Skip to content

Same-model provider benchmark

Amazon Bedrock vs Phala: LLM provider comparison

Compare Amazon Bedrock and Phala on 3 exact shared text models. ProviderBench keeps speed, price, and catalog coverage separate so naturally faster model catalogs cannot distort the result.

Amazon Bedrock

23 indexed models

Headquarters
United States
Server regions
eu-west-1 · Irelandus-east-1 · United States
Model types
23 text

Phala

18 indexed models

Headquarters
United States
Server regions
N/a
Model types
18 text

At a glance

Metric winners

There is no overall score. Each winner answers one specific question using only directly comparable data.

Fastest on shared models

3.37× typical advantage

3 exact models with complete recent speed data

Lowest token cost

$0.71 / 1M

3 exact models using a 1K-input/500-output mix

Most models available

23 models

Complete catalog coverage across all indexed modalities

Shared-model benchmark summary

MetricAmazon BedrockPhala
Typical 500-token response3.59 s12.10 s
Typical response start1.55 s0.74 s
Typical output speed207.0 tok/s44.0 tok/s
Blended token price$0.71 / 1M$0.7811 / 1M
Typical route uptime99.97%99.77%

Visual comparison

Price and performance charts

Amazon BedrockPhala
500-token response by shared model

Estimated seconds using recent median response-start and output-speed data. Lower is better.

Amazon Bedrock has the lower typical same-model response ratio across 3 measured models.

Blended token price by shared model

USD per 1 million tokens using a 1,000-input/500-output mix. Lower is better.

Amazon Bedrock has the lower typical price ratio across 3 priced shared models.

Shared text models

3 exact models · newest first

ModelAmazon BedrockPhala
GLM 5Winner · Amazon Bedrock

z-ai/glm-5

Amazon Bedrock
Blended price
$1.7333 / 1M
500-token response
22.41 s
Response starts
3.18 s
Output speed
26.0 tok/s
Route uptime
40.91%
Context
202.8K
Route
amazon-bedrock
Phala
Blended price
$1.9667 / 1M
500-token response
22.96 s
Response starts
2.96 s
Output speed
25.0 tok/s
Route uptime
N/a
Context
202.8K
Route
phala
gpt-oss-120bWinner · Amazon Bedrock

openai/gpt-oss-120b

Amazon Bedrock
Blended price
$0.3 / 1M
500-token response
3.59 s
Response starts
1.55 s
Output speed
244.5 tok/s
Route uptime
99.94%
Context
131.1K
Route
amazon-bedrock
Phala
Blended price
$0.3 / 1M
500-token response
12.10 s
Response starts
0.74 s
Output speed
44.0 tok/s
Route uptime
99.76%
Context
131.1K
Route
phala
gpt-oss-20bWinner · Amazon Bedrock

openai/gpt-oss-20b

Amazon Bedrock
Blended price
$0.0967 / 1M
500-token response
2.78 s
Response starts
0.36 s
Output speed
207.0 tok/s
Route uptime
100.00%
Context
131.1K
Route
amazon-bedrock
Phala
Blended price
$0.0767 / 1M
500-token response
9.39 s
Response starts
0.62 s
Output speed
57.0 tok/s
Route uptime
99.78%
Context
131.1K
Route
phala

A per-model winner combines blended price and estimated 500-token response time with equal proportional weight. Ties and rows missing either measurement receive no badge. Comparison data calculated . Values use one deterministic route per provider and model; missing measurements remain visible as N/a.

Amazon Bedrock vs Phala analysis

How Amazon Bedrock and Phala compare for AI inference

Amazon Bedrock and Phala share 3 indexed text models, including GLM 5, gpt-oss-120b, gpt-oss-20b. Amazon Bedrock has the stronger typical response-time result on the directly measured set.

Same-model speed evidence

3 shared models currently have complete response-start and output-speed measurements on both providers. The speed comparison uses per-model ratios before taking the median, so naturally faster model catalogs do not improve the result.

Token pricing on one workload

3 shared models have complete input and output prices on both providers. Prices use the same 1,000-input/500-output-token mix and are normalized to one million tokens for readability.

Catalog and deployment differences

Amazon Bedrock has 23 indexed models and lists 2 published server regions; Phala has 18 models and lists no regions. Verify data residency, privacy terms, limits, and production latency directly before choosing.