Discover High-Performance AI Models.
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.
01
Define AI Use Case
02
Invite Peers
03
Training and fine tuning
04
Control data
05
Evaluate Model
06
Drive ROI
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Easily test
which vendor models perform best —
on your data, for your use cases

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Any vendor, anywhere
Work with any vendor — from startups to research labs — globally, in minutes. Onboard instantly via email whitelisting.
Any use case, any task
Evaluate models for any use case — from classification to forecasting — across vision, language, tabular, and more.
Test inside your environment
Test external models directly on your infrastructure — no data transfer, full control, complete compliance.
Supports strict governance
Easily integrate with your internal audit, approval, and access control systems — no shortcuts, no risks.
Secure by infrastructure
Training and inference run in secure environments — on bare metal or private cloud within your infrastructure — keeping company data protected at all times.
Trust without compromise
Work with external AI experts while maintaining full compliance, control, and peace of mind.

Discover the best-performing AI models —
maximize ROI

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Boost ROI of your use cases
Select higher-performing models for your task — drive measurable business impact and keep your stakeholders happy.
Tailored fine-tuning at the edge
Allow vendors to finetune models securely on your data — unlocking higher accuracy and task-specific performance.
Global discovery, zero friction
Tap into top-performing models from startups, researchers, and vendors worldwide.
Supports strict governance
Easily integrate with your internal audit, approval, and access control systems — no shortcuts, no risks.
Secure by infrastructure
Training and inference run in secure environments — on bare metal or private cloud within your infrastructure — keeping company data protected at all times.
Trust without compromise
Work with external AI experts while maintaining full compliance, control, and peace of mind.

Keep
your data and IP.

private and protected — stay in full control

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Enterprise-grade security
Built for the strictest environments — from healthcare to defense. Fully aligns with enterprise security and compliance policies.
No data transfer. No exposure
All training happens inside your infrastructure — nothing leaves. No data movement, no data sharing.
Finetuned weights stay on-prem
Ensuring full protection of your data and intellectual property at all times.
Supports strict governance
Easily integrate with your internal audit, approval, and access control systems — no shortcuts, no risks.
Secure by infrastructure
Training and inference run in secure environments — on bare metal or private cloud within your infrastructure — keeping company data protected at all times.
Trust without compromise
Work with external AI experts while maintaining full compliance, control, and peace of mind.
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.
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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.
Vendor Onboarding
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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.

Step 1: Set up secure environment
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.

Step 2: Ingest training and test data
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.

Step 3: Define use case
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.

Step 4: Invite vendors & allocate compute
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.

Step 5: Train & fine-tune on your infrastructure
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.

Step 6: Benchmark and discover best-performing model
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

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

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

FAQs

Answers to common questions

Business & Use Case

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

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?