Why I'm deciding against Featherless for my current usage:

Although Pi works well with Featherless' 4 concurrent request limit, the $25 entry plan on Featherless chokes context to 32K tokens. Even their $100/month plan caps context at 256K. I actually make use of 1 million token context, constantly. Context limits are listed on the pricing page:

https://featherless.ai/#pricing

Also, because Featherless multiplexes 30,000+ open-source models dynamically across shared serverless GPU clusters, they do not keep massive weights constantly active in memory for low-tier flat-rate accounts.

So for most users' needs, the plain old Deepseek API still currently beats Featherless for cost per capability, especially with Pi coding agent. Because Pi preserves context via sessions, you end up getting a huge percentage of cache hits with the DeepSeek V4 APIs, and no other model family beats Deepseek for cache hit cost. With an average cached input expense of ~$0.05 per million, processing 30 million tokens per week costs something like $1.50-$3.00. You're not tied to any monthly base cost, and you're not rate-limited (it just costs more for more use). If your usage sometimes dips lower, it's really hard to beat that pricing structure.

Clearly, if your open source LLM API expenses constantly exceed $200 per month, then Featherless could certainly end up being a great buy. With most of my work now being accomplished by Deepseek V4 Flash, I'd need to consistently average 1-1.5 trillion tokens per month, to make the switch worth while.

Why I'm deciding against Featherless for my current usage:

Although Pi works well with Featherless' 4 concurrent request limit, the $25 entry plan on Featherless chokes context to 32K tokens. Even their $100/month plan caps context at 256K. I actually make use of 1 million token context, constantly. Context limits are listed on the pricing page:

https://featherless.ai/#pricing

Also, because Featherless multiplexes 30,000+ open-source models dynamically across shared serverless GPU clusters, they do not keep massive weights constantly active in memory for low-tier flat-rate accounts.

So for my needs, Deepseek API is still the current winner for cost per capability, especially with Pi coding agent. Because Pi preserves context via sessions, you end up getting a huge percentage of cache hits with the DeepSeek V4 APIs, and no other model family beats Deepseek for cache hit cost. With an average cached input expense of ~$0.05 per million, processing 30 million tokens per week costs something like $1.50-$3.00. You're not tied to any monthly base cost, and you're not rate-limited (it just costs more for more use). If your usage sometimes dips lower, it's really hard to beat that pricing structure.

Clearly, if your open source LLM API expenses constantly exceed $200 per month, then Featherless could certainly end up being a great buy. With most of my work now being accomplished by Deepseek V4 Flash, I'd need to consistently average 1-1.5 trillion tokens per month, to make the switch.

Why I'm deciding against Featherless:

Although Pi works well with Featherless' 4 concurrent request limit, the $25 entry plan on Featherless chokes context down to 32K tokens. Even their $100/month plan caps at 256K. Also, because Featherless multiplexes 30,000+ open-source models dynamically across shared serverless GPU clusters, they do not keep massive weights constantly active in memory for low-tier flat-rate accounts.

So for my needs, Deepseek API is still the current winner for cost per capability, especially with Pi coding agent. Because Pi preserves context branches via session trees, you end up getting a huge percentage of cache hits with the DeepSeek V4 Pro API. With an average cached input cost of ~$0.05 per million, processing 30 million tokens per week costs something like $1.50-$3.00. You're not tied to a monthly base cost, and you're not rate-limited (it just costs more for more use). If your usage sometimes dips lower, it's really hard to beat that pricing structure.

Why I'm deciding against Featherless:

Although Pi works well with Featherless' 4 concurrent request limit, the $25 entry plan on Featherless chokes context down to 32K tokens. Even their $100/month plan caps at 256K. Also, because Featherless multiplexes 30,000+ open-source models dynamically across shared serverless GPU clusters, they do not keep massive weights constantly active in hot memory for low-tier flat-rate accounts.

So Deepseek API is still the winner for cost per capability, especially with Pi coding agent. Because Pi heavily preserves context branches via its session trees, you end up hitting near-constant cache hits on DeepSeek V4 Pro. At an average cached input rate of ~$0.05 per million tokens, processing 30 million tokens per week costs something like $1.50 to $3.00. And you're never tied in to a monthly base cost. It's really hard to beat that.