Portkey vs Together AI
Detailed side-by-side comparison to help you choose the right tool
Portkey
🔴DeveloperLLM Gateway & Observability
Production AI control plane: AI gateway, prompt management, observability, guardrails, and MCP gateway in front of 1,600+ LLM providers.
Was this helpful?
Starting Price
FreeTogether AI
🔴DeveloperAI Model Hosting & Inference
AI-native cloud for inference, fine-tuning, and dedicated GPU clusters, offering 200+ open-source and frontier-class models behind an OpenAI-compatible API plus reserved H100/H200/B200 capacity.
Was this helpful?
Starting Price
$0.02/1M tokensFeature Comparison
Scroll horizontally to compare details.
💡 Our 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
- ✓OpenAI-compatible API gives teams one integration point while still routing to 1,600+ models across providers such as OpenAI, Anthropic, Google, Mistral, AWS Bedrock, Azure OpenAI, Cohere, Together, Fireworks, and Groq.
- ✓Fallback and load-balancing are built into the gateway layer, so reliability policies can be configured centrally instead of duplicated across each application service.
- ✓Combines 5 production AI functions in one platform: AI gateway, prompt management, observability, guardrails, and MCP Gateway.
- ✓Prompt versioning and A/B testing help teams change production prompts with more control than hard-coded prompt strings in application code.
- ✓Observability includes per-request tracing and cost analytics, which is especially useful when several teams or products share model providers.
- ✓Enterprise options mentioned in the available content include VPC deployment, SSO, audit logs, and SOC 2 / HIPAA support.
Cons
- ✗Adds a hosted gateway hop between the application and the LLM provider, so teams must evaluate added latency and dependency risk.
- ✗The main paid self-serve plan is $49/month for 100k recorded logs, with overage fees beyond that included quota.
- ✗May be more platform than needed for teams that only want basic LLM request logging or tracing.
- ✗Advanced enterprise controls such as VPC deployment, SSO, audit logs, and compliance support appear oriented toward Enterprise contracts rather than small self-serve users.
- ✗Teams must learn Portkey-specific routing, guardrail, prompt, and gateway configuration concepts before they get full value.
Together AI - Pros & Cons
Pros
- ✓Breadth of open-weight model catalog (200+) with one OpenAI-compatible API
- ✓One account spans serverless, dedicated endpoints, fine-tuning, and reserved GPU capacity
- ✓Transparent per-token pricing — easy to model unit economics against closed providers
- ✓InfiniBand-backed GPU Clusters are credible for real training, not just inference
Cons
- ✗Frontier-class reasoning still lags closed models on the hardest benchmarks
- ✗Fastest single-model latency is sometimes beaten by Groq or Cerebras
- ✗Many model variants means model selection itself becomes a project
- ✗Dedicated endpoint cost calculations require attention to GPU type and utilization
Not sure which to pick?
🎯 Take our quiz →🔒 Security & Compliance Comparison
Scroll horizontally to compare details.
Price Drop Alerts
Get notified when AI tools lower their prices
Get weekly AI agent tool insights
Comparisons, new tool launches, and expert recommendations delivered to your inbox.