Fireworks AI vs DeepInfra: model catalog compared

When you’re picking an inference provider, the Fireworks AI vs DeepInfra model catalog decision comes down to which trade-offs you can live with. Both platforms serve open-weight models through OpenAI-compatible APIs, but they differ sharply in optimization philosophy, supported modalities, and deployment ergonomics.

Capabilities and model coverage

Fireworks AI

Fireworks built its reputation on fast serving of popular open models. The catalog centers on Llama, Mixtral, Qwen, and Gemma families, plus proprietary fine-tunes like FireFunction for tool calling and FireLLaVA for vision. You get chat, completions, embeddings, and vision endpoints, but the selection is curated. They optimize a smaller set rather than hosting everything.

The platform pins model versions and exposes them under stable IDs like accounts/fireworks/models/llama-v3p1-70b-instruct. That stability matters when you bake a model string into a config. Fireworks also ships task-specific endpoints (e.g., a dedicated reranker) but the count is deliberately limited.

DeepInfra

DeepInfra takes a broader hosting approach. The model catalog includes hundreds of text generators, embedding models (BGE, E5), rerankers, speech-to-text (Whisper variants), and image generators (SDXL, Flux). It’s closer to a model zoo where community uploads appear quickly. If you need a niche checkpoint—say a 1B Japanese LLM or a specific diffusion variant—DeepInfra likely has it before Fireworks.

Model IDs follow HuggingFace conventions: meta-llama/Meta-Llama-3.1-70B-Instruct. DeepInfra tracks upstream commits, so you can pin a revision hash. This is flexible but noisier for production pinning.

The Fireworks AI vs DeepInfra model catalog split is therefore curation versus breadth. Fireworks is a tuned garage; DeepInfra is a public parking lot with many spots.

Price and cost model

Neither publishes a single flat rate; both use per-token metering for serverless inference. Fireworks positions itself as premium-priced for speed, with dedicated deployment options billed hourly per GPU. DeepInfra uses per-token for serverless and also offers bare GPU rental by the hour for self-hosted-style control.

Concrete difference: Fireworks charges separately for input and output tokens with discounts on cached prompts; DeepInfra does similar but often at lower per-token rates for equivalent model sizes. For a 70B model, expect DeepInfra to be cheaper per output token, at the cost of variable latency. Avoid guessing exact cents—check live pricing pages before committing.

Both support prompt caching to cut repeat-cost. Fireworks honors cache_control hints on supported models; DeepInfra leverages vLLM prefix caching automatically. If your traffic has high prefix overlap (system prompts, few-shot examples), both will save, but the implementation differs.

Latency and throughput

Fireworks invests in a custom inference engine (FireAttention) that reduces kernel launch overhead. In practice, small-to-mid requests on 7B–34B models feel interactive, often sub-200ms time-to-first-token on warm pools. DeepInfra relies on vLLM and similar OSS stacks; cold starts are more visible, and tail latency varies with model popularity.

Throughput follows the same pattern. Fireworks keeps stable tokens/sec under load for hosted models; DeepInfra can throttle less-popular models to fewer replicas, causing queueing. For high-QPS workloads, Fireworks dedicated instances remove noisy neighbors; DeepInfra’s GPU rental gives similar isolation but requires you to manage the serving container.

If you stream, both support SSE. Fireworks tends to deliver smoother token intervals; DeepInfra can burst after a slower TTFT on cold models.

Ergonomics and DX

API shape

Both expose OpenAI-compatible REST. You can swap base_url and key.

from openai import OpenAI

fw = OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key="FW_API_KEY",
)
di = OpenAI(
    base_url="https://api.deepinfra.com/v1/openai",
    api_key="DI_API_KEY",
)

fw.chat.completions.create(
    model="accounts/fireworks/models/llama-v3p1-70b-instruct",
    messages=[{"role": "user", "content": "ping"}],
)
di.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-70B-Instruct",
    messages=[{"role": "user", "content": "ping"}],
)

Client differences

Fireworks returns provider-specific fields like logprobs and supports custom sampling parameters (top_logprobs). DeepInfra passes through vLLM options such as guided_json for structured output. Both honor stream: true.

# DeepInfra structured output via vLLM guided_json
di.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    messages=[{"role":"user","content":"Return JSON"}],
    extra_body={"guided_json": {"type":"object","properties":{"name":{"type":"string"}}}},
)
# Fireworks equivalent with response_format
fw.chat.completions.create(
    model="accounts/fireworks/models/llama-v3p1-8b-instruct",
    messages=[{"role":"user","content":"Return JSON"}],
    response_format={"type":"json_object"},
)

Fireworks documents model IDs under accounts/fireworks/models/...; DeepInfra uses HuggingFace-style org/name. That mismatch matters when you abstract your caller.

Observability

Fireworks provides per-request trace IDs, token breakdowns, and a latency percentile dashboard. DeepInfra shows usage graphs and raw request logs but less granularity on tail latency. For debugging production incidents, Fireworks gives you more to work with out of the box.

Ecosystem and tooling

Fireworks ships a Python SDK with async support and a web playground tuned for function calling. DeepInfra provides a minimal SDK but leans on community integrations; its console shows usage graphs and one-click deployments.

If you want a single endpoint that spans both without writing fallback logic, a gateway like n4n.ai fronts 240+ models—including many from these two vendors—behind one OpenAI-compatible route with automatic provider fallback when rate limits hit. That’s useful when your app needs breadth but not two vendor relationships.

DeepInfra’s strength is the long tail: you can pull a model that has no equivalent on Fireworks and call it the same way. Fireworks’ strength is first-party tooling around its curated set—evals, fine-tune jobs, and deployment slots.

Limits and quotas

Fireworks enforces per-minute token and request caps that scale with tier; dedicated deployments lift these. DeepInfra serverless has default daily spend limits and per-model rate caps that can surprise you on rare checkpoints.

Context length follows the underlying model: both serve 128K contexts for Llama-3.1, but Fireworks may truncate earlier on free tier. DeepInfra allows larger batch sizes on rented GPUs. Max output tokens are model-bound; neither lets you exceed the checkpoint’s native limit.

File upload boundaries differ: Fireworks vision models accept base64 images inline; DeepInfra’s multimodal endpoints similarly take URLs or base64 but may restrict size per request.

Head-to-head comparison

Which to choose

For latency-sensitive production RAG

Pick Fireworks. The tuned serving stack and dedicated instances give predictable tail latency. If your SLA demands interactive first token on a 70B model, Fireworks is the safer bet.

For multimodal experimentation

DeepInfra wins on catalog breadth. Need to pipe Whisper transcripts into SDXL image gen and embed with BGE in one account? DeepInfra already hosts them. Fireworks would require stitching external services.

For cost-bound batch jobs

DeepInfra’s lower per-token rates and GPU rental suit offline summarization or eval sweeps. You can spin a 4xA100 box, run vLLM yourself, and shut down. Fireworks dedicated may cost more but saves ops time.

For function-calling agents

Fireworks’ FireFunction models are purpose-trained for tool use, yielding higher success rates than base Instruct models on DeepInfra. If agent reliability matters, start there.

For unified routing across vendors

If you don’t want to maintain two clients, use a gateway that aggregates both. The Fireworks AI vs DeepInfra model catalog question becomes moot when one endpoint serves either with fallback. Engineer for the abstraction, not the vendor.

For research on obscure checkpoints

DeepInfra’s model zoo is unmatched for variety. You can test a new 2B fine-tune the day it hits HuggingFace. Fireworks will lag until they decide to optimize it.

The practical move: prototype on DeepInfra to access weird models cheaply, then migrate hot paths to Fireworks when latency or reliability bites. Keep your call site behind an interface so the switch is a config change, not a rewrite.