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Glama AI Gateway Review: Pricing & Comparison

An AI gateway with transparent price comparison, quick multi-vendor switching for experiments

Last verified: 2026-07-04 · Visit official site →

Price transparency as a competitive strategy

In the AI middle-layer gateway market, Glama (glama.ai) has chosen a differentiated positioning: fully transparent pricing, 0% markup.

Quite a few AI API aggregation platforms carry a hidden markup — you think you’re paying upstream pricing, but there’s actually a 5% to 20% platform premium buried in the bill. Glama has gone the opposite direction: it lists each model’s upstream price directly in its docs, and commits to adding no extra fees.

This positioning targets developers who already know which model they want to use and just need a unified access point — not enterprise users who need complex feature sets.

Unified endpoint: one key, multiple models

Glama’s core feature is its unified endpoint — access models from different providers with a single API key:

from openai import OpenAI

client = OpenAI(
    api_key="your Glama API key",
    base_url="https://glama.ai/api/gateway/openai/v1"
)

# Switching models just means changing the model parameter
models_to_test = [
    "claude-fable-5",
    "gpt-5.5",
    "gemini-3.5-flash",
    "deepseek/deepseek-v4-pro"
]

prompt = "Explain what a vector database is in three sentences"

for model in models_to_test:
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}]
    )
    print(f"\n=== {model} ===")
    print(response.choices[0].message.content)

This pattern is particularly well suited to the model-selection stage: feed the same prompt to different models, quickly compare output quality, and decide which model best fits your specific task.

Comparison with Portkey/Helicone

Glama’s positioning is lighter-weight than Portkey and Helicone:

FeatureGlamaPortkeyHelicone
Unified multi-model endpointYesYesYes
Request loggingBasicDetailedDetailed
CachingNoYesYes
Guardrails/filteringNoYesPartial
Cost trackingBasicDetailedDetailed
0% markupYesNo (usage-based fees)Yes

If all you need is a simple gateway that unifies multiple APIs into one, without complex observability features, Glama is a low-friction choice. If you need production-grade monitoring and management, Portkey or Helicone offer more complete feature sets.

Information verified 2026-07-04. Check Glama’s official site for the current supported-model list and pricing.

  • Sulian AI: low-latency multi-line backup, disaster-recovery architecture, stable direct connect for production
  • Zhihui API: a new 2026 platform, redemption codes starting at ¥5, Opus as low as ¥8.12/million tokens
  • Portkey: the full LLMOps suite, 1600+ model coverage, 50+ guardrail rules, a production-grade AI gateway
  • SBGPT: exchange rate of ¥0.4-0.6/USD, Azure-grouped, a top pick for rock-bottom pricing

Quick facts

Pricing model0% markup, pass-through of upstream original pricing; billed by actual usage; a free tier is available to get started
Model coverageOpenAI, Anthropic, Google, Mistral, open-source models, and other mainstream providers, accessible via a unified endpoint
Latency / SLAMiddle-layer proxy; latency depends on the upstream provider; no rate limiting
Mainland direct connectProxy required
Best forDevelopers
Referral programNo public affiliate/referral program found.

Pros

  • Fully transparent pricing: Glama publicly displays each model's upstream price, so you know exactly where your money is going
  • 0% markup: you only pay the actual upstream API cost, with no extra profit margin tacked on — appealing to price-sensitive developers
  • Fast multi-vendor experimentation: switch between Claude/GPT/Gemini/open-source models with a single API key, making A/B testing efficient

Cons

  • Relatively basic feature set — it doesn't have the full observability and caching capabilities that Portkey offers
  • Requires a proxy from mainland China
  • A fairly new platform, so stability and feature maturity still need to be watched over time

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