vendor Onboarding
Enterprise-Grade Logging
and Audit Compliance
All activity — from model access to system-level operations — is fully logged and structured for compliance with GDPR, ISO 27001, and the upcoming EU AI Act.
Fine-Tuning & Training
Flexible Model Tuning
Capabilities
Supports full-model fine-tuning, parameter-efficient tuning (e.g., LoRA, adapters), ensemble modeling, hyperparameter optimization, and custom feature-engineering pipelines and more.
Fine-Tuning & Training
Flexible Model Tuning
Capabilities
Supports full-model fine-tuning, parameter-efficient tuning (e.g., LoRA, adapters), ensemble modeling, hyperparameter optimization, and custom feature-engineering pipelines and more.
Benchmarking
Small Language Model
(SLM) Benchmarking Support
Evaluate small and domain-specific language models on real enterprise tasks — such as classification, summarization, retrieval, or document parsing — optimized for real-world constraints.
Benchmarking
Small Language Model
(SLM) Benchmarking Support
Evaluate small and domain-specific language models on real enterprise tasks — such as classification, summarization, retrieval, or document parsing — optimized for real-world constraints.
Vendor Onboarding
Use Case-Based Vendor
Whitelisting
Ingesting data is fast and hassle-free. We provide pre-configured pipelines for common use cases, enabling secure and efficient data transfer into the Kubernetes cluster.
Vendor Onboarding
Use Case-Based Vendor
Whitelisting
Ingesting data is fast and hassle-free. We provide pre-configured pipelines for common use cases, enabling secure and efficient data transfer into the Kubernetes cluster.
Fine-Tuning & Training
Multi-Framework Model Compatibility
Out-of-the-box support for PyTorch, TensorFlow, and custom containers — enabling vendors to use their preferred tooling without codebase changes.
Fine-Tuning & Training
Multi-Framework Model Compatibility
Out-of-the-box support for PyTorch, TensorFlow, and custom containers — enabling vendors to use their preferred tooling without codebase changes.
Data Control
Isolated Model Execution
with No Data Exposure
Vendor models are allowed to access proprietary data for training and inference — but all fine-tuned weights remain strictly within your infrastructure. No model artifacts leave your environment.
Data Control
Isolated Model Execution
with No Data Exposure
Vendor models are allowed to access proprietary data for training and inference — but all fine-tuned weights remain strictly within your infrastructure. No model artifacts leave your environment.
Fine-Tuning & Training
Hardware-Agnostic
Execution Layer
Execute jobs across CPUs, GPUs, and TPUs using Kubernetes-native scheduling. Optimize for speed, cost-efficiency, and hardware availability.
Fine-Tuning & Training
Hardware-Agnostic
Execution Layer
Execute jobs across CPUs, GPUs, and TPUs using Kubernetes-native scheduling. Optimize for speed, cost-efficiency, and hardware availability.
Vendor Onboarding
Individual Compute
Budget Allocation
Assign compute budgets to individual vendors or users, giving you full control over resource consumption and usage limits.
Vendor Onboarding
Individual Compute
Budget Allocation
Assign compute budgets to individual vendors or users, giving you full control over resource consumption and usage limits.
Vendor Onboarding
Support for Proprietary Vendor Models
Vendors can bring their own models using frameworks like TensorFlow or PyTorch. Fine-tuning and advanced techniques are fully supported.
Vendor Onboarding
Support for Proprietary Vendor Models
Vendors can bring their own models using frameworks like TensorFlow or PyTorch. Fine-tuning and advanced techniques are fully supported.
Benchmarking
Compatibility & Policy Checks
Automatically verify that submitted models meet enterprise requirements — such as use of approved packages, compatibility with existing runtimes, presence or absence of pretrained weights, and other technical constraints.
Benchmarking
Compatibility & Policy Checks
Automatically verify that submitted models meet enterprise requirements — such as use of approved packages, compatibility with existing runtimes, presence or absence of pretrained weights, and other technical constraints.
Fine-Tuning & Training
Federated Learning Across
Distributed Environments
Enable collaborative training across sites or regions. Data remains local while models are trained securely in a decentralized setup.
Fine-Tuning & Training
Federated Learning Across
Distributed Environments
Enable collaborative training across sites or regions. Data remains local while models are trained securely in a decentralized setup.
Vendor Onboarding
Emergency Halt for All Vendor Activity
Instantly pause all training and inference jobs across vendors with a single action—ideal for handling security events or urgent reviews.
Vendor Onboarding
Emergency Halt for All Vendor Activity
Instantly pause all training and inference jobs across vendors with a single action—ideal for handling security events or urgent reviews.
Fine-Tuning & Training
Enforced Compute Budgets per Vendor
Define and automatically enforce compute budgets using FLOPs or runtime quotas — giving you tight control over usage and cost exposure.
Fine-Tuning & Training
Enforced Compute Budgets per Vendor
Define and automatically enforce compute budgets using FLOPs or runtime quotas — giving you tight control over usage and cost exposure.
Benchmarking
Custom Inference
Quotas per Vendor
Define how much of the test dataset each vendor can use for inference — enabling fair, resource-aware evaluations with clear usage boundaries.
Benchmarking
Custom Inference
Quotas per Vendor
Define how much of the test dataset each vendor can use for inference — enabling fair, resource-aware evaluations with clear usage boundaries.
Data Control
End-to-End Infrastructure
Security
Encrypted communication channels, fine-grained access controls, and support for sovereign cloud and air-gapped environments ensure security across every layer.
Data Control
End-to-End Infrastructure
Security
Encrypted communication channels, fine-grained access controls, and support for sovereign cloud and air-gapped environments ensure security across every layer.
Vendor Onboarding
Multi-Tenant, Multi–Use
Case Isolation
Support parallel projects across different teams, locations, and use cases—while maintaining strict isolation at the runtime and data level.
Vendor Onboarding
Multi-Tenant, Multi–Use
Case Isolation
Support parallel projects across different teams, locations, and use cases—while maintaining strict isolation at the runtime and data level.
Fine-Tuning & Training
Isolated Kubernetes-
Native Sandboxing
Each model runs in its own containerized environment with full namespace isolation, managed by Kubernetes for autoscaling, fault tolerance, and high availability.
Fine-Tuning & Training
Isolated Kubernetes-
Native Sandboxing
Each model runs in its own containerized environment with full namespace isolation, managed by Kubernetes for autoscaling, fault tolerance, and high availability.
Vendor Onboarding
Massively Parallel
Model Execution
Run hundreds of training and fine-tuning jobs simultaneously. Our autoscaling, Kubernetes-native infrastructure is optimized for high throughput.
Vendor Onboarding
Massively Parallel
Model Execution
Run hundreds of training and fine-tuning jobs simultaneously. Our autoscaling, Kubernetes-native infrastructure is optimized for high throughput.
Data Control
Flexible Deployment
Across Any Infrastructure
Deploy tracebloc on bare metal, private cloud, public cloud, or in hybrid environments — with Kubernetes-native orchestration, autoscaling, and high-availability built-in.
Data Control
Flexible Deployment
Across Any Infrastructure
Deploy tracebloc on bare metal, private cloud, public cloud, or in hybrid environments — with Kubernetes-native orchestration, autoscaling, and high-availability built-in.
Benchmarking
Leaderboard-Based
Vendor Model Comparison
Compare all vendor submissions via a shared leaderboard. Models are evaluated under identical conditions — same data, compute, and metrics — for transparent, apples-to-apples comparison and objective selection.
Benchmarking
Leaderboard-Based
Vendor Model Comparison
Compare all vendor submissions via a shared leaderboard. Models are evaluated under identical conditions — same data, compute, and metrics — for transparent, apples-to-apples comparison and objective selection.
Benchmarking
Automated Inference
Pipelines
Fully automated pipelines streamline the entire benchmarking workflow — from model loading and data preprocessing to inference execution and metric logging.
Benchmarking
Automated Inference
Pipelines
Fully automated pipelines streamline the entire benchmarking workflow — from model loading and data preprocessing to inference execution and metric logging.
Data Control
Encrypted Models & Weights
to Protect Vendor IP
All model binaries and weight files are encrypted in transit and at rest, ensuring that no vendor intellectual property is exposed or leaked during execution.
Data Control
Encrypted Models & Weights
to Protect Vendor IP
All model binaries and weight files are encrypted in transit and at rest, ensuring that no vendor intellectual property is exposed or leaked during execution.
Data Control
Compliance-Ready
Logging & Traceability
All activity — from model access to system-level operations — is fully logged and structured for compliance with GDPR, ISO 27001, and the upcoming EU AI Act.
Data Control
Compliance-Ready
Logging & Traceability
All activity — from model access to system-level operations — is fully logged and structured for compliance with GDPR, ISO 27001, and the upcoming EU AI Act.
Benchmarking
Efficiency Benchmarking
Assess models not just on quality, but also on performance efficiency — including runtime, memory usage, and compute cost — to ensure scalability and production-readiness.
Benchmarking
Efficiency Benchmarking
Assess models not just on quality, but also on performance efficiency — including runtime, memory usage, and compute cost — to ensure scalability and production-readiness.
Fine-Tuning & Training
Accelerator-Optimized Large Model Training
Built for speed and scale on modern accelerators (GPUs, TPUs). Integrated with DeepSpeed to maximize hardware utilization through memory optimization, parallelism strategies, and ZeRO-based sharding — enabling efficient training of large models across distributed nodes.
Fine-Tuning & Training
Accelerator-Optimized Large Model Training
Built for speed and scale on modern accelerators (GPUs, TPUs). Integrated with DeepSpeed to maximize hardware utilization through memory optimization, parallelism strategies, and ZeRO-based sharding — enabling efficient training of large models across distributed nodes.
Data Control
Pre-Built Data Pipelines
for Seamless Ingestion
Ingesting data is fast and hassle-free. We provide pre-configured pipelines for common use cases, enabling secure and efficient data transfer into the Kubernetes cluster.
Data Control
Pre-Built Data Pipelines
for Seamless Ingestion
Ingesting data is fast and hassle-free. We provide pre-configured pipelines for common use cases, enabling secure and efficient data transfer into the Kubernetes cluster.
Benchmarking
Use-Case–Driven
Evaluation Metrics
Benchmark models against the KPIs that matter most — from accuracy, F1 score, latency, and robustness to energy consumption and carbon footprint. All metrics are fully configurable and tailored to your specific use case.
Benchmarking
Use-Case–Driven
Evaluation Metrics
Benchmark models against the KPIs that matter most — from accuracy, F1 score, latency, and robustness to energy consumption and carbon footprint. All metrics are fully configurable and tailored to your specific use case.
Fine-Tuning & Training
Fully Private Execution
— On-Prem or Cloud
All training, fine-tuning, and inference operations take place entirely within your infrastructure. Your sensitive data and fine-tuned weights never leave your controlled environment.
Fine-Tuning & Training
Fully Private Execution
— On-Prem or Cloud
All training, fine-tuning, and inference operations take place entirely within your infrastructure. Your sensitive data and fine-tuned weights never leave your controlled environment.
Vendor Onboarding
Individual Compute
Budget Allocation
Assign compute budgets to individual vendors or users, giving you full control over resource consumption and usage limits.
Vendor Onboarding
Individual Compute
Budget Allocation
Assign compute budgets to individual vendors or users, giving you full control over resource consumption and usage limits.
How to setup your first AI use case
Set up your first use case, onboard vendors, and evaluate their models securely.
01.
Set up secure environment
Deploy an isolated Kubernetes cluster in your cloud (VPC) or on bare metal. Pull the tracebloc client from Docker Hub and connect it to our backend.
02.
Ingest training and test data
Ingest your datasets into the secure environment. A meta representation will be displayed in the web app; your data always stays local.
03.
Define use case
Create your use case, select datasets, pick a predefined benchmark or define your own, and add an EDA and description to guide vendors.
04.
Invite vendors & allocate compute
Invite vendors via email whitelisting. Assign compute budgets per use in Flops. Vendors can submit any model architecture compatible with TensorFlow or PyTorch.
05.
Train & fine-tune on your infrastructure
Vendors fine-tune models inside your isolated Kubernetes environment. Your data and fine-tuned weights never leave your infrastructure.
06.
Benchmark and discover best-performing model
Evaluate models on e.g. accuracy, latency, and robustness, efficiency, gCO₂e. Select the best model, negotiate usage terms with vendor, and hand over to your MLOps team.
Pricing
Pricing is based on the compute your vendors consume when training, finetuning, or benchmarking models on your infrastructure
HOBBY
Free
For individuals and early experimentation
Includes
Access to core platform features
20 PFs (Petaflops) of compute / month
100 model inferences / month
PRO
$15 / month
For professionals, startups and researchers
Everything in Hobby, plus
100 PFs & 500 inferences / month
Any additional 100 PFs at $8
Priority queuing for training & inference
Share metadatasets — enable AI use cases across multiple data owners
BUSINESS
Custom
For enterprises and larger teams
Everything in Pro, plus
Optimized for large-scale, high-volume AI workloads
Centralized team and admin management for compute, metadata, and use cases
Support for federated learning and on-prem/hybrid deployments
Enterprise-grade controls: SSO, RBAC, audit logging
Dedicated onboarding, SLA-backed reliability, and compliance guidance
Pricing
Pay only for what's used — pricing is based on the compute your vendors consume when training, fine-tuning, or benchmarking models on your infrastructure
FAQs
Answers to common questions
Business & Use Case
Business & Use Case
IT & Security
Training, Compute & Models
Data, IP & Compliance
Who typically uses tracebloc inside company?
Users usually are Senior, Lead, Principal, Director of Data Science. Other who draw benefits using tracebloc are
• Business teams -> model performance as a direct effect on the top or bottom line
• Procurement teams -> preselect and technically evaluate potential vendors
• Research teams -> more closely collaborate with other external researchers
• Engineering teams -> protecting data and IP
• Business teams -> model performance as a direct effect on the top or bottom line
• Procurement teams -> preselect and technically evaluate potential vendors
• Research teams -> more closely collaborate with other external researchers
• Engineering teams -> protecting data and IP
What kind of ROI can we expect from using tracebloc?
Can we use tracebloc for vendor selection?
How does tracebloc fit into our procurement process?
Is tracebloc a consulting service or a product?
Can we reuse evaluation setups for future tenders?
Do we need to sign a long-term contract?
How does tracebloc help reduce vendor lock-in?
How long does it take to set up a use case?
Can we run multiple use cases at once?
Can non-technical stakeholders understand evaluation results?
Can we reuse pipelines across similar use cases?
Can we collaborate with public research institutes?
Who typically uses tracebloc inside company?
Users usually are Senior, Lead, Principal, Director of Data Science. Other who draw benefits using tracebloc are
• Business teams -> model performance as a direct effect on the top or bottom line
• Procurement teams -> preselect and technically evaluate potential vendors
• Research teams -> more closely collaborate with other external researchers
• Engineering teams -> protecting data and IP
• Business teams -> model performance as a direct effect on the top or bottom line
• Procurement teams -> preselect and technically evaluate potential vendors
• Research teams -> more closely collaborate with other external researchers
• Engineering teams -> protecting data and IP
What kind of ROI can we expect from using tracebloc?
Can we use tracebloc for vendor selection?
How does tracebloc fit into our procurement process?
Is tracebloc a consulting service or a product?
Can we reuse evaluation setups for future tenders?
Do we need to sign a long-term contract?
How does tracebloc help reduce vendor lock-in?
How long does it take to set up a use case?
Can we run multiple use cases at once?
Can non-technical stakeholders understand evaluation results?
Can we reuse pipelines across similar use cases?
Can we collaborate with public research institutes?