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✓ verifiedFreemium
Serverless AI cloud for running inference, training and sandboxes on GPUs with fast cold starts and pay-per-use billing.
988K visits/mo
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Latitude
✓ verifiedFreemium
Open-source AI-agent observability platform for tracing sessions, clustering failures and running evals on live traffic.
57K visits/mo
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Maxim AI
✓ verifiedFreemium
End-to-end evaluation and observability platform for building, testing, and monitoring AI agents and LLM apps.
102K visits/mo
Pricing
Starter: $0/mo + compute ($30 free credit)
Team: $250/mo + compute
No public pricing
No public pricing
Developer: $0 (3 seats, 10k logs/mo)
Professional: $29/seat/mo (100k logs/mo)
Business: $49/seat/mo (500k logs/mo)
Free trial available
Core features
- ✦Serverless GPU compute defined in Python
- ✦Sub-second container cold starts
- ✦Autoscale 0 to 1000+ GPUs
- ✦Inference, training and batch workloads
- ✦Secure sandboxes for untrusted code
- ✦Built-in logging and observability
- ✦Open-Source AI Gateway
- ✦Multi-LLM Management & Cost Optimization
- ✦Efficient and Secure LLMs Invocation
- ✦Unified API Signature for LLMs
- ✦Load Balancer for seamless switching between LLMs
- ✦Fine-Grained Traffic Control for LLMs
- ✦LLM Quota Management
- ✦Real-time LLM Traffic Monitoring
- ✦Caching Strategies for AI in Production
- ✦Flexible Prompt Management
- ✦Agent trace capture and conversation intelligence
- ✦Semantic and exact-text search across all traces
- ✦Automatic issue discovery with Slack/email/webhook alerts
- ✦OpenTelemetry-compatible SDK with no lock-in
- ✦Automated evals and golden dataset generation
- ✦Failure-mode clustering and MCP server integration
- ✦Prompt IDE, versioning, and deployment
- ✦Agent simulation and evaluation
- ✦Production tracing and observability
- ✦Pre-built and custom evaluators
- ✦Human-in-the-loop evaluation
- ✦Bifrost LLM gateway
Use cases
- →Deploying and scaling model inference
- →Fine-tuning and training models
- →Running batch/parallel AI jobs
- →Executing untrusted code in sandboxes
- →Building API portals for secure sharing of internal APIs with partners.
- →Tracking API usage and driving API monetization.
- →Managing and securing API access in compliance with enterprise policies.
- →Connecting to multiple AI large models simultaneously.
- →Optimizing LLM costs and improving efficiency.
- →Protecting against LLM attacks and data leaks.
- →Monitoring AI agents in production
- →Debugging and triaging agent failures
- →Building regression evals from real traffic
- →Getting alerted on new or escalating issues
- →Testing and comparing prompts and models
- →Evaluating and simulating AI agents
- →Monitoring agents in production
- →Running human evaluation pipelines
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