Portkey AI
AI gateway + observability — load balancing across 200+ LLMs, retries, caching, eval, prompts. Open-source Gateway + commercial Portkey Cloud.
At a glance
- Type
- Production AI gateway
- Tier
- T2
- Section
- Inference platforms & gateways
- Created
- 2023
- Latest release
- searched not found
- License
- MIT (Gateway)
- GitHub
- searched not found
- Pricing
- searched not found
- Funding
- $3M seed 2024 (Lightspeed India + Y Combinator); OSS + Cloud gateway/observability.
Taxonomy
- storage
- n/a
- retrieval
- n/a
- persistence
- n/a
- update
- n/a
- unit
- n/a
- governance
- n/a
- conflict
- n/a
When to use
Optimised for: searched not found
Anti-fit: searched not found
Pros & cons
Pros
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Cons
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Claims & capabilities
Seed-funded; OSS + Cloud.
Technical surface
- API surface
- searched not found
- Backend storage
- not applicable — not a memory product
- Deployment
- searched not found
- Embedding model
- not applicable — not a memory product
- Multi-tenancy
- searched not found
- MCP
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- A2A
- searched not found
- OpenTelemetry
- searched not found
Similar systems
Other inference platforms & gateways in the catalog, ranked by inbound references.
- LiteLLM T1
BerriAI's open-source LLM gateway — unified OpenAI-format API for 100+ providers; LiteLLM Proxy adds budgets, fallbacks, observability, rate-limiting.
- vLLM T1
Open-source LLM inference engine with PagedAttention — high-throughput batching, paged KV-cache. UC Berkeley origin; de-facto OSS inference stack.
- Anyscale T1
Commercial platform built on Ray — distributed training, fine-tuning, serving. Anyscale Endpoints provides hosted OSS inference; ray cluster is the substrate underneath OpenAI's training stack.
- Baseten T1
Inference platform for ML models — Truss package format, Chains for multi-model workflows, autoscaling GPU/CPU serving. Targeted at production teams shipping LLM/diffusion endpoints.
- Fireworks AI T1
Fast inference platform for OSS LLMs — custom Cuda kernels, speculative decoding, multi-LoRA serving. Targets latency-sensitive production use cases.
- Modal T1
Serverless cloud for AI/ML — Python-decorator workflow defines GPU/CPU functions deployed to managed infra; widely used for training jobs, batch inference, and agent backends.