Best AI Inference Providers Curated by GitHub Users

Abstract

This technical assessment provides an evidence-based analysis of AI inference / LLM providers. In contrast to commercial review sites, this framework prioritizes empirical analysis via independent security audits, public source code availability, and operational transparency focused on privacy.

Simply the facts.

Methodology

Evaluation Criteria

Our evaluation considers:

1. Code Transparency: Public availability of source code and model weights

2. Independent Verification: Third party review and documentation

3. Architectural Verifiability: Fact or trust

4. Organizational Transparency: Public disclosure of ownership and policies

5. Privacy Architecture: Technical implementation and training defaults

Ignore the marketing. Read the facts.

AI Service Comparison

Rank	Service	Source Available	Proof	Anonymous Use	Self-Hostable	No Training on Data	No Correlation
1	Self-Hosted Open-Weights	Yes	Yes (you control)	Yes	Yes	Yes	Yes
2	Lumo (Proton)	~ Mixed (clients open; backend/models not fully public)	Yes	No (Proton account required)	No	Yes	?
3	Venice AI	Mixed (open models; platform proprietary)	Yes	Yes (no-login free tier)	No	Yes	?
4	Hugging Face Endpoints	Yes	Yes	No	Yes	Yes	?
5	AWS Bedrock	Yes (mixed)	Yes	No	No	Yes	?
6	Google Vertex AI	No	Yes	No	No	Yes (restricted)	?
7	Azure OpenAI	No	Yes	No	No	Yes	?
8	Meta Llama API	Yes (open weights)	Yes	No	Yes	Yes	?
9	Together AI	Yes (mostly open)	Yes	No	Yes	Yes (configurable)	?
10	OpenAI API/Team/Enterprise	No	Yes	No	No (limited)	Yes (Enterprise/API)	?
11	Mistral	Yes (mixed)	Yes	No	Yes	Yes (Enterprise)	?
12	Cohere	No	Yes	No	No	No (opt-out required)	?
13	Claude (Anthropic)	No	Yes	No	No	No (Consumer) / Yes (Enterprise)	?

Critical Understanding: Architectural vs Policy Based Privacy

Class 1: Architectural Privacy (Self-Hosted)

The following approach represents complete control over AI inference. External providers cannot train on your data by design.

Self-Hosted Open-Weights Models: Complete control over inference; no third-party logs, retention, or training; architecturally private. Models include Llama 3, Mistral, Qwen, Phi, Gemma, Mixtral. Frameworks include Ollama, vLLM, LM Studio, Text Generation WebUI, TGI/HuggingFace, local CUDA/MPS/Metal inference.

Class 2: Privacy-Oriented Hosted Providers

These providers attempt to architect systems to minimize data exposure (but still require trust):

Lumo (Proton): Zero-access encryption of chat history; no logs of conversations; no training on user prompts; open-source codebase claimed publicly; production pipeline not attested, trust required.
Venice AI: Local-only history; no server-side logging; decentralized GPU compute; no training on user prompts; encryption via local browser + proxy; no third-party audits, trust required. Exposes decrypted prompts to decentralized GPU operators during inference.

Class 3: Contractually Private Enterprise/API

These providers do not train on enterprise/API data by policy, verified through documentation and compliance frameworks. Note: These are managed cloud services and cannot be self-hosted.

All rely on trust in infrastructure, operations teams, provider honesty, provider logs, and subpoena/LEA exposure.

Hugging Face Endpoints: No training on user data; SOC 2 compliance; models can be self-hosted separately
AWS Bedrock: No training on user data; extensive compliance certifications; managed service only
Google Vertex AI: No training on user data (restricted); compliance certified; managed service only
Azure OpenAI: No training on user data; compliance certified; managed service only
Meta Llama API: No training on user data; Llama models can be self-hosted separately
Together AI: Configurable retention; can disable training; uses open models that can be self-hosted separately
OpenAI (Enterprise/API): No training on user data for paid enterprise/API tiers; proprietary models
Mistral (Enterprise): No training on user data for enterprise tier; open models can be self-hosted separately

Class 4: Policy-Based Privacy (Opt-Out Required)

Providers that train on user data by default; privacy requires configuration:

Mistral (Consumer): Trains on user data by default
Cohere: Requires explicit opt-out
Claude (Consumer): Uses chats and coding sessions for training by default unless you opt out; Anthropic currently keeps this data for up to ~5 years

Detailed Service Analysis

1. Self-Hosted Open-Weights Models

Code transparency: Fully available (Llama, Mistral, Qwen, Phi, Gemma, Mixtral, etc.)
Verification: You control all aspects
Org transparency: N/A (you are the operator)
Privacy architecture: Complete isolation; no third-party access; no external logging or training
Signup & payment: N/A
What's logged (by policy): Nothing (you control all logs)
Demonstrated correlation capability: None (you control infrastructure)
Operational history: Varies by model (1-3+ years)

2. Lumo (Proton)

Code transparency: Proton markets Lumo as open source and publishes client apps and some controls on GitHub, but the full backend stack and model weights are not yet fully public.
Verification: Proton security documentation
Org transparency: Fully disclosed
Privacy architecture: Zero-access encryption for stored chat history; Proton states it does not log or use conversations to train models; production pipeline still requires trust.
Signup & payment: Proton account required (email or alias); no fully anonymous use
What's logged (by policy): Account metadata only
Demonstrated correlation capability: Unknown
Operational history: ~1 year (launched 2024)

3. Venice AI

Code transparency: Uses open models; platform proprietary
Verification: Privacy architecture documentation
Org transparency: Disclosed
Privacy architecture: Local-only history; no server-side logging; decentralized GPU compute; browser-based encryption
Signup & payment: No account needed for basic free tier; email required for higher limits/Pro
What's logged (by policy): No prompts or model responses server-side; limited metadata and event logs retained
Demonstrated correlation capability: Prompts exposed to decentralized GPU operators during inference
Operational history: ~1 year (launched 2024)

4. Hugging Face Endpoints

Code transparency: Fully published
Verification: SOC 2 Type 2 certified
Org transparency: Fully disclosed
Privacy architecture: Isolated inference; no training on user data; dedicated endpoints available
Signup & payment: Email required; accepts card
What's logged (by policy): Access logs (configurable retention)
Demonstrated correlation capability: None disclosed
Operational history: ~8 years

5. AWS Bedrock

Code transparency: Mixed (some models open, infrastructure proprietary)
Verification: Extensive compliance certifications
Org transparency: Fully disclosed
Privacy architecture: Customer data isolation; no training on customer data
Signup & payment: Email required; accepts card
What's logged (by policy): CloudTrail logs (customer controlled)
Demonstrated correlation capability: None disclosed
Operational history: ~18 years (AWS), ~2 years (Bedrock)

6. Google Vertex AI

Code transparency: Proprietary
Verification: Compliance certifications
Org transparency: Fully disclosed
Privacy architecture: Customer data isolation; restricted training policies
Signup & payment: Email required; accepts card
What's logged (by policy): Cloud Logging (customer controlled)
Demonstrated correlation capability: None disclosed
Operational history: ~17 years (Google Cloud), ~3 years (Vertex AI)

7. Azure OpenAI

Code transparency: Proprietary
Verification: Compliance certifications
Org transparency: Fully disclosed
Privacy architecture: Customer data isolation; no training on customer data
Signup & payment: Email required; accepts card
What's logged (by policy): Azure Monitor logs (customer controlled)
Demonstrated correlation capability: None disclosed
Operational history: ~15 years (Azure), ~2 years (Azure OpenAI)

8. Meta Llama API

Code transparency: Open weights published
Verification: Security documentation
Org transparency: Fully disclosed
Privacy architecture: No training on API user data; standard cloud logging
Signup & payment: Email required; accepts card
What's logged (by policy): Standard access logs
Demonstrated correlation capability: None disclosed
Operational history: ~2 years (API), ~20 years (Meta)

9. Together AI

Code transparency: Mostly open models
Verification: Security and compliance documentation
Org transparency: Disclosed
Privacy architecture: Configurable data retention; can disable training
Signup & payment: Email required; accepts card
What's logged (by policy): Configurable
Demonstrated correlation capability: None disclosed
Operational history: ~3 years

10. OpenAI API/Team/Enterprise

Code transparency: Proprietary
Verification: SOC 2 Type 2 certified
Org transparency: Disclosed
Privacy architecture: No training on API/Enterprise data; standard cloud infrastructure
Signup & payment: Email required; accepts card
What's logged (by policy): API logs retained up to 30 days for abuse monitoring; business products (Team/Enterprise/Edu) have configurable retention; consumer/free tiers retain data longer for product improvement
Demonstrated correlation capability: None disclosed
Operational history: ~7 years

11. Mistral

Code transparency: Mixed (open models + proprietary)
Verification: Security documentation
Org transparency: Disclosed
Privacy architecture: No training on enterprise data; consumer tier trains by default
Signup & payment: Email required; accepts card
What's logged (by policy): Varies by tier
Demonstrated correlation capability: None disclosed
Operational history: ~2 years

12. Cohere

Code transparency: Proprietary
Verification: SOC 2 Type 2 certified
Org transparency: Disclosed
Privacy architecture: Training opt-out required; enterprise options available
Signup & payment: Email required; accepts card
What's logged (by policy): Standard access logs
Demonstrated correlation capability: None disclosed
Operational history: ~4 years

13. Claude (Anthropic)

Code transparency: Proprietary
Verification: SOC 2 Type 2 certified
Org transparency: Disclosed
Privacy architecture: Consumer tier trains by default unless you opt out; Enterprise/API data not used for training
Signup & payment: Email required; accepts card
What's logged (by policy): Consumer: retention currently up to ~5 years; Enterprise/API: no training use
Demonstrated correlation capability: None disclosed
Operational history: ~3 years

Conclusion

Self-hosting represents the only truly verifiable privacy option for AI inference. All hosted providers, even the most privacy-focused like Lumo and Venice, rely on trust in infrastructure, operations, and policies rather than cryptographically verifiable architecture.

The privacy hierarchy is clear: architectural privacy (self-hosting) > privacy-oriented providers (Lumo, Venice) > enterprise/API tiers with contractual no-training guarantees > consumer services with opt-out training.

For maximum privacy: run open-weights models (Llama, Mistral, Qwen) locally using Ollama, vLLM, or similar frameworks. Everything else requires trust.