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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.

SiliconFlow logo
SiliconFlow
✓ verified

Developer platform serving 200+ optimized LLMs via APIs; high traffic.

434K visits/mo1.1K saves
liteLLM logo
liteLLM
✓ verifiedFreemium

Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.

703K visits/mo
4.4M visits/mo
Weights & Biases logo
Weights & Biases
✓ verifiedFreemium

Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.

2.5M visits/mo
Fiddler AI logo
Fiddler AI
✓ verifiedFreemium

Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.

51K visits/mo
Pricing

No public pricing

Open Source: $0 (self-hosted, 100+ providers)

Free trial available

No public pricing

No public pricing

Free: $0 (real-time guardrails)
Developer: $0.002 per trace
Core features
  • Access over 200 optimized models, including LLMs, image, video, and audio processing.
  • Achieve low-latency, high-throughput inference with SiliconFlow's self-developed acceleration frameworks.
  • Deploy models via serverless inference, dedicated endpoints, or reserved GPUs to suit various workloads.
  • Customize models to your data with built-in monitoring and elastic compute resources.
  • Ensure data privacy and business security with dynamic scaling and fault tolerance mechanisms.
  • Unified access to 100+ LLMs in OpenAI format
  • Cost/spend tracking per key, user and team
  • Budgets and rate limiting
  • Automatic provider fallbacks and retries
  • Virtual keys and team management
  • Logging and observability integrations
  • Dialogue with GLM large model
  • AI search
  • AI drawing
  • AI reading
  • AI-generated video (沉思清影-AI生视频)
  • AI-generated PPT
  • Data analysis tools
  • Code assistance (代码速写)
  • Intelligent agents
  • Experiment tracking and visualization for ML training runs
  • Model and artifact versioning and management
  • Hyperparameter optimization tooling
  • Collaborative dashboards and reports for ML teams
  • LLM application tracing and evaluation tooling
  • End-to-end agentic and ML observability
  • Real-time guardrails (hallucination, PII, jailbreak)
  • Continuous evaluations and custom judges
  • Root-cause analysis and decision lineage
  • AI governance, risk, and compliance controls
  • Flexible SaaS, VPC, or on-prem deployment
Use cases
  • Quickly deploy various AI models via a simple API, supporting tasks like text, image, audio, and video processing.
  • Utilize serverless GPUs to automatically scale AI applications, ensuring flexibility and cost-efficiency.
  • Access high-performance GPUs for demanding workloads, such as large-scale inference and video generation.
  • Deploy custom models with guaranteed performance and scalability, tailored to specific business needs.
  • Giving developers governed access to many LLMs
  • Attributing and controlling LLM spend
  • Keeping apps running during provider outages
  • Engaging in conversations with an AI model
  • Generating images and videos using AI
  • Creating presentations with AI assistance
  • Analyzing data with AI tools
  • Assisting with code development
  • ML engineers tracking and comparing training experiments
  • Research teams versioning datasets and model checkpoints
  • Teams building and evaluating LLM-powered applications
  • Organizations collaborating on machine learning projects
  • Monitoring production AI agents
  • Enforcing safety guardrails on LLM apps
  • Evaluating and debugging model behavior
  • Governance and compliance for enterprise AI
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