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DSPy vs Sambanova

Sambanova scores higher on the AgentReady, 48/100 against 47/100. They differ on 9 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

DSPy. DSPy is a Python framework for building AI systems.

Sambanova. SambaNova is an AI infrastructure company pushing the AI frontier with premium inference, maximizing dataflow efficiency with high speed and sustained throughput for running the largest models.

Where DSPy is ahead

DSPy passes pricing understandable, and Sambanova does not. That is understand, whether an agent can read the docs and work out how the API behaves before calling it.

It also holds adopt: self-service signup, free trial or free allowance, fast time to first request and cli available. Sambanova misses those.

Where Sambanova is ahead

Sambanova passes structured api reference and authentication documented, and DSPy does not. That is understand, whether an agent can read the docs and work out how the API behaves before calling it.

It also holds adopt: official typescript sdk. DSPy misses it.

And on operate, observable execution. DSPy misses it.

What neither does

Both fail public docs discoverable, llms-full.txt / full agent docs, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, no mandatory sales call, agent-compatible signup flow, programmatic credential creation, structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified. If your agent needs any of those, you will be building it yourself either way.

Score, pillar by pillar

The AgentReady splits into four pillars, scored separately, because a product can be easy to find and still impossible to adopt.

Discover. Both sit at 80/100 here. DSPy misses public docs discoverable, llms-full.txt / full agent docs; Sambanova misses public docs discoverable, llms-full.txt / full agent docs.

Understand. Sambanova leads 38 to 23. DSPy misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented; Sambanova misses openapi / spec quality, pricing understandable, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented.

Adopt is whether an agent can get a key and make its first successful call without a human in the loop. DSPy leads 60 to 40. DSPy misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, official typescript sdk; Sambanova misses self-service signup, no mandatory sales call, agent-compatible signup flow, programmatic credential creation, free trial or free allowance, fast time to first request, cli available.

Operate. Sambanova leads 35 to 24. DSPy misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, observable execution, agent compatibility verified; Sambanova misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified.

Pricing

DSPy does not publish a machine-readable starting price and has a free tier. Sambanova does not publish one.

Signal by signal

Which to pick

DSPy clears more of the signals an agent needs, so it is the safer default for unattended use. Full profiles: DSPy and Sambanova. Alternatives to each: DSPy, Sambanova.

An agent can fetch this as data: POST /v1/compare {"slugs": ["dspy", "sambanova"]}