Eldric vs AWS Bedrock

Core Philosophy

Deployment

Supported Operating Systems

Eldric

macOS

• macOS 12+ (Intel x86_64)
• macOS 12+ (Apple Silicon ARM64)
• Native SwiftUI GUI + CLI

Linux (x86_64 & ARM64)

• Ubuntu 22.04 LTS / 24.04 LTS
• Debian 11 / 12
• RHEL 9 / Rocky Linux 9 / AlmaLinux 9
• Fedora 40 / 41 / 42
• openSUSE Leap 15.5+

ARM / Edge / IoT

• Raspberry Pi OS (64-bit ARM)
• Raspberry Pi 4 / 5
• NVIDIA Jetson (ARM64)
• Any ARM64 Linux

Windows

• Windows 10 / 11 (Qt GUI)
• WSL2 (CLI)

AWS Bedrock

• Any OS with Python/AWS SDK
• But inference runs on AWS, not your machine
• Requires internet connection

Packages & Sizes

CLI & GUI

Distributed System

Package Formats

• .deb - Ubuntu, Debian
• .rpm - RHEL, Fedora, Rocky Linux
• .tar.gz - Universal Linux, macOS
• .pkg - macOS installer
• .exe - Windows installer

AWS Bedrock

• boto3 + dependencies: 100+ MB
• No local binaries (runs on AWS cloud only)

Performance Comparison

Latency (time to first token)

Throughput (tokens/sec)

Concurrent Requests

Offline Capability

Eldric Advantages

Complete Data Sovereignty

All inference runs on your hardware. No data leaves your network. GDPR/HIPAA/regulatory compliance built-in. No vendor lock-in.

Cost Efficiency at Scale

• AWS Bedrock (Claude): ~15/Minputtokens, 75/M output tokens
• Eldric (self-hosted): $0/token after hardware investment

For high-volume use cases, Eldric pays for itself quickly.

Specialized Workers Not Available in Bedrock

Backend Flexibility

Eldric supports 12+ backends simultaneously:

• Local: Ollama, vLLM, llama.cpp, TGI, MLX
• Enterprise: NVIDIA Triton, TensorFlow Serving, TorchServe
• Cloud (optional): OpenAI, Anthropic, Google AI

Bedrock locks you into their model catalog.

Distributed Architecture

• Multi-region deployment with edge servers
• Intelligent load balancing across heterogeneous hardware
• AI-powered routing decisions
• No AWS region limitations

Agent Builder (Unique to Eldric)

• Autonomous agent creation using reasoning LLMs (DeepSeek R1, Qwen QwQ)
• 8-phase build pipeline with testing and validation
• Agent Hub marketplace for sharing/forking agents
• Agent evolution and learning

Full RAG Stack Ownership

• Vector database on your infrastructure
• Custom embedding models
• Multi-tenant knowledge bases
• No data egress to AWS

When to Use Each

When Bedrock Makes Sense

• Quick prototyping without infrastructure
• Small-scale usage (< $1K/month in tokens)
• Already deep in AWS ecosystem
• Need access to specific models (Titan, some Claude features)

When Eldric Wins

• Regulated industries (healthcare, finance, government)
• High-volume inference (millions of requests/day)
• Specialized workloads (bioinformatics, industrial IoT)
• Air-gapped environments
• Cost-sensitive production deployments
• Custom model requirements (fine-tuned, quantized, merged)
• Edge deployment (Raspberry Pi, Jetson, IoT devices)

TCO Comparison (10M tokens/day)

Eldric breaks even in 1-2 months for heavy workloads.

Free vs Paid Tiers

FREE GUI Client - Working

Chat & AI

• ✅ Chat interface with Ollama
• ✅ All 9 agent types
• ✅ Multimodal input (images)
• ✅ Conversation history & sessions
• ✅ Streaming responses
• ✅ Tool execution (Bash, Read, Write, Edit, Glob, Grep)

Model Management

• ✅ Model Workbench (download, delete, manage)
• ✅ Quick Create Model (Modelfile creation)
• ✅ Model Comparison (side-by-side)
• ✅ Model Templates
• ✅ Model Visualizer

Model Merging

• ✅ Linear, SLERP, TIES, DARE methods
• ✅ Task arithmetic
• ✅ Real merging via mergekit/ollama

Training

• ✅ LoRA/QLoRA fine-tuning (requires llama.cpp or axolotl/unsloth installed)
• ✅ Training job management
• ✅ Dataset preparation
• ⚠️ Training Chain Designer (UI ready, workflow execution partial)

RAG & Data

• ✅ RAG Manager (document ingestion, vector search)
• ✅ Database Browser (SQLite, PostgreSQL, MySQL)
• ✅ Embeddings (local via Ollama)

Agents

• ✅ Agent Designer (create custom agents)
• ✅ MCP Creator (build MCP servers)
• ✅ Prompt Engineering

Backends (Free)

• ✅ Ollama (2 workers)
• ✅ llama.cpp (2 workers)

In Development (UI ready, backend partial)

• ⚠️ Alignment Training (RLHF, DPO) - UI complete, backend calls simulated
• ⚠️ Latent Reasoning Workbench - UI complete, needs reasoning model integration
• ⚠️ MoE Manager - UI complete, requires distributed Controller

FREE Distributed System - Working

Core

• ✅ 1 Controller (full dashboard)
• ✅ 1 Router (load balancing)
• ✅ 2 Workers (inference)

Specialized Workers

• ✅ 1 Data Worker (storage, DB, RAG)
• ✅ 1 Agent Worker (agentic RAG)
• ✅ 1 Media Worker (STT/TTS via Whisper/Piper)
• ✅ 1 IoT Worker (smart home)
• ✅ 1 Science Worker (210 API providers)

Comm Worker (Mostly Working)

• ✅ Email (IMAP/SMTP) - fully working
• ✅ SMS (Twilio) - working
• ✅ WhatsApp (Business API) - working
• ✅ Signal - working
• ✅ Teams - working
• ⚠️ XMPP - partial (2 TODOs remaining)

Science Worker (Free Limits)

• ✅ 10 API providers (NASA, NCBI, PDB...)
• ✅ 100 API calls/hour
• ✅ Space, Genomics, Literature categories
• ✅ 10 queries/day research agent
• ❌ BLAST, docking, ADMET, AlphaFold - paid only

Media Worker (Free Limits)

• ✅ Audio transcription up to 5 min
• ✅ Video transcription up to 2 min
• ✅ 2 concurrent jobs
• ❌ Streaming, diarization, voice cloning - paid only

Agent Hub (New)

• ✅ Source code database
• ✅ Docker compilation
• ⚠️ Agent Builder - paid feature, backend implemented

PAID Tiers - What You Get

Standard

• ✅ More workers (3 total)
• ✅ vLLM, TGI backends
• ✅ BLAST search
• ✅ Agent Builder (1 concurrent build)
• ✅ 30 min audio, 15 min video
• ✅ All comm protocols

Professional

• ✅ 5 workers
• ✅ NVIDIA NIM, Triton backends
• ✅ Molecular docking, ADMET
• ✅ Agent Builder (5 builds, evolution)
• ✅ 2 hour audio, 1 hour video
• ✅ GLP compliance

Enterprise

• ✅ 30+ workers
• ✅ All backends including cloud APIs
• ✅ IIoT Worker (Industrial IoT)
• ✅ AlphaFold integration
• ✅ 21 CFR Part 11 compliance
• ✅ Unlimited everything
• ✅ Private Agent Hub

Legend

• ✅ = Working now
• ⚠️ = UI ready, backend partial or needs external tools
• ❌ = Paid feature

Free tier is fully usable for:

• Local AI chat with tools
• Model management & merging
• Training (with llama.cpp/axolotl installed)
• RAG & database access
• Basic distributed system (1 controller, 2 workers)
• Science APIs (limited)
• Media processing (limited)
• Email integration

Get Started

macOS:

brew install ollama && brew install eldric

Linux (Ubuntu/Debian):

curl -fsSL https://get.eldric.ai | sh

Linux (RHEL/Fedora):

dnf install eldric

Raspberry Pi: Download from eldric.ai/raspberry

Windows: Download installer from eldric.ai