THE COMPARISON / A CLOSER LOOK
Portkey vs Together AI
Compare the listed plans, useful capabilities, and practical tradeoffs.
Prices and features reflect editorial records. Check vendor terms before you buy; documentary checks do not imply hands-on testing.
Portkey
🔴DeveloperAI Gateway
Portkey is an AI gateway for routing, observing, securing, and governing production traffic across model providers, agents, and MCP tools.
SOURCE-CHECK COVERAGE
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Starting Price
FreeTogether AI
🔴DeveloperLLM Infrastructure
GPU cloud and inference platform for open-source LLMs — fast, low-cost APIs for Llama, DeepSeek, Qwen, Mistral, plus fine-tuning and dedicated endpoints.
SOURCE-CHECK COVERAGE
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Starting Price
$0.02/1M tokensFeature Comparison
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The editorial take
Choose Portkey if your team needs a governance and routing layer across many providers, with prompt management, observability, fallback policies, guardrails, and MCP Gateway controls. Choose Together AI if your main goal is direct access to open-source model inference and hosting rather than a cross-provider production control plane.
Portkey - Pros & Cons
Pros
- ✓Centralizes routing, fallbacks, observability, and policy
- ✓Supports multi-provider model access and MCP governance
- ✓Virtualized access can reduce direct credential distribution
- ✓Request and cost metadata can support team-level attribution
Cons
- ✗Fetched pricing page was script-heavy and prices need confirmation
- ✗Adds another dependency and latency hop to model requests
- ✗Retries and caching require careful side-effect and privacy controls
- ✗Gateway fees do not include underlying model-provider charges
Together AI - Pros & Cons
Pros
- ✓Cheaper per token than closed-model APIs for equivalent tasks — Llama 3.3 70B at ~$0.88/M is a real cost delta
- ✓Broad catalog covers most open models teams actually consider (Llama, DeepSeek, Qwen, Mistral, Kimi)
- ✓Three deployment tiers (serverless, dedicated, cluster) let you graduate without moving off the platform
- ✓Full-parameter and LoRA fine-tuning available with your dataset — a rare combination at this price
- ✓HIPAA-eligible and SOC 2 Type II environments make enterprise deals possible
Cons
- ✗Frontier reasoning still lags Claude, GPT, and Gemini for the hardest tasks; open models close but rarely lead
- ✗Serverless cold starts and shared-tenant latency vary — high-QPS production usually needs dedicated endpoints
- ✗Pricing per model shifts frequently as new SKUs launch; long-term budgeting requires occasional re-costing
- ✗Dedicated endpoints and clusters require some capacity planning — not fire-and-forget like serverless
Not sure which to pick?
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