Compare Private AI Infrastructure
Data-driven comparison of model training platforms. All numbers verified from official documentation.
Achiral AI
Private Model Training Platform
$149/month
Best for: Teams training private models without ML expertise
OpenAI API
General-Purpose AI API
Pay per token
Best for: Prototyping and general-purpose AI tasks
Self-Hosted
Complete Control
$8,000+/month
Best for: Organizations with ML teams and strict data requirements
AWS SageMaker
Cloud ML Platform
Variable
Best for: AWS-native organizations with ML expertise
Model Training & Customization
| Feature | Achiral AI | OpenAI API | Self-Hosted | AWS SageMaker |
|---|---|---|---|---|
| Private Model Training | Full fine-tuning Train foundational models on your private data | Limited API fine-tuning Fine-tune select models via API only | Complete control Any model, any method, requires ML expertise | SageMaker training Flexible but complex setup |
| Who Trains | Domain experts No ML/data science team required | Developers API integration + data prep | ML engineers Requires dedicated ML expertise | ML engineers Cloud ML knowledge needed |
| Training Timeline | 1-3 weeks Initial training to production-ready model | Days API fine-tuning when available | 4-12 weeks Setup, training, validation, deployment | 2-8 weeks Infrastructure + training + optimization |
| Continuous Learning | Built-in Models improve with human feedback loop | Not available Static models post-training | DIY Build your own feedback pipeline | You implement Requires custom workflow |
Data Privacy & Isolation
| Feature | Achiral AI | OpenAI API | Self-Hosted | AWS SageMaker |
|---|---|---|---|---|
| Data Isolation | Logical multi-tenant Dedicated tenant per org, pod isolation for Elite | Shared infrastructure Multi-tenant with no isolation guarantees | Complete Your infrastructure, your data | VPC isolation Shared underlying hardware |
| Training Data Privacy | Never leaves platform Data stays in your tenant, zero external sharing | API terms apply Data used for improvements unless opted out | Complete control Data never leaves your infrastructure | AWS access possible Per AWS service agreement terms |
| Model Ownership | You own fine-tuned model Full control of trained adapters | OpenAI owns base model Limited access to fine-tuned version | Complete ownership You own everything | You own Model artifacts in your S3 |
| Compliance | SOC 2, HIPAA-ready, GDPR Certified and ready for BAA | SOC 2 Enterprise tier only | You certify Your responsibility to maintain | AWS compliance Inherit AWS certifications |
Infrastructure & Operations
| Feature | Achiral AI | OpenAI API | Self-Hosted | AWS SageMaker |
|---|---|---|---|---|
| Provisioning Time | 2-3 minutes Automated tenant provisioning | Instant API key only, no infrastructure | 1-4 weeks Hardware, software, and network setup | 1-2 weeks Account setup, VPC, SageMaker configuration |
| Infrastructure Type | Managed in data center Not AWS/GCP/Azure - our Kubernetes cluster | Fully managed OpenAI handles everything | You provision On-prem or cloud, you manage | AWS managed services SageMaker, EC2, S3 stack |
| GPU Management | Managed 128GB VRAM shared (GX10), dedicated pods for Elite | Not applicable API service, no GPU exposure | You manage Purchase, maintain, upgrade GPUs | AWS GPU instances Pay per hour, configure yourself |
| Uptime SLA | 99.9% Scale tier and above | 99.9% Enterprise tier only | Your responsibility No SLA unless you build it | 99.9% SageMaker service SLA |
| Updates & Maintenance | Automatic Zero-downtime rolling updates | Automatic No control over timing | Manual You schedule and execute | Semi-automatic Requires configuration and testing |
Pricing & Costs
| Feature | Achiral AI | OpenAI API | Self-Hosted | AWS SageMaker |
|---|---|---|---|---|
| Pricing Model | Fixed monthly $149/$599/$1,999/mo flat rate | Per-token metered Usage-based, unpredictable scaling costs | Infrastructure + labor GPU rental ($3-10k/mo) + ML engineers ($150-250k/yr) | Per-hour + storage GPU hours + S3 + data transfer fees |
| Cost at 100K requests/day | $599-$1,999/mo Fixed, no surprise bills | $5,000-$15,000/mo Depends on model and token count | $8,000-$25,000/mo GPU + bandwidth + engineering time | $4,000-$12,000/mo Instance hours + storage + egress |
| Hidden Costs | None All-inclusive pricing | Token overages Spikes during high usage | Labor + downtime Ongoing maintenance and troubleshooting | Data transfer Egress charges can be significant |
| Price Predictability | High Know exact monthly cost upfront | Low Varies with usage patterns | Medium Fixed infra, variable ops | Low Many billing components |
Performance & Scale
| Feature | Achiral AI | OpenAI API | Self-Hosted | AWS SageMaker |
|---|---|---|---|---|
| Context Window | 128K tokens Large context for complex tasks | 128K tokens GPT-4 Turbo and above | Model dependent Varies by model choice | Model dependent Depends on deployed model |
| Request Quotas | Tier-based Spark: 5, Seed: 20, Scale: 100, Dedicated: unlimited | RPM/TPM limits Strict rate limits per tier | Hardware limited No artificial caps | Service quotas Request increases needed |
| Token Limits | Tier-based Spark: 2K/req 10K/hr, Seed: 4K/req 100K/hr, Scale: 8K/req unlimited | Per-model Varies by model tier | Hardware limited Depends on GPU memory | Instance dependent Based on instance type |
| Latency | Multi-tenant shared Variable based on load, pod isolation for Elite | 200-500ms P95 Shared multi-tenant infrastructure | You optimize Depends on setup and tuning | 100-300ms Varies by region and instance |
Choose Based On Your Needs
Choose Achiral AI if you:
- Want to train models on private data without hiring ML engineers
- Need predictable flat-rate pricing with no per-token charges
- Require SOC 2 / HIPAA compliance without managing infrastructure
- Want domain experts to train models, not data scientists
- Need production-ready models in weeks, not months
Choose OpenAI if you:
- Need general-purpose AI for prototyping
- Have minimal data privacy requirements
- Want instant setup with no infrastructure
- Don't need to train custom models on private data
Choose Self-Hosting if you:
- Have ML engineering expertise in-house
- Need absolute control over every infrastructure detail
- Can handle ongoing maintenance, upgrades, and ops
- Have budget for GPUs and engineering labor ($20k+/mo)
Choose AWS if you:
- Already use AWS for all infrastructure
- Have cloud ML expertise on your team
- Need tight integration with other AWS services
- Can manage complex billing and cost optimization
Limitations
- •No multi-cloud deployment: We run our own infrastructure, not AWS/GCP/Azure
- •No on-premise deployment: Platform runs in our data center only
- •Provisioning time: Initial training takes 1-3 weeks for production quality
- •Single region: All infrastructure currently in one data center
We're transparent about constraints. If any of these are blockers, contact us to discuss alternatives.
Ready to train your private model?
Fixed pricing, no ML expertise required, production-ready in weeks.